MCP: serve tools from the API process over Streamable HTTP at /mcp

- netx_api/mcp_remote: ops / topology / bizMonitor tools, Bearer auth,
  per-tool RBAC filtering, ?groups= filter, readOnlyHint and group meta.
- Mount the official MCP Streamable HTTP app at /mcp (stateless);
  AuthAuditMiddleware is pure ASGI and skips /mcp.
- Let uvicorn chunk MCP responses; some Windows LSPs rewrite
  Content-Length replies on */mcp* paths into broken chunked framing.
- NETX_MCP_ALLOWED_HOSTS opts into DNS-rebinding checks (off by default
  so LAN and reverse-proxy hosts work; Bearer is the gate).
- Remove the three stdio Python MCP packages; tests moved to /mcp,
  including real-TCP framing regressions.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

netx_api/mcp_remote/topology/__init__.py

@@ -0,0 +1,5 @@
+"""netx topology MCP — canvas / fabric tools for drawing topology maps."""
+
+__version__ = "0.1.51"
+# Bump when public catalog/actions change so agents know to restart stdio.
+NETX_MCP_REV = "2026-08-12-from-scratch-polish"

netx_api/mcp_remote/topology/layout_job_worker.py

@@ -0,0 +1,92 @@
+"""Subprocess entry for durable layout jobs (survives MCP parent restart)."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+import time
+from pathlib import Path
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(prog="netx_api.mcp_remote.topology.layout_job_worker")
+    parser.add_argument("--job-id", required=True)
+    args = parser.parse_args(argv)
+    job_id = str(args.job_id).strip()
+    if not job_id:
+        return 2
+
+    from netx_api.mcp_remote.topology.layout_jobs import (
+        _JobCancelled,
+        _args_path,
+        _read_job_disk,
+        _write_job_disk,
+        bind_job,
+        finish_job,
+        is_cancelled,
+        report_progress,
+        touch_heartbeat,
+        unbind_job,
+    )
+
+    args_file = _args_path(job_id)
+    if not args_file.is_file():
+        finish_job(job_id, error="job_args_missing")
+        return 1
+
+    try:
+        tool_args = json.loads(args_file.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError) as exc:
+        finish_job(job_id, error=f"job_args_invalid:{exc}")
+        return 1
+
+    token = bind_job(job_id)
+    import threading
+
+    stop_beat = threading.Event()
+
+    def _beat() -> None:
+        while not stop_beat.wait(15.0):
+            touch_heartbeat()
+
+    threading.Thread(target=_beat, name=f"job-beat-{job_id}", daemon=True).start()
+    try:
+        job = _read_job_disk(job_id) or {"job_id": job_id, "status": "running"}
+        job["pid"] = __import__("os").getpid()
+        job["heartbeat_at"] = time.time()
+        job["progress"] = {
+            **(job.get("progress") or {}),
+            "phase": "running",
+            "message": "worker bound",
+            "updated_at": time.time(),
+            "pct": 1.0,
+        }
+        _write_job_disk(job)
+        report_progress("running", pct=1.0, message="worker started")
+        if is_cancelled():
+            raise _JobCancelled()
+
+        # Late import: pulls http_tools + layout stack.
+        from netx_api.mcp_remote.topology.http_tools import _layout_topology_view
+
+        result = _layout_topology_view(tool_args)
+        finish_job(job_id, result=result, cancelled=is_cancelled())
+        return 0 if result.get("ok") else 1
+    except _JobCancelled:
+        finish_job(job_id, cancelled=True)
+        return 0
+    except Exception as exc:  # noqa: BLE001
+        finish_job(job_id, error=f"job_exception:{exc}")
+        return 1
+    finally:
+        stop_beat.set()
+        unbind_job(token)
+        try:
+            args_file.unlink(missing_ok=True)
+        except OSError:
+            pass
+
+
+if __name__ == "__main__":
+    sys.exit(main())

netx_api/mcp_remote/topology/layout_jobs.py

@@ -0,0 +1,496 @@
+"""Durable background jobs for long layoutTopologyView calls.
+
+Cursor MCP tool calls often die around ~60s, and Cursor may also **respawn**
+the MCP process between tool calls — in-memory threads then vanish.
+
+Design:
+- Job records live as JSON under ``NETX_LAYOUT_JOB_DIR`` (default
+  ``<netx>/data/runtime/layout_jobs``).
+- Workers run in a **subprocess** so they survive MCP parent restarts.
+- Poll via ``job_status`` (same contract); cancel is cooperative via the
+  JSON ``cancel_requested`` flag.
+
+Remote: any sticky MCP worker that shares the job dir can poll; true
+multi-host still wants API-hosted jobs later.
+"""
+
+from __future__ import annotations
+
+import contextvars
+import json
+import os
+import subprocess
+import sys
+import threading
+import time
+import uuid
+from pathlib import Path
+from typing import Any, Callable
+
+
+_LOCK = threading.Lock()
+_JOBS: dict[str, dict[str, Any]] = {}  # hot cache; disk is source of truth
+_MAX_JOBS = 48
+_STALE_AFTER_S = 90.0
+_CURRENT_JOB_ID: contextvars.ContextVar[str | None] = contextvars.ContextVar(
+    "layout_job_id", default=None
+)
+
+
+def _job_dir() -> Path:
+    env = (os.getenv("NETX_LAYOUT_JOB_DIR") or "").strip()
+    if env:
+        p = Path(env)
+    else:
+        # netx_api/mcp_remote/topology → repo data/
+        here = Path(__file__).resolve()
+        repo = here.parents[4]  # .../netx
+        p = repo / "data" / "runtime" / "layout_jobs"
+    p.mkdir(parents=True, exist_ok=True)
+    return p
+
+
+def _job_path(job_id: str) -> Path:
+    return _job_dir() / f"{job_id}.json"
+
+
+def _args_path(job_id: str) -> Path:
+    return _job_dir() / f"{job_id}.args.json"
+
+
+def _atomic_write(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    tmp = path.with_suffix(path.suffix + ".tmp")
+    tmp.write_text(json.dumps(payload, ensure_ascii=False), encoding="utf-8")
+    tmp.replace(path)
+
+
+def _read_job_disk(job_id: str) -> dict[str, Any] | None:
+    path = _job_path(job_id)
+    if not path.is_file():
+        return None
+    try:
+        data = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError):
+        return None
+    return data if isinstance(data, dict) else None
+
+
+def _write_job_disk(job: dict[str, Any]) -> None:
+    jid = str(job.get("job_id") or "").strip()
+    if not jid:
+        return
+    _atomic_write(_job_path(jid), job)
+
+
+def _prune_disk() -> None:
+    files = sorted(_job_dir().glob("*.json"), key=lambda p: p.stat().st_mtime)
+    # ignore *.args.json
+    files = [p for p in files if not p.name.endswith(".args.json")]
+    if len(files) <= _MAX_JOBS:
+        return
+    # Active jobs must remain pollable and cancellable even when history fills up.
+    removable = [
+        path for path in files
+        if (_read_job_disk(path.stem) or {}).get("status") in {"done", "error", "cancelled"}
+    ]
+    for path in removable[: max(0, len(files) - _MAX_JOBS)]:
+        try:
+            path.unlink(missing_ok=True)
+            path.with_name(path.stem + ".args.json").unlink(missing_ok=True)
+        except OSError:
+            pass
+
+
+def _public_snapshot(job: dict[str, Any]) -> dict[str, Any]:
+    now = time.time()
+    started = float(job.get("started_at") or now)
+    finished = job.get("finished_at")
+    status = str(job.get("status") or "unknown")
+    heartbeat = float(job.get("heartbeat_at") or started)
+    elapsed_ms = job.get("elapsed_ms")
+    if elapsed_ms is None:
+        end = float(finished) if finished is not None else now
+        elapsed_ms = int(max(0.0, end - started) * 1000)
+    heartbeat_age_ms = int(max(0.0, now - heartbeat) * 1000)
+    stale = status in {"running", "cancelling"} and (now - heartbeat) >= _STALE_AFTER_S
+    progress = dict(job.get("progress") or {})
+    out = {
+        "job_id": job.get("job_id"),
+        "status": status,
+        "action": job.get("action"),
+        "view_id": job.get("view_id"),
+        "started_at": job.get("started_at"),
+        "finished_at": job.get("finished_at"),
+        "elapsed_ms": elapsed_ms,
+        "heartbeat_at": job.get("heartbeat_at"),
+        "heartbeat_age_ms": heartbeat_age_ms,
+        "stale": stale,
+        "cancel_requested": bool(job.get("cancel_requested")),
+        "progress": progress,
+        "meta": dict(job.get("meta") or {}),
+        "error": job.get("error"),
+        "pid": job.get("pid"),
+    }
+    if status in {"done", "error", "cancelled"}:
+        out["result"] = job.get("result")
+    return out
+
+
+def current_job_id() -> str | None:
+    return _CURRENT_JOB_ID.get()
+
+
+def report_progress(
+    phase: str,
+    *,
+    pct: float | None = None,
+    message: str = "",
+    step: int | None = None,
+    total_steps: int | None = None,
+    **extra: Any,
+) -> None:
+    jid = _CURRENT_JOB_ID.get()
+    if not jid:
+        return
+    now = time.time()
+    phase_s = str(phase or "").strip() or "running"
+    prog: dict[str, Any] = {
+        "phase": phase_s,
+        "message": str(message or "")[:240],
+        "updated_at": now,
+    }
+    if pct is not None:
+        try:
+            prog["pct"] = max(0.0, min(100.0, float(pct)))
+        except (TypeError, ValueError):
+            pass
+    if step is not None:
+        try:
+            prog["step"] = int(step)
+        except (TypeError, ValueError):
+            pass
+    if total_steps is not None:
+        try:
+            prog["total_steps"] = int(total_steps)
+        except (TypeError, ValueError):
+            pass
+    for k, v in extra.items():
+        if v is None:
+            continue
+        if isinstance(v, (str, int, float, bool)):
+            prog[k] = v
+        elif isinstance(v, (list, tuple)) and len(v) <= 12:
+            prog[k] = list(v)
+    with _LOCK:
+        job = _read_job_disk(jid) or _JOBS.get(jid)
+        if not job or job.get("status") not in {"running", "cancelling"}:
+            return
+        prev = dict(job.get("progress") or {})
+        prev.update(prog)
+        job["progress"] = prev
+        job["heartbeat_at"] = now
+        _JOBS[jid] = job
+        _write_job_disk(job)
+
+
+def touch_heartbeat() -> None:
+    jid = _CURRENT_JOB_ID.get()
+    if not jid:
+        return
+    with _LOCK:
+        job = _read_job_disk(jid) or _JOBS.get(jid)
+        if job and job.get("status") in {"running", "cancelling"}:
+            job["heartbeat_at"] = time.time()
+            _JOBS[jid] = job
+            _write_job_disk(job)
+
+
+def is_cancelled() -> bool:
+    jid = _CURRENT_JOB_ID.get()
+    if not jid:
+        return False
+    with _LOCK:
+        job = _read_job_disk(jid) or _JOBS.get(jid)
+        if not job:
+            return False
+        return bool(job.get("cancel_requested"))
+
+
+def cancel_job(job_id: str) -> dict[str, Any]:
+    jid = str(job_id or "").strip()
+    if not jid:
+        return {"ok": False, "error": "job_id_required"}
+    with _LOCK:
+        job = _read_job_disk(jid) or _JOBS.get(jid)
+        if not job:
+            return {"ok": False, "error": "job_not_found", "job_id": jid}
+        status = str(job.get("status") or "")
+        if status in {"done", "error", "cancelled"}:
+            return {
+                "ok": True,
+                "job_id": jid,
+                "status": status,
+                "cancel_requested": bool(job.get("cancel_requested")),
+                "hint": "Job already finished; cancel is a no-op.",
+            }
+        job["cancel_requested"] = True
+        job["status"] = "cancelling"
+        job["heartbeat_at"] = time.time()
+        job["progress"] = {
+            **(job.get("progress") or {}),
+            "phase": "cancelling",
+            "message": "cancel requested",
+            "updated_at": time.time(),
+        }
+        _JOBS[jid] = job
+        _write_job_disk(job)
+        return {
+            "ok": True,
+            "job_id": jid,
+            "status": "cancelling",
+            "cancel_requested": True,
+            "hint": "Cooperative cancel armed; worker exits at next checkpoint.",
+        }
+
+
+class _JobCancelled(Exception):
+    """Internal: cooperative cancel before/during runner."""
+
+
+def raise_if_cancelled() -> None:
+    if is_cancelled():
+        raise _JobCancelled()
+
+
+def bind_job(job_id: str) -> contextvars.Token:
+    """Worker entry: bind ContextVar for report_progress / is_cancelled."""
+    return _CURRENT_JOB_ID.set(str(job_id))
+
+
+def unbind_job(token: contextvars.Token) -> None:
+    _CURRENT_JOB_ID.reset(token)
+
+
+def finish_job(
+    job_id: str,
+    *,
+    result: dict[str, Any] | None = None,
+    error: str | None = None,
+    cancelled: bool = False,
+) -> None:
+    """Worker finalizer — write terminal status to disk."""
+    jid = str(job_id or "").strip()
+    with _LOCK:
+        job = _read_job_disk(jid) or _JOBS.get(jid) or {"job_id": jid}
+        started = float(job.get("started_at") or time.time())
+        now = time.time()
+        applied = bool(isinstance(result, dict) and result.get("applied"))
+        if cancelled and not applied:
+            job["status"] = "cancelled"
+            job["error"] = "cancelled"
+            job["result"] = result or {
+                "ok": False,
+                "error": "cancelled",
+                "job_id": jid,
+                "applied": False,
+            }
+        elif error and not (isinstance(result, dict) and result.get("ok")):
+            job["status"] = "error"
+            job["error"] = str(error)[:500]
+            job["result"] = result or {
+                "ok": False,
+                "error": job["error"],
+                "job_id": jid,
+            }
+        else:
+            job["status"] = "done" if (result or {}).get("ok") else "error"
+            job["result"] = result
+            if cancelled and applied and isinstance(job["result"], dict):
+                job["result"] = {
+                    **job["result"],
+                    "cancel_requested": True,
+                    "hint": (
+                        str(job["result"].get("hint") or "")
+                        + " (cancel requested after apply; write kept)"
+                    ).strip(),
+                }
+            if not (result or {}).get("ok"):
+                job["error"] = (result or {}).get("error") or error or "job_failed"
+        job["finished_at"] = now
+        job["elapsed_ms"] = int((now - started) * 1000)
+        job["heartbeat_at"] = now
+        job["progress"] = {
+            **(job.get("progress") or {}),
+            "phase": job["status"],
+            "pct": 100.0 if job["status"] == "done" else (job.get("progress") or {}).get("pct"),
+            "message": str(job.get("error") or job["status"]),
+            "updated_at": now,
+        }
+        _JOBS[jid] = job
+        _write_job_disk(job)
+
+
+def start_job(
+    *,
+    action: str,
+    view_id: str,
+    runner: Callable[[], dict[str, Any]] | None = None,
+    meta: dict[str, Any] | None = None,
+    tool_args: dict[str, Any] | None = None,
+) -> str:
+    """Start a durable subprocess job.
+
+    ``tool_args`` should be the full layoutTopologyView args with
+    ``params._force_sync=True`` so the worker runs the heavy path inline.
+    ``runner`` is only used in unit tests (in-process fallback).
+    """
+    job_id = uuid.uuid4().hex[:16]
+    now = time.time()
+    job = {
+        "job_id": job_id,
+        "status": "running",
+        "action": action,
+        "view_id": view_id,
+        "started_at": now,
+        "finished_at": None,
+        "elapsed_ms": None,
+        "heartbeat_at": now,
+        "cancel_requested": False,
+        "progress": {
+            "phase": "queued",
+            "pct": 0.0,
+            "message": "starting worker",
+            "updated_at": now,
+        },
+        "result": None,
+        "error": None,
+        "meta": dict(meta or {}),
+        "pid": None,
+    }
+    with _LOCK:
+        _prune_disk()
+        _JOBS[job_id] = job
+        _write_job_disk(job)
+
+    # Unit-test / sync path: in-process thread when no tool_args.
+    if tool_args is None and runner is not None:
+        return _start_thread_job(job_id, action, view_id, runner)
+
+    if tool_args is None:
+        raise ValueError("tool_args_or_runner_required")
+
+    args_payload = dict(tool_args)
+    params = dict(args_payload.get("params") or {})
+    params["_force_sync"] = True
+    params.pop("background", None)
+    args_payload["params"] = params
+    args_payload["mode"] = "apply"
+    args_payload["action"] = action
+    args_payload["view_id"] = view_id
+    _atomic_write(_args_path(job_id), args_payload)
+
+    env = os.environ.copy()
+    env.setdefault("PYTHONUTF8", "1")
+    env.setdefault("PYTHONIOENCODING", "utf-8")
+    # Ensure worker can import the same package tree.
+    src = str(Path(__file__).resolve().parents[1])
+    prev = env.get("PYTHONPATH", "")
+    env["PYTHONPATH"] = src + (os.pathsep + prev if prev else "")
+
+    creationflags = 0
+    if sys.platform == "win32":
+        # Detach from MCP console; survive parent exit.
+        creationflags = getattr(subprocess, "CREATE_NEW_PROCESS_GROUP", 0) | getattr(
+            subprocess, "DETACHED_PROCESS", 0x00000008
+        )
+
+    try:
+        proc = subprocess.Popen(  # noqa: S603 — controlled argv
+            [sys.executable, "-m", "netx_api.mcp_remote.topology.layout_job_worker", "--job-id", job_id],
+            env=env,
+            stdin=subprocess.DEVNULL,
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.DEVNULL,
+            creationflags=creationflags,
+            close_fds=True,
+        )
+    except OSError as exc:
+        finish_job(job_id, error=f"worker_start_failed:{exc}")
+        _args_path(job_id).unlink(missing_ok=True)
+        # The caller receives a pollable terminal job instead of an orphaned running record.
+        return job_id
+    with _LOCK:
+        job = _read_job_disk(job_id) or job
+        job["pid"] = proc.pid
+        # A short computation can finish before Popen returns to the parent.
+        # Keep its terminal progress/result intact.
+        if job.get("status") in {"running", "cancelling"}:
+            job["progress"] = {
+                **(job.get("progress") or {}),
+                "message": f"worker pid={proc.pid}",
+                "updated_at": time.time(),
+            }
+            job["heartbeat_at"] = time.time()
+        _JOBS[job_id] = job
+        _write_job_disk(job)
+    return job_id
+
+
+def _start_thread_job(
+    job_id: str,
+    action: str,
+    view_id: str,
+    runner: Callable[[], dict[str, Any]],
+) -> str:
+    stop_beat = threading.Event()
+
+    def _heartbeat_ticker() -> None:
+        while not stop_beat.wait(15.0):
+            touch_heartbeat()
+            with _LOCK:
+                job = _read_job_disk(job_id)
+                if not job or job.get("status") not in {"running", "cancelling"}:
+                    return
+
+    def _run() -> None:
+        token = bind_job(job_id)
+        try:
+            report_progress("running", pct=1.0, message="runner started")
+            raise_if_cancelled()
+            result = runner()
+            finish_job(
+                job_id,
+                result=result,
+                cancelled=is_cancelled(),
+            )
+        except _JobCancelled:
+            finish_job(job_id, cancelled=True)
+        except Exception as exc:  # noqa: BLE001
+            finish_job(job_id, error=f"job_exception:{exc}")
+        finally:
+            stop_beat.set()
+            unbind_job(token)
+
+    threading.Thread(
+        target=_heartbeat_ticker, name=f"layout-job-beat-{job_id}", daemon=True
+    ).start()
+    threading.Thread(target=_run, name=f"layout-job-{job_id}", daemon=True).start()
+    return job_id
+
+
+def get_job(job_id: str) -> dict[str, Any] | None:
+    with _LOCK:
+        job = _read_job_disk(job_id)
+        if job:
+            _JOBS[str(job_id)] = job
+        return dict(job) if job else None
+
+
+def job_public(job_id: str) -> dict[str, Any] | None:
+    with _LOCK:
+        job = _read_job_disk(job_id)
+        if not job:
+            return None
+        _JOBS[str(job_id)] = job
+        return _public_snapshot(job)

netx_api/mcp_remote/topology/layout_metrics.py

@@ -0,0 +1,824 @@
+"""Canvas layout metrics: edge crossings, spacing, label/icon overlap.
+
+Coordinates are API icon-center (x, y), matching TopologyPage neApiPosition.
+Visual constants mirror web/src (TOPO_ICON=25, caption under glyph).
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+# --- canvas visual budget (zoom=1, full LOD) ---
+ICON_SIZE = 25.0
+CAPTION_GAP = 1.0
+CAPTION_NAME_H = 10.0  # ~8px font * 1.15 line-height
+CAPTION_META_H = 8.0
+CHAR_W = 4.8  # empirical for 8px UI font, Latin/digit NE names
+# Comfortable center-to-center (infinite canvas — prefer airy over dense)
+MIN_CENTER_DX = 160.0
+MIN_CENTER_DY = 100.0
+REC_CENTER_DX = 200.0
+REC_CENTER_DY = 170.0
+MIN_CENTER_DIST = 150.0  # euclidean floor between icon centers
+# Node-center to non-incident edge segment (icon + pad); below → edge_clearance hit.
+EDGE_CLEARANCE_THR = 40.0
+EDGE_CLEARANCE_ENDPOINT_T = 0.05  # t near 0/1 ⇒ nn-like, skip (avoid double-count)
+EDGE_CLEARANCE_SKIP_NE = 200_000  # n*e above this: skip full scan
+# Edge axis: H/V metro look; within tol of axis counts as orthogonal.
+EDGE_AXIS_TOL_DEG = 8.0
+EDGE_AXIS_TOL_PX = 4.0
+EDGE_AXIS_CREDIT_H = 1.0
+EDGE_AXIS_CREDIT_V = 0.75  # horizontal preferred over vertical
+
+
+def estimate_label_width(name: str) -> float:
+    n = max(1, len((name or "").strip()))
+    return max(40.0, n * CHAR_W)
+
+
+def node_footprint(
+    name: str, *, with_meta: bool = False
+) -> tuple[float, float, float, float]:
+    """AABB relative to icon center: (min_x, min_y, max_x, max_y)."""
+    half_icon = ICON_SIZE / 2.0
+    lw = estimate_label_width(name) / 2.0
+    half_w = max(half_icon, lw)
+    top = -half_icon
+    bottom = half_icon + CAPTION_GAP + CAPTION_NAME_H
+    if with_meta:
+        bottom += CAPTION_META_H
+    return (-half_w, top, half_w, bottom)
+
+
+def _orient(p: tuple[float, float], q: tuple[float, float], r: tuple[float, float]) -> float:
+    return (q[0] - p[0]) * (r[1] - p[1]) - (q[1] - p[1]) * (r[0] - p[0])
+
+
+def segments_properly_intersect(
+    a: tuple[float, float],
+    b: tuple[float, float],
+    c: tuple[float, float],
+    d: tuple[float, float],
+) -> bool:
+    """True iff open segments ab and cd cross (shared endpoint ⇒ False)."""
+    if a == c or a == d or b == c or b == d:
+        return False
+    o1, o2 = _orient(a, b, c), _orient(a, b, d)
+    o3, o4 = _orient(c, d, a), _orient(c, d, b)
+    return o1 * o2 < 0 and o3 * o4 < 0
+
+
+def point_segment_dist(
+    p: tuple[float, float], a: tuple[float, float], b: tuple[float, float]
+) -> tuple[float, float]:
+    """Distance from point ``p`` to segment ``ab`` and clamped projection ``t``∈[0,1]."""
+    ax, ay = a
+    bx, by = b
+    px, py = p
+    vx, vy = bx - ax, by - ay
+    wx, wy = px - ax, py - ay
+    L2 = vx * vx + vy * vy
+    if L2 < 1e-12:
+        return math.hypot(wx, wy), 0.0
+    t = max(0.0, min(1.0, (wx * vx + wy * vy) / L2))
+    return math.hypot(px - (ax + t * vx), py - (ay + t * vy)), t
+
+
+def compute_edge_clearance(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    *,
+    names: dict[str, str] | None = None,
+    thr: float = EDGE_CLEARANCE_THR,
+    endpoint_t: float = EDGE_CLEARANCE_ENDPOINT_T,
+    top_n: int = 5,
+) -> dict[str, Any]:
+    """Score nodes sitting too close to non-incident edge segments.
+
+    A hit = node N within ``thr`` of segment AB where N∉{A,B} and the
+    projection is not near an endpoint (endpoint nearness ≈ neighbour spacing).
+    """
+    ids = [n for n in pos if n in pos]
+    n_nodes = len(ids)
+    n_links = len(links)
+    tip_ok = (
+        "非关联边不得擦过网元图标；d≥thr（默认 40）。"
+        "端点近距豁免（与 nn 分工）。阶段2：clear_edge_hits。"
+    )
+    if n_nodes == 0 or n_links == 0:
+        return {
+            "edge_clearance_hits": 0,
+            "nodes_hit": 0,
+            "min_clearance_p50": None,
+            "edge_clearance_score": 1.0,
+            "top_edge_hits": [],
+            "edge_clearance_tip": tip_ok,
+            "edge_clearance_skipped": False,
+        }
+    if n_nodes * n_links > EDGE_CLEARANCE_SKIP_NE:
+        return {
+            "edge_clearance_hits": None,
+            "nodes_hit": None,
+            "min_clearance_p50": None,
+            "edge_clearance_score": 1.0,
+            "top_edge_hits": [],
+            "edge_clearance_tip": (
+                f"skipped: n*e={n_nodes * n_links}>{EDGE_CLEARANCE_SKIP_NE}"
+            ),
+            "edge_clearance_skipped": True,
+        }
+
+    name_map = names or {}
+    hits: list[dict[str, Any]] = []
+    clearances: list[float] = []
+    nodes_with_hit: set[str] = set()
+    thr_f = float(thr)
+    et = float(endpoint_t)
+
+    for nid in ids:
+        p = pos[nid]
+        best_d = float("inf")
+        for a, b in links:
+            if nid in (a, b) or a not in pos or b not in pos:
+                continue
+            d, t = point_segment_dist(p, pos[a], pos[b])
+            if t <= et or t >= 1.0 - et:
+                continue
+            if d < best_d:
+                best_d = d
+            if d < thr_f:
+                hits.append(
+                    {
+                        "fabric_node_id": nid,
+                        "name": name_map.get(nid, nid),
+                        "a_node_id": a,
+                        "b_node_id": b,
+                        "a_name": name_map.get(a, a),
+                        "b_name": name_map.get(b, b),
+                        "dist": round(d, 2),
+                        "t": round(t, 4),
+                    }
+                )
+                nodes_with_hit.add(nid)
+        if math.isfinite(best_d):
+            clearances.append(best_d)
+
+    hits.sort(key=lambda h: float(h["dist"]))
+    # Worst edge per node (sorted by dist)
+    per_node: list[dict[str, Any]] = []
+    seen_n: set[str] = set()
+    for h in hits:
+        nid = str(h["fabric_node_id"])
+        if nid in seen_n:
+            continue
+        seen_n.add(nid)
+        per_node.append(h)
+
+    hits_n = len(hits)
+    # Dual signal so hub chords with many segment hits actually hurt:
+    # 1) unique nodes hit / n  (0 → 1, ≥20% → 0)
+    # 2) hits / links         (≤0.02 → 1, ≥0.20 → 0) — separates sparse vs scrubbed eyes
+    hit_frac = len(nodes_with_hit) / max(n_nodes, 1)
+    hit_per_link = hits_n / max(n_links, 1)
+    if hit_frac <= 0.0:
+        node_s = 1.0
+    elif hit_frac >= 0.2:
+        node_s = 0.0
+    else:
+        node_s = 1.0 - hit_frac / 0.2
+    if hit_per_link <= 0.02:
+        inten_s = 1.0
+    elif hit_per_link >= 0.20:
+        inten_s = 0.0
+    else:
+        inten_s = 1.0 - (hit_per_link - 0.02) / 0.18
+    score = 0.45 * node_s + 0.55 * inten_s
+
+    def _pct(vals: list[float], p: float) -> float | None:
+        if not vals:
+            return None
+        vs = sorted(vals)
+        idx = min(len(vs) - 1, max(0, int(round((len(vs) - 1) * p))))
+        return round(vs[idx], 1)
+
+    return {
+        "edge_clearance_hits": hits_n,
+        "nodes_hit": len(nodes_with_hit),
+        "min_clearance_p50": _pct(clearances, 0.5),
+        "edge_clearance_score": round(score, 4),
+        "edge_clearance_node_score": round(node_s, 4),
+        "edge_clearance_intensity_score": round(inten_s, 4),
+        "hit_per_link": round(hit_per_link, 4),
+        "top_edge_hits": per_node[:top_n],
+        "hit_nodes": per_node,
+        "edge_clearance_tip": tip_ok,
+        "edge_clearance_skipped": False,
+        "edge_clearance_thr": thr_f,
+    }
+
+
+def compute_edge_axis(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    *,
+    names: dict[str, str] | None = None,
+    tol_deg: float = EDGE_AXIS_TOL_DEG,
+    tol_px: float = EDGE_AXIS_TOL_PX,
+    top_n: int = 5,
+) -> dict[str, Any]:
+    """Score edges for axis-alignment; prefer horizontal over vertical.
+
+    Classification (first match):
+    - horizontal: |dy|≤tol_px or angle-to-H ≤ tol_deg
+    - vertical:   |dx|≤tol_px or angle-to-V ≤ tol_deg
+    - diagonal:   else
+
+    Per-edge credit: H=1.0, V=0.75, diagonal=0. Score = mean credit.
+    """
+    name_map = names or {}
+    tip = (
+        "边宜水平/垂直（地铁风）；水平优先于垂直。"
+        f"容差≈{tol_deg:g}°或{tol_px:g}px。斜边拉低总分。"
+    )
+    if not links:
+        return {
+            "edge_axis_score": 1.0,
+            "axis_frac": 1.0,
+            "horiz_frac": 1.0,
+            "vert_frac": 0.0,
+            "diag_frac": 0.0,
+            "horiz_n": 0,
+            "vert_n": 0,
+            "diag_n": 0,
+            "top_skew_edges": [],
+            "edge_axis_tip": tip,
+            "edge_axis_tol_deg": float(tol_deg),
+        }
+
+    tol_rad = math.radians(max(0.1, float(tol_deg)))
+    tol_p = max(0.0, float(tol_px))
+    h_n = v_n = d_n = 0
+    credits: list[float] = []
+    skew: list[dict[str, Any]] = []
+
+    for a, b in links:
+        if a not in pos or b not in pos:
+            continue
+        ax, ay = pos[a]
+        bx, by = pos[b]
+        dx, dy = bx - ax, by - ay
+        length = math.hypot(dx, dy)
+        if length < 1e-9:
+            # Degenerate: count as aligned (no geometry to fix)
+            h_n += 1
+            credits.append(EDGE_AXIS_CREDIT_H)
+            continue
+        adx, ady = abs(dx), abs(dy)
+        # Angle from nearest axis in [0, 45°]
+        ang_h = math.atan2(ady, adx)  # 0=H, π/2=V
+        ang_v = abs(math.pi / 2 - ang_h)
+        near_h = ady <= tol_p or ang_h <= tol_rad
+        near_v = adx <= tol_p or ang_v <= tol_rad
+        if near_h and (not near_v or ang_h <= ang_v):
+            kind = "H"
+            h_n += 1
+            credits.append(EDGE_AXIS_CREDIT_H)
+            skew_deg = math.degrees(ang_h)
+        elif near_v:
+            kind = "V"
+            v_n += 1
+            credits.append(EDGE_AXIS_CREDIT_V)
+            skew_deg = math.degrees(ang_v)
+        else:
+            kind = "D"
+            d_n += 1
+            credits.append(0.0)
+            skew_deg = math.degrees(min(ang_h, ang_v))
+            skew.append(
+                {
+                    "a_node_id": a,
+                    "b_node_id": b,
+                    "a_name": name_map.get(a, a),
+                    "b_name": name_map.get(b, b),
+                    "label": f"{name_map.get(a, a)}–{name_map.get(b, b)}",
+                    "angle_from_axis_deg": round(skew_deg, 1),
+                    "len": round(length, 1),
+                    "kind": kind,
+                }
+            )
+
+    n = max(len(credits), 1)
+    score = sum(credits) / n
+    axis_n = h_n + v_n
+    total_n = h_n + v_n + d_n
+    skew.sort(key=lambda r: -float(r["angle_from_axis_deg"]))
+    return {
+        "edge_axis_score": round(score, 4),
+        "axis_frac": round(axis_n / max(total_n, 1), 4),
+        "horiz_frac": round(h_n / max(total_n, 1), 4),
+        "vert_frac": round(v_n / max(total_n, 1), 4),
+        "diag_frac": round(d_n / max(total_n, 1), 4),
+        "horiz_n": h_n,
+        "vert_n": v_n,
+        "diag_n": d_n,
+        "top_skew_edges": skew[:top_n],
+        "edge_axis_tip": tip,
+        "edge_axis_tol_deg": float(tol_deg),
+        "edge_axis_tol_px": float(tol_px),
+    }
+
+
+def collapse_links(edges: list[dict[str, Any]]) -> list[tuple[str, str]]:
+    seen: set[tuple[str, str]] = set()
+    out: list[tuple[str, str]] = []
+    for e in edges:
+        if not isinstance(e, dict):
+            continue
+        a = str(e.get("a_node_id") or e.get("a") or "").strip()
+        b = str(e.get("b_node_id") or e.get("b") or "").strip()
+        if not a or not b or a == b:
+            continue
+        key = (a, b) if a < b else (b, a)
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append(key)
+    return out
+
+
+def _seg_bbox(
+    p: tuple[float, float], q: tuple[float, float]
+) -> tuple[float, float, float, float]:
+    return (
+        min(p[0], q[0]),
+        min(p[1], q[1]),
+        max(p[0], q[0]),
+        max(p[1], q[1]),
+    )
+
+
+def _bbox_overlap(
+    a: tuple[float, float, float, float], b: tuple[float, float, float, float]
+) -> bool:
+    return a[0] <= b[2] and a[2] >= b[0] and a[1] <= b[3] and a[3] >= b[1]
+
+
+def count_edge_crossings(
+    pos: dict[str, tuple[float, float]], links: list[tuple[str, str]]
+) -> int:
+    """Full O(E²) crossing count with bbox prune (skips obvious non-hits)."""
+    segs: list[
+        tuple[tuple[float, float], tuple[float, float], tuple[float, float, float, float]]
+    ] = []
+    for u, v in links:
+        if u in pos and v in pos:
+            p, q = pos[u], pos[v]
+            segs.append((p, q, _seg_bbox(p, q)))
+    n = 0
+    for i in range(len(segs)):
+        p1, p2, b1 = segs[i]
+        for j in range(i + 1, len(segs)):
+            p3, p4, b2 = segs[j]
+            if not _bbox_overlap(b1, b2):
+                continue
+            if segments_properly_intersect(p1, p2, p3, p4):
+                n += 1
+    return n
+
+
+def crossings_involving_node(
+    node: str,
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    adj: dict[str, set[str]],
+) -> int:
+    """Crossings on edges incident to ``node`` vs the rest of the graph.
+
+    Used for O(deg·E) trial scoring when one node moves.
+    """
+    nbs = adj.get(node, ())
+    if not nbs or node not in pos:
+        return 0
+    incident = [(node, v) if node < v else (v, node) for v in nbs if v in pos]
+    if not incident:
+        return 0
+    inc_set = set(incident)
+    n = 0
+    p0 = pos[node]
+    for u, v in incident:
+        other = v if u == node else u
+        p1 = pos[other]
+        bb = _seg_bbox(p0, p1)
+        for a, b in links:
+            if (a, b) in inc_set or node in (a, b) or other in (a, b):
+                continue
+            if a not in pos or b not in pos:
+                continue
+            pa, pb = pos[a], pos[b]
+            if not _bbox_overlap(bb, _seg_bbox(pa, pb)):
+                continue
+            if segments_properly_intersect(p0, p1, pa, pb):
+                n += 1
+    return n
+
+
+def crossings_after_node_move(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    adj: dict[str, set[str]],
+    node: str,
+    new_xy: tuple[float, float],
+    *,
+    current_total: int,
+    local_before: int | None = None,
+) -> int:
+    """Global crossing count after moving ``node`` — incremental, exact."""
+    if node not in pos:
+        return int(current_total)
+    before = (
+        int(local_before)
+        if local_before is not None
+        else crossings_involving_node(node, pos, links, adj)
+    )
+    trial = dict(pos)
+    trial[node] = new_xy
+    after = crossings_involving_node(node, trial, links, adj)
+    return int(current_total) - before + after
+
+
+def crossing_participation(
+    pos: dict[str, tuple[float, float]], links: list[tuple[str, str]]
+) -> tuple[int, dict[str, int]]:
+    """Count crossings + per-node hit counts (endpoint of a crossing edge)."""
+    n, node_hit, _edge_hit = crossing_participation_full(pos, links)
+    return n, node_hit
+
+
+def crossing_participation_full(
+    pos: dict[str, tuple[float, float]], links: list[tuple[str, str]]
+) -> tuple[int, dict[str, int], dict[tuple[str, str], int]]:
+    """Count crossings + per-node and per-edge hit counts.
+
+    Edge keys are undirected ``(min_id, max_id)``. An edge's hit count is how
+    many proper crossings that segment participates in.
+    """
+    node_hit: dict[str, int] = {}
+    edge_hit: dict[tuple[str, str], int] = {}
+    n = 0
+    for i, (a, b) in enumerate(links):
+        if a not in pos or b not in pos:
+            continue
+        p1, p2 = pos[a], pos[b]
+        bb1 = _seg_bbox(p1, p2)
+        e1 = (a, b) if a < b else (b, a)
+        for c, d in links[i + 1 :]:
+            if len({a, b, c, d}) < 4 or c not in pos or d not in pos:
+                continue
+            p3, p4 = pos[c], pos[d]
+            if not _bbox_overlap(bb1, _seg_bbox(p3, p4)):
+                continue
+            if segments_properly_intersect(p1, p2, p3, p4):
+                n += 1
+                e2 = (c, d) if c < d else (d, c)
+                for nid in (a, b, c, d):
+                    node_hit[nid] = node_hit.get(nid, 0) + 1
+                edge_hit[e1] = edge_hit.get(e1, 0) + 1
+                edge_hit[e2] = edge_hit.get(e2, 0) + 1
+    return n, node_hit, edge_hit
+
+
+def top_crossing_nodes(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    *,
+    names: dict[str, str] | None = None,
+    adj: dict[str, set[str]] | None = None,
+    top_n: int = 5,
+    participation: dict[str, int] | None = None,
+) -> list[dict[str, Any]]:
+    """Top-N nodes by crossing participation — primary surgical untangle targets."""
+    hit = participation
+    if hit is None:
+        _cross, hit = crossing_participation(pos, links)
+    if not hit:
+        return []
+    names = names or {}
+    ranked = sorted(
+        hit.items(),
+        key=lambda kv: (-kv[1], names.get(kv[0], kv[0]), kv[0]),
+    )
+    out: list[dict[str, Any]] = []
+    for nid, hits in ranked[: max(0, int(top_n))]:
+        if adj is not None:
+            deg = len(adj.get(nid, ()))
+        else:
+            deg = sum(1 for u, v in links if nid in (u, v) and u in pos and v in pos)
+        xy = pos.get(nid, (0.0, 0.0))
+        out.append(
+            {
+                "fabric_node_id": nid,
+                "name": names.get(nid, nid),
+                "crossing_hits": int(hits),
+                "degree": int(deg),
+                "x": round(float(xy[0]), 1),
+                "y": round(float(xy[1]), 1),
+            }
+        )
+    return out
+
+
+def top_crossing_edges(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    *,
+    names: dict[str, str] | None = None,
+    top_n: int = 5,
+    edge_participation: dict[tuple[str, str], int] | None = None,
+) -> list[dict[str, Any]]:
+    """Top-N undirected edges by number of crossings they participate in."""
+    edge_hit = edge_participation
+    if edge_hit is None:
+        _n, _nodes, edge_hit = crossing_participation_full(pos, links)
+    if not edge_hit:
+        return []
+    names = names or {}
+    ranked = sorted(
+        edge_hit.items(),
+        key=lambda kv: (
+            -kv[1],
+            names.get(kv[0][0], kv[0][0]),
+            names.get(kv[0][1], kv[0][1]),
+            kv[0],
+        ),
+    )
+    out: list[dict[str, Any]] = []
+    for (a, b), hits in ranked[: max(0, int(top_n))]:
+        ax, ay = pos.get(a, (0.0, 0.0))
+        bx, by = pos.get(b, (0.0, 0.0))
+        a_name = names.get(a, a)
+        b_name = names.get(b, b)
+        out.append(
+            {
+                "a_node_id": a,
+                "b_node_id": b,
+                "a_name": a_name,
+                "b_name": b_name,
+                "label": f"{a_name}<->{b_name}",
+                "crossing_hits": int(hits),
+                "ax": round(float(ax), 1),
+                "ay": round(float(ay), 1),
+                "bx": round(float(bx), 1),
+                "by": round(float(by), 1),
+                "mid_x": round((float(ax) + float(bx)) / 2.0, 1),
+                "mid_y": round((float(ay) + float(by)) / 2.0, 1),
+            }
+        )
+    return out
+
+
+def _aabb_overlap(
+    ax0: float, ay0: float, ax1: float, ay1: float, bx0: float, by0: float, bx1: float, by1: float
+) -> bool:
+    return ax0 < bx1 and ax1 > bx0 and ay0 < by1 and ay1 > by0
+
+
+def analyze_positions(
+    nodes: list[dict[str, Any]],
+    edges: list[dict[str, Any]],
+    *,
+    with_meta: bool = False,
+) -> dict[str, Any]:
+    """Compute crossings + spacing/overlap stats for a view graph."""
+    pos: dict[str, tuple[float, float]] = {}
+    names: dict[str, str] = {}
+    for n in nodes:
+        if not isinstance(n, dict):
+            continue
+        fid = str(n.get("fabric_node_id") or n.get("id") or "").strip()
+        if not fid:
+            continue
+        try:
+            x = float(n.get("x") if n.get("x") is not None else 0.0)
+            y = float(n.get("y") if n.get("y") is not None else 0.0)
+        except (TypeError, ValueError):
+            x, y = 0.0, 0.0
+        pos[fid] = (x, y)
+        names[fid] = str(n.get("name") or n.get("label") or fid)
+
+    links = collapse_links(edges)
+    crossings, hit, edge_hit = crossing_participation_full(pos, links)
+    n_nodes = len(pos)
+    n_links = len(links)
+    adj: dict[str, set[str]] = {nid: set() for nid in pos}
+    for u, v in links:
+        if u in adj and v in adj:
+            adj[u].add(v)
+            adj[v].add(u)
+    top_x = top_crossing_nodes(
+        pos, links, names=names, adj=adj, top_n=5, participation=hit
+    )
+    top_e = top_crossing_edges(
+        pos, links, names=names, top_n=5, edge_participation=edge_hit
+    )
+
+    # nearest-neighbor distances
+    ids = list(pos.keys())
+    nn: list[float] = []
+    close_pairs = 0
+    for i, a in enumerate(ids):
+        ax, ay = pos[a]
+        best = None
+        for b in ids:
+            if a == b:
+                continue
+            bx, by = pos[b]
+            d = math.hypot(ax - bx, ay - by)
+            if best is None or d < best:
+                best = d
+            if d < MIN_CENTER_DIST:
+                close_pairs += 1
+        if best is not None:
+            nn.append(best)
+    # close_pairs counted twice
+    close_pairs //= 2
+
+    overlap_pairs = 0
+    label_overlap_pairs = 0
+    for i, a in enumerate(ids):
+        ax, ay = pos[a]
+        fa = node_footprint(names[a], with_meta=with_meta)
+        for b in ids[i + 1 :]:
+            bx, by = pos[b]
+            fb = node_footprint(names[b], with_meta=with_meta)
+            if _aabb_overlap(
+                ax + fa[0],
+                ay + fa[1],
+                ax + fa[2],
+                ay + fa[3],
+                bx + fb[0],
+                by + fb[1],
+                bx + fb[2],
+                by + fb[3],
+            ):
+                overlap_pairs += 1
+                # stricter: only caption bands (below icon)
+                a_cap = (ax + fa[0], ay + ICON_SIZE / 2 + CAPTION_GAP, ax + fa[2], ay + fa[3])
+                b_cap = (bx + fb[0], by + ICON_SIZE / 2 + CAPTION_GAP, bx + fb[2], by + fb[3])
+                if _aabb_overlap(*a_cap, *b_cap):
+                    label_overlap_pairs += 1
+
+    xs = [p[0] for p in pos.values()] or [0.0]
+    ys = [p[1] for p in pos.values()] or [0.0]
+    nn_sorted = sorted(nn)
+    def pct(p: float) -> float | None:
+        if not nn_sorted:
+            return None
+        idx = min(len(nn_sorted) - 1, max(0, int(round((len(nn_sorted) - 1) * p))))
+        return round(nn_sorted[idx], 1)
+
+    crossings_per_link = round(crossings / n_links, 4) if n_links else 0.0
+    crossings_per_node = round(crossings / n_nodes, 4) if n_nodes else 0.0
+    bw = max(xs) - min(xs)
+    bh = max(ys) - min(ys)
+    area = max(bw * bh, 1.0)
+    # Ideal tile per NE ≈ recommended center pitch; util ∈ (0, ~1+].
+    space_utilization = round(n_nodes * REC_CENTER_DX * REC_CENTER_DY / area, 4)
+
+    return {
+        "node_count": n_nodes,
+        "link_count": n_links,
+        "edge_crossings": crossings,
+        "crossings_per_link": crossings_per_link,
+        "crossings_per_node": crossings_per_node,
+        "top_crossing_nodes": top_x,
+        "top_crossing_edges": top_e,
+        "bbox": [round(bw, 1), round(bh, 1)],
+        "nn_min": pct(0.0),
+        "nn_p50": pct(0.5),
+        "nn_p10": pct(0.1),
+        "pairs_closer_than_min_dist": close_pairs,
+        "footprint_overlap_pairs": overlap_pairs,
+        "label_overlap_pairs": label_overlap_pairs,
+        "space_utilization": space_utilization,
+        "spacing_guide": {
+            "icon_px": ICON_SIZE,
+            "min_center_dx": MIN_CENTER_DX,
+            "min_center_dy": MIN_CENTER_DY,
+            "recommended_center_dx": REC_CENTER_DX,
+            "recommended_center_dy": REC_CENTER_DY,
+            "min_center_dist": MIN_CENTER_DIST,
+            "note": (
+                "API x/y = icon center. Caption sits under icon (8px, nowrap). "
+                "Layout should keep centers ≥ recommended dx/dy so icons+names do not collide. "
+                "space_utilization = n*rec_dx*rec_dy / bbox_area (higher is denser; avoid empty zoom)."
+            ),
+        },
+        "crossing_definition": (
+            "Undirected NE-NE links (port edges collapsed). "
+            "A crossing = two link segments properly intersect in the plane; "
+            "shared endpoints do not count."
+        ),
+    }
+
+
+def grade_layout(
+    metrics: dict[str, Any],
+    *,
+    ume_reference: bool = False,
+) -> dict[str, Any]:
+    """Pass/warn/fail vs spacing + crossing density.
+
+    Default: hard zero footprint/label overlap. Set ume_reference=True to tolerate
+    tiny residue when scoring engineer UME canvases.
+    """
+    n = int(metrics.get("node_count") or 0)
+    cross = int(metrics.get("edge_crossings") or 0)
+    cpl = float(metrics.get("crossings_per_link") or 0.0)
+    overlaps = int(metrics.get("footprint_overlap_pairs") or 0)
+    label_overlaps = int(metrics.get("label_overlap_pairs") or 0)
+    nn_p50 = metrics.get("nn_p50")
+    util = metrics.get("space_utilization")
+    issues: list[str] = []
+    # Crossing budgets: absolute + crossings_per_link (UME KRO 201-500 p50≈0.16, p90≈0.16)
+    if n <= 50:
+        cross_warn, cross_fail = 5, 20
+        cpl_warn, cpl_fail = 0.05, 0.20
+    elif n <= 200:
+        cross_warn, cross_fail = 20, 80
+        cpl_warn, cpl_fail = 0.10, 0.25
+    elif n <= 500:
+        cross_warn, cross_fail = 80, 200
+        cpl_warn, cpl_fail = 0.16, 0.30
+    else:
+        cross_warn, cross_fail = 150, 400
+        cpl_warn, cpl_fail = 0.18, 0.35
+    if cross >= cross_fail or cpl >= cpl_fail:
+        cross_grade = "fail"
+        issues.append(f"crossings={cross}/cpl={cpl} (fail {cross_fail}/{cpl_fail})")
+    elif cross >= cross_warn or cpl >= cpl_warn:
+        cross_grade = "warn"
+        issues.append(f"crossings={cross}/cpl={cpl} (warn {cross_warn}/{cpl_warn})")
+    else:
+        cross_grade = "ok"
+
+    if ume_reference:
+        overlap_warn = max(3, n // 100)
+        overlap_fail = max(10, n // 40)
+        hard_overlap = overlaps > overlap_fail
+        soft_overlap = overlaps > overlap_warn
+    else:
+        # Absolute: icons/names must not overlap or block each other.
+        hard_overlap = overlaps > 0 or label_overlaps > 0
+        soft_overlap = False
+        if overlaps > 0:
+            issues.append(f"footprint_overlaps={overlaps}>0")
+        if label_overlaps > 0:
+            issues.append(f"label_overlaps={label_overlaps}>0")
+
+    util_warn = 0.08
+    util_fail = 0.04
+    util_f = float(util) if util is not None else None
+    util_bad = util_f is not None and util_f < util_fail
+    util_soft = util_f is not None and util_f < util_warn
+
+    if hard_overlap or util_bad or (nn_p50 is not None and float(nn_p50) < 80):
+        space_grade = "fail"
+        if util_bad:
+            issues.append(f"space_utilization={util_f}<{util_fail}")
+        if nn_p50 is not None and float(nn_p50) < 80:
+            issues.append(f"nn_p50={nn_p50}<80")
+    elif soft_overlap or util_soft or (nn_p50 is not None and float(nn_p50) < MIN_CENTER_DIST):
+        space_grade = "warn"
+        if soft_overlap and ume_reference:
+            issues.append(f"footprint_overlaps={overlaps}")
+        if util_soft:
+            issues.append(f"space_utilization={util_f}<{util_warn}")
+        if nn_p50 is not None and float(nn_p50) < MIN_CENTER_DIST:
+            issues.append(f"nn_p50={nn_p50}<{MIN_CENTER_DIST}")
+    else:
+        space_grade = "ok"
+
+    order = {"ok": 0, "warn": 1, "fail": 2}
+    overall = max([cross_grade, space_grade], key=lambda g: order[g])
+    return {
+        "overall": overall,
+        "crossing_grade": cross_grade,
+        "spacing_grade": space_grade,
+        "issues": issues,
+        "budgets": {
+            "cross_warn": cross_warn,
+            "cross_fail": cross_fail,
+            "min_center_dist": MIN_CENTER_DIST,
+            "recommended_dx": REC_CENTER_DX,
+            "recommended_dy": REC_CENTER_DY,
+            "overlap_hard_zero": not ume_reference,
+            "util_warn": util_warn,
+            "util_fail": util_fail,
+        },
+        "hint": (
+            "Fewer edge_crossings is better. Prefer crossings_per_link as size-normalized score. "
+            "Hard spacing: footprint_overlap_pairs=0 and label_overlap_pairs=0; "
+            "raise space_utilization without stacking icons."
+        ),
+    }

netx_api/mcp_remote/topology/layout_ops/__init__.py

@@ -0,0 +1,77 @@
+"""Composable topology layout atoms + multipass recipes.
+
+Usage:
+  state = build_state_from_nodes_edges(nodes, edges)
+  state, trace, score = run_recipe(state, "smd_corridor_v1", LayoutParams(...))
+  positions = positions_for_api(state)
+
+Scoped / block ops:
+  select_scope(mode=component|bbox|layer|ids|all)
+  list_blocks(mode=component|hub_territory|leiden|soft|…)
+  foreach_blocks in recipes runs sub-passes per block (default=CC).
+  Soft partition: layout_ops.partition (hub BFS + optional igraph).
+"""
+
+from netx_api.mcp_remote.topology.layout_ops.graph_util import build_state_from_nodes_edges
+from netx_api.mcp_remote.topology.layout_ops.partition import (
+    igraph_available,
+    pack_block_centers,
+    pack_soft_blocks,
+    partition_report,
+    partition_soft_blocks,
+    resolve_block_mode,
+)
+from netx_api.mcp_remote.topology.layout_ops.recipe import (
+    RECIPES,
+    REGISTRY,
+    agg_rings_v1_passes,
+    positions_for_api,
+    run_recipe,
+    smd_corridor_compact_v1_passes,
+    smd_corridor_unstick_v1_passes,
+    smd_corridor_v1_passes,
+)
+from netx_api.mcp_remote.topology.layout_ops.rings import build_ring_skeleton
+from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local, relax_hotspots
+from netx_api.mcp_remote.topology.layout_ops.scope import list_blocks, select_scope
+from netx_api.mcp_remote.topology.layout_ops.score import score_state
+from netx_api.mcp_remote.topology.layout_ops.compose_views import (
+    compose_into_state,
+    strip_pack_blocks,
+)
+from netx_api.mcp_remote.topology.layout_ops.dual_units import (
+    find_dual_portal_units,
+    layout_dual_unit,
+)
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+__all__ = [
+    "LayoutParams",
+    "LayoutState",
+    "OpResult",
+    "REGISTRY",
+    "RECIPES",
+    "agg_rings_v1_passes",
+    "build_ring_skeleton",
+    "build_state_from_nodes_edges",
+    "compose_into_state",
+    "find_dual_portal_units",
+    "fix_overlaps_local",
+    "igraph_available",
+    "layout_dual_unit",
+    "list_blocks",
+    "pack_block_centers",
+    "pack_soft_blocks",
+    "partition_report",
+    "partition_soft_blocks",
+    "positions_for_api",
+    "relax_hotspots",
+    "resolve_block_mode",
+    "run_recipe",
+    "score_state",
+    "select_scope",
+    "strip_pack_blocks",
+    "smd_corridor_v1_passes",
+    "smd_corridor_compact_v1_passes",
+    "smd_corridor_unstick_v1_passes",
+]

netx_api/mcp_remote/topology/layout_ops/align_reference.py

@@ -0,0 +1,279 @@
+"""Align a canvas to a reference layout (e.g. hand golden / UME).
+
+Uses shared ``fabric_node_id``s. Preferred mode ``similarity``: map reference
+portal chord → target portal chord (scale+rotate+translate), then place every
+shared node from the transformed reference. Target-only leftovers stay put
+(or park toward nearest aligned neighbour).
+
+This is the escape hatch when gated local polish stalls: reuse known-good
+geometry instead of more until_limit.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.graph_util import bbox
+from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import _has_any_footprint_overlap
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _dist(a: tuple[float, float], b: tuple[float, float]) -> float:
+    return math.hypot(a[0] - b[0], a[1] - b[1])
+
+
+def _similarity_from_portals(
+    ref: dict[str, tuple[float, float]],
+    tgt_portals: dict[str, tuple[float, float]],
+    portal_ids: list[str],
+) -> tuple[float, float, float, float, float, float, float] | None:
+    """Return (cos, sin, scale, tx, ty, rx0, ry0) mapping ref → target via two portals.
+
+    x' = scale * R * (x - r0) + t0
+    """
+    if len(portal_ids) < 2:
+        return None
+    a, b = portal_ids[0], portal_ids[1]
+    if a not in ref or b not in ref or a not in tgt_portals or b not in tgt_portals:
+        return None
+    r0 = ref[a]
+    r1 = ref[b]
+    t0 = tgt_portals[a]
+    t1 = tgt_portals[b]
+    rdx, rdy = r1[0] - r0[0], r1[1] - r0[1]
+    tdx, tdy = t1[0] - t0[0], t1[1] - t0[1]
+    rlen = math.hypot(rdx, rdy)
+    tlen = math.hypot(tdx, tdy)
+    if rlen < 1e-6 or tlen < 1e-6:
+        return None
+    scale = tlen / rlen
+    ang = math.atan2(tdy, tdx) - math.atan2(rdy, rdx)
+    return (math.cos(ang), math.sin(ang), scale, t0[0], t0[1], r0[0], r0[1])
+
+
+def _apply_sim(
+    xy: tuple[float, float],
+    sim: tuple[float, float, float, float, float, float, float],
+) -> tuple[float, float]:
+    cos_a, sin_a, scale, tx, ty, rx0, ry0 = sim
+    dx, dy = xy[0] - rx0, xy[1] - ry0
+    return (
+        tx + scale * (dx * cos_a - dy * sin_a),
+        ty + scale * (dx * sin_a + dy * cos_a),
+    )
+
+
+def _procrustes_similarity(
+    src: dict[str, tuple[float, float]],
+    dst: dict[str, tuple[float, float]],
+    ids: list[str],
+) -> tuple[float, float, float, float, float, float, float] | None:
+    """Umeyama-like 2D similarity from matched point pairs (ids in both)."""
+    pts = [i for i in ids if i in src and i in dst]
+    if len(pts) < 2:
+        return None
+    sx = sum(src[i][0] for i in pts) / len(pts)
+    sy = sum(src[i][1] for i in pts) / len(pts)
+    dx = sum(dst[i][0] for i in pts) / len(pts)
+    dy = sum(dst[i][1] for i in pts) / len(pts)
+    var_s = 0.0
+    cross = 0.0  # complex: sum conj(s)*d
+    # Using: scale*R maps (s-mean_s) → (d-mean_d)
+    sum_xx = sum_yy = sum_xy = sum_yx = 0.0
+    for i in pts:
+        sx0, sy0 = src[i][0] - sx, src[i][1] - sy
+        dx0, dy0 = dst[i][0] - dx, dst[i][1] - dy
+        var_s += sx0 * sx0 + sy0 * sy0
+        sum_xx += sx0 * dx0
+        sum_yy += sy0 * dy0
+        sum_xy += sx0 * dy0
+        sum_yx += sy0 * dx0
+    if var_s < 1e-8:
+        return None
+    # R = [[c,-s],[s,c]]; from SVD of covariance — 2D closed form:
+    # mu = atan2(sum_xy - sum_yx, sum_xx + sum_yy) ... use complex
+    re = sum_xx + sum_yy
+    im = sum_xy - sum_yx
+    ang = math.atan2(im, re)
+    cos_a, sin_a = math.cos(ang), math.sin(ang)
+    # scale = trace(R^T Cov) / var_s
+    scale = (re * cos_a + im * sin_a) / var_s
+    if scale < 1e-6:
+        scale = 1.0
+    # x' = scale R (x - mean_s) + mean_d
+    return (cos_a, sin_a, scale, dx, dy, sx, sy)
+
+
+def align_to_reference(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    reference: dict[str, tuple[float, float]],
+    portal_ids: list[str] | None = None,
+    mode: str = "similarity",
+    park_missing: bool = True,
+    freeze_portals: bool = True,
+) -> OpResult:
+    """Rewrite ``state.positions`` from ``reference`` geometry.
+
+    ``mode``:
+      - ``similarity``: portal (or Procrustes) similarity; keep target portals
+        fixed when ``freeze_portals`` and portals provided.
+      - ``adopt``: copy reference coords for shared ids (normalize origin ≥40).
+    """
+    del params
+    st = state.copy()
+    pos = dict(st.positions)
+    ref = {k: v for k, v in reference.items() if k in pos or True}
+    portals = [str(p) for p in (portal_ids or []) if str(p).strip()]
+    shared = sorted(set(pos) & set(ref))
+    if len(shared) < 2:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="align_reference",
+            note="align_reference:too_few_shared",
+            params={"error": "too_few_shared", "shared_n": len(shared)},
+        )
+
+    mode_k = str(mode or "similarity").strip().lower() or "similarity"
+    g0 = count_edge_crossings(pos, st.links)
+    area0 = 0.0
+    if len(pos) >= 2:
+        x0, y0, x1, y1 = bbox(pos)
+        area0 = max((x1 - x0) * (y1 - y0), 1.0)
+
+    new_pos = dict(pos)
+    moved: set[str] = set()
+    sim = None
+    meta_mode = mode_k
+
+    if mode_k in {"adopt", "copy", "absolute"}:
+        xs = [ref[i][0] for i in shared]
+        ys = [ref[i][1] for i in shared]
+        ox, oy = min(xs), min(ys)
+        pad = 40.0
+        for nid in shared:
+            new_pos[nid] = (ref[nid][0] - ox + pad, ref[nid][1] - oy + pad)
+            moved.add(nid)
+        meta_mode = "adopt"
+    else:
+        # similarity
+        if len(portals) >= 2 and all(p in ref and p in pos for p in portals[:2]):
+            sim = _similarity_from_portals(ref, pos, portals[:2])
+        if sim is None:
+            # Procrustes on hubs: prefer portals if present else all shared
+            pivot = [p for p in portals if p in shared] if portals else shared
+            if len(pivot) < 2:
+                pivot = shared
+            sim = _procrustes_similarity(ref, pos, pivot[: min(32, len(pivot))])
+        if sim is None:
+            return OpResult(
+                state=st,
+                moved=set(),
+                op="align_reference",
+                note="align_reference:no_transform",
+                params={"error": "no_transform", "shared_n": len(shared)},
+            )
+        for nid in shared:
+            if freeze_portals and nid in portals[:2]:
+                continue  # keep target portals pinned
+            new_pos[nid] = _apply_sim(ref[nid], sim)
+            moved.add(nid)
+        meta_mode = "similarity"
+
+    # Target-only: park near nearest aligned neighbour
+    only_tgt = [n for n in pos if n not in ref]
+    parked = 0
+    if park_missing and only_tgt and shared:
+        for nid in only_tgt:
+            # nearest shared by old distance
+            nb = min(
+                shared,
+                key=lambda s: _dist(pos[nid], pos[s]),
+            )
+            old_dx = pos[nid][0] - pos[nb][0]
+            old_dy = pos[nid][1] - pos[nb][1]
+            # shrink orphan offset toward new nb (keeps relative stub)
+            new_pos[nid] = (new_pos[nb][0] + old_dx * 0.85, new_pos[nb][1] + old_dy * 0.85)
+            moved.add(nid)
+            parked += 1
+
+    # Optional flip across portal chord if it lowers crossings
+    if (
+        meta_mode == "similarity"
+        and len(portals) >= 2
+        and portals[0] in new_pos
+        and portals[1] in new_pos
+    ):
+        pa, pb = new_pos[portals[0]], new_pos[portals[1]]
+        flipped = dict(new_pos)
+        ax, ay = pa
+        bx, by = pb
+        dx, dy = bx - ax, by - ay
+        llen2 = dx * dx + dy * dy
+        if llen2 > 1e-8:
+            for nid, (x, y) in new_pos.items():
+                if nid in portals[:2]:
+                    continue
+                # reflect point across line AB
+                t = ((x - ax) * dx + (y - ay) * dy) / llen2
+                projx, projy = ax + t * dx, ay + t * dy
+                flipped[nid] = (2 * projx - x, 2 * projy - y)
+            g_a = count_edge_crossings(new_pos, st.links)
+            g_b = count_edge_crossings(flipped, st.links)
+            if g_b < g_a:
+                new_pos = flipped
+                meta_mode = "similarity_flipped"
+
+    st.positions = new_pos
+    st.last_moved = moved
+    g1 = count_edge_crossings(new_pos, st.links)
+    ov = _has_any_footprint_overlap(new_pos, st.names)
+    x0, y0, x1, y1 = bbox(new_pos) if len(new_pos) >= 2 else (0, 0, 1, 1)
+    area1 = max((x1 - x0) * (y1 - y0), 1.0)
+    meta: dict[str, Any] = {
+        "mode": meta_mode,
+        "shared_n": len(shared),
+        "moved_n": len(moved),
+        "parked_n": parked,
+        "target_only_n": len(only_tgt),
+        "ref_only_n": len(set(ref) - set(pos)),
+        "portal_ids": portals[:4],
+        "freeze_portals": bool(freeze_portals),
+        "start_crossings": g0,
+        "end_crossings": g1,
+        "overlaps": bool(ov),
+        "start_area": round(area0, 1),
+        "end_area": round(area1, 1),
+        "scale": round(float(sim[2]), 4) if sim else None,
+    }
+    st.meta["align_reference"] = meta
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="align_reference",
+        params=meta,
+        note=(
+            f"align_reference mode={meta_mode} shared={len(shared)} "
+            f"moved={len(moved)} x={g0}→{g1} area={meta['start_area']}→{meta['end_area']}"
+        ),
+    )
+
+
+def align_reference_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    o = overrides or {}
+    portals = o.get("portal_ids") or o.get("portals") or []
+    if isinstance(portals, str):
+        portals = [portals]
+    return {
+        "portal_ids": [str(x) for x in portals if str(x).strip()],
+        "mode": str(o.get("mode") or o.get("align_mode") or "similarity").strip().lower(),
+        "park_missing": bool(o.get("park_missing", True)),
+        "freeze_portals": bool(o.get("freeze_portals", True)),
+        "reference_view_id": str(
+            o.get("reference_view_id") or o.get("ref_view_id") or ""
+        ).strip(),
+    }

netx_api/mcp_remote/topology/layout_ops/bundle_orbit.py

@@ -0,0 +1,801 @@
+"""Contract pure chains / ring+chain into a super-node, orbit, then expand.
+
+When single-point ``orbit_sweep`` stalls, long chords are often owned by a
+*corridor* (deg≤2 chain) or a small ring with a dangling chain. Moving any
+interior node alone barely changes global crossings; moving the whole bundle
+as a unit can.
+
+Expand rule (hard)
+------------------
+Expansion must **not** raise global crossings. If a full-length expand invades
+crowded space, probe **minimized** packings (shorter step along the ray) and
+only accept a candidate that clears the gate after expand. No silent “apply
+now, fix later”.
+
+Modes
+-----
+- **chain**: tip/centroid samples → expand mobile nodes on anchor→tip ray
+  with scale probes.
+- **ring_chain**: triangle tip (+ dangling chain) sweeps; ring base stays;
+  expand chain outward with the same minimize-probe.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    count_edge_crossings,
+    top_crossing_nodes,
+)
+from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import (
+    _MAX_JUMP_CAP,
+    _far_field_guides,
+    _polar_grid,
+)
+from netx_api.mcp_remote.topology.layout_ops.ring_faces import extract_ring_faces
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_topology_quality import extract_chain_paths
+
+# Compact-first: tight packs before full-length (fits crowded pockets).
+# Floor must clear icon+caption; smaller steps cause member self-overlap → apply reject.
+_EXPAND_SCALES = (0.35, 0.45, 0.55, 0.7, 0.85, 1.0)
+_MIN_STEP = 80.0
+_TIP_SAMPLE_CAP = 48
+
+
+@dataclass(frozen=True)
+class Bundle:
+    """Mobile corridor with ordered path and optional fixed anchor."""
+
+    kind: str  # chain | ring_chain
+    member_ids: tuple[str, ...]
+    tip_id: str
+    base_ids: tuple[str, ...] = ()
+    path: tuple[str, ...] = ()  # ordered anchor…tip (anchor may be fixed)
+    anchor_id: str | None = None
+
+    @property
+    def key(self) -> str:
+        return f"{self.kind}:{self.tip_id}:{len(self.member_ids)}"
+
+
+def _centroid(
+    pos: dict[str, tuple[float, float]], ids: list[str] | tuple[str, ...]
+) -> tuple[float, float] | None:
+    pts = [pos[n] for n in ids if n in pos]
+    if not pts:
+        return None
+    return (sum(p[0] for p in pts) / len(pts), sum(p[1] for p in pts) / len(pts))
+
+
+def _mean_step(path: list[str] | tuple[str, ...], pos: dict[str, tuple[float, float]]) -> float:
+    pts = [pos[n] for n in path if n in pos]
+    if len(pts) < 2:
+        return 140.0
+    total = 0.0
+    for i in range(len(pts) - 1):
+        total += math.hypot(pts[i + 1][0] - pts[i][0], pts[i + 1][1] - pts[i][1])
+    return max(_MIN_STEP, total / (len(pts) - 1))
+
+
+def _walk_dangling_chain(
+    start: str,
+    adj: dict[str, set[str]],
+    *,
+    blocked: set[str],
+) -> list[str]:
+    """From ``start`` (just outside blocked), walk a deg≤2 corridor away."""
+    if start in blocked or start not in adj:
+        return []
+    path = [start]
+    prev = None
+    cur = start
+    while True:
+        nbs = [v for v in adj.get(cur, ()) if v != prev and v not in blocked]
+        if len(path) == 1:
+            low = [v for v in nbs if len(adj.get(v, ())) <= 2]
+            if len(low) == 1:
+                nxt = low[0]
+            elif len(nbs) == 1 and len(adj.get(nbs[0], ())) <= 2:
+                nxt = nbs[0]
+            else:
+                break
+        else:
+            if len(adj.get(cur, ())) > 2:
+                break
+            if len(nbs) != 1:
+                break
+            nxt = nbs[0]
+            if len(adj.get(nxt, ())) > 2 and nxt not in blocked:
+                break
+        path.append(nxt)
+        prev, cur = cur, nxt
+        if len(path) > 40:
+            break
+    return path
+
+
+def detect_chain_bundles(
+    adj: dict[str, set[str]],
+    pos: dict[str, tuple[float, float]],
+    *,
+    frozen: set[str],
+    min_nodes: int = 3,
+) -> list[Bundle]:
+    """Pure (or portal-ended) chains → mobile interiors as expandable bundles."""
+    out: list[Bundle] = []
+    seen: set[frozenset[str]] = set()
+    for full in extract_chain_paths(adj):
+        if len(full) < min_nodes:
+            continue
+        # Orient: high-deg / frozen portal first when possible.
+        ordered = list(full)
+        d0 = len(adj.get(ordered[0], ()))
+        d1 = len(adj.get(ordered[-1], ()))
+        if d1 > d0 or (ordered[-1] in frozen and ordered[0] not in frozen):
+            ordered = list(reversed(ordered))
+
+        mobile = [
+            n
+            for n in ordered
+            if n in pos
+            and n not in frozen
+            and not (len(adj.get(n, ())) > 2 and n in {ordered[0], ordered[-1]})
+        ]
+        if len(mobile) < max(2, min_nodes - 1):
+            continue
+        key = frozenset(mobile)
+        if key in seen:
+            continue
+        seen.add(key)
+
+        # Tip = free end of the oriented path (last mobile).
+        tip = mobile[-1]
+        bases = [n for n in (ordered[0], ordered[-1]) if n not in key and n in pos]
+        anchor = None
+        if ordered[0] not in key and ordered[0] in pos:
+            anchor = ordered[0]
+        elif bases:
+            anchor = bases[0]
+
+        # Path for expand: anchor (optional) + mobiles in corridor order.
+        if anchor:
+            # mobiles already follow corridor from hub side
+            path = (anchor, *mobile)
+        else:
+            path = tuple(mobile)
+
+        out.append(
+            Bundle(
+                kind="chain",
+                member_ids=tuple(mobile),
+                tip_id=tip,
+                base_ids=tuple(bases),
+                path=path,
+                anchor_id=anchor,
+            )
+        )
+    out.sort(key=lambda b: (-len(b.member_ids), b.tip_id))
+    return out
+
+
+def detect_ring_chain_bundles(
+    state: LayoutState,
+    *,
+    frozen: set[str],
+    max_ring_len: int = 5,
+) -> list[Bundle]:
+    """Ring + dangling chain → sweep the attachment tip (triangle vertex)."""
+    adj = state.adj
+    pos = state.positions
+    out: list[Bundle] = []
+    seen: set[frozenset[str]] = set()
+    faces = extract_ring_faces(state, max_len=max_ring_len, max_cycles=60)
+    for face in faces:
+        rset = set(face.node_ids)
+        if len(rset) < 3:
+            continue
+        for tip in face.node_ids:
+            if tip in frozen or tip not in pos:
+                continue
+            outs = [v for v in adj.get(tip, ()) if v not in rset]
+            for seed in outs:
+                chain = _walk_dangling_chain(seed, adj, blocked=rset)
+                if len(chain) < 1:
+                    continue
+                mobile = [tip, *chain]
+                mobile = [n for n in mobile if n not in frozen and n in pos]
+                if len(mobile) < 2:
+                    continue
+                key = frozenset(mobile)
+                if key in seen:
+                    continue
+                seen.add(key)
+                base = tuple(n for n in face.node_ids if n != tip)
+                # path: tip is anchor of the dangling chain (ring vertex stays
+                # the triangle tip we move); chain packs outward from tip.
+                path = tuple([tip, *[n for n in chain if n in key]])
+                out.append(
+                    Bundle(
+                        kind="ring_chain",
+                        member_ids=tuple(mobile),
+                        tip_id=tip,
+                        base_ids=base,
+                        path=path,
+                        anchor_id=tip,
+                    )
+                )
+    out.sort(
+        key=lambda b: (
+            0 if len(b.base_ids) == 2 else 1,
+            -len(b.member_ids),
+            b.tip_id,
+        )
+    )
+    return out
+
+
+def detect_bundles(
+    state: LayoutState,
+    *,
+    frozen: set[str] | None = None,
+) -> list[Bundle]:
+    frozen = set(frozen or ())
+    chains = detect_chain_bundles(state.adj, state.positions, frozen=frozen)
+    rings = detect_ring_chain_bundles(state, frozen=frozen)
+    used: set[str] = set()
+    out: list[Bundle] = []
+    for b in list(rings) + list(chains):
+        members = set(b.member_ids)
+        if members & used:
+            continue
+        if any(n in frozen for n in b.member_ids):
+            continue
+        used |= members
+        out.append(b)
+    return out
+
+
+def _bundle_ok(
+    members: set[str],
+    trial: dict[str, tuple[float, float]],
+    names: dict[str, str],
+    nn_floor: float,
+) -> bool:
+    """Gate: no footprint invade (members∪outsiders) and nn_floor vs outsiders."""
+    from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import (
+        _box,
+        _centers_may_overlap,
+    )
+    from netx_api.mcp_remote.topology.layout_metrics import node_footprint
+
+    # Apply refuses any footprint overlap — members must clear each other too.
+    mem_list = [n for n in members if n in trial]
+    for i, n in enumerate(mem_list):
+        ax0, ay0, ax1, ay1 = _box(n, trial, names)
+        nx, ny = trial[n]
+        fa = node_footprint(names.get(n, ""))
+        # vs other members
+        for m in mem_list[i + 1 :]:
+            mx, my = trial[m]
+            fb = node_footprint(names.get(m, ""))
+            if not _centers_may_overlap(nx, ny, mx, my, fa, fb):
+                continue
+            bx0, by0, bx1, by1 = _box(m, trial, names)
+            if ax0 < bx1 and ax1 > bx0 and ay0 < by1 and ay1 > by0:
+                return False
+        # vs outsiders + nn_floor
+        floor2 = nn_floor * nn_floor if nn_floor > 0 else 0.0
+        for b, (x, y) in trial.items():
+            if b in members:
+                continue
+            if floor2 and (x - nx) * (x - nx) + (y - ny) * (y - ny) < floor2:
+                return False
+            fb = node_footprint(names.get(b, ""))
+            if not _centers_may_overlap(nx, ny, x, y, fa, fb):
+                continue
+            bx0, by0, bx1, by1 = _box(b, trial, names)
+            if ax0 < bx1 and ax1 > bx0 and ay0 < by1 and ay1 > by0:
+                return False
+    return True
+
+
+def _expand_path_placements(
+    path: list[str],
+    *,
+    origin: tuple[float, float],
+    ux: float,
+    uy: float,
+    step: float,
+    mobile: set[str],
+    tip_xy: tuple[float, float] | None = None,
+    pin_tip: bool = False,
+) -> dict[str, tuple[float, float]]:
+    """Place mobile nodes along ray; optionally pin free tip at tip_xy."""
+    ox, oy = origin
+    out: dict[str, tuple[float, float]] = {}
+    movables = [n for n in path if n in mobile]
+    if not movables:
+        return out
+    if pin_tip and tip_xy is not None and len(movables) >= 1:
+        # Chord pack: first mobile near origin+step, last at tip_xy.
+        tx, ty = tip_xy
+        if len(movables) == 1:
+            out[movables[0]] = (tx, ty)
+            return out
+        # Keep tip fixed; distribute interiors on chord origin→tip.
+        for i, n in enumerate(movables):
+            t = (i + 1) / len(movables)
+            # start a bit off the anchor
+            out[n] = (ox + (tx - ox) * t, oy + (ty - oy) * t)
+        out[movables[-1]] = (tx, ty)
+        return out
+    for i, n in enumerate(movables):
+        k = i + 1
+        out[n] = (ox + ux * step * k, oy + uy * step * k)
+    return out
+
+
+def _probe_expand(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    names: dict[str, str],
+    bundle: Bundle,
+    *,
+    tip_xy: tuple[float, float],
+    global0: int,
+    nn_floor: float,
+    min_delta: int,
+) -> dict[str, Any] | None:
+    """Try full→minimized expands toward tip_xy; keep first non-increasing best.
+
+    Returns candidate dict with placements, or None if every expand raises
+    crossings / invades.
+    """
+    members = [n for n in bundle.member_ids if n in pos]
+    mem_set = set(members)
+    if len(members) < 2:
+        return None
+
+    path = [n for n in (bundle.path or bundle.member_ids) if n in pos or n == bundle.anchor_id]
+    if not path:
+        path = list(members)
+
+    anchor = bundle.anchor_id
+    if anchor and anchor in pos:
+        ox, oy = pos[anchor]
+    elif bundle.base_ids:
+        bpts = [pos[n] for n in bundle.base_ids if n in pos]
+        if not bpts:
+            c0 = _centroid(pos, members)
+            if not c0:
+                return None
+            ox, oy = c0
+        else:
+            ox = sum(p[0] for p in bpts) / len(bpts)
+            oy = sum(p[1] for p in bpts) / len(bpts)
+    else:
+        # No fixed anchor: use opposite end of path as soft origin (old tip side).
+        fixed = [n for n in path if n not in mem_set and n in pos]
+        if fixed:
+            ox, oy = pos[fixed[0]]
+        else:
+            c0 = _centroid(pos, members)
+            if not c0:
+                return None
+            ox, oy = c0
+
+    tx, ty = tip_xy
+    dx, dy = tx - ox, ty - oy
+    dist = math.hypot(dx, dy)
+    if dist < 60.0:
+        return None
+    ux, uy = dx / dist, dy / dist
+    base_step = _mean_step(path if len(path) >= 2 else members, pos)
+    n_mobile = len(members)
+
+    for scale in _EXPAND_SCALES:
+        step = max(_MIN_STEP, base_step * float(scale))
+        # Prefer tip-pinned chord pack: interiors on origin→tip.
+        # Minimized scales shorten tip reach so the footprint fits clearance.
+        max_reach = step * max(1, n_mobile)
+        reach = min(dist, max_reach) if scale < 0.99 else dist
+        tip_use = (ox + ux * reach, oy + uy * reach)
+        placed = _expand_path_placements(
+            path,
+            origin=(ox, oy),
+            ux=ux,
+            uy=uy,
+            step=step,
+            mobile=mem_set,
+            tip_xy=tip_use,
+            pin_tip=True,
+        )
+        if len(placed) < len(mem_set):
+            # fallback: step-only for missing
+            for n in members:
+                if n not in placed:
+                    continue
+        if not placed:
+            continue
+        trial = dict(pos)
+        trial.update(placed)
+        # _bundle_ok already rejects outsider footprint invasion + nn_floor.
+        if not _bundle_ok(mem_set, trial, names, nn_floor):
+            continue
+        g1 = count_edge_crossings(trial, links)
+        # Hard rule: expand must not raise crossings.
+        if g1 > global0:
+            continue
+        delta = g1 - global0
+        if delta > -max(1, int(min_delta)):
+            continue
+        return {
+            "x": round(tip_use[0], 1),
+            "y": round(tip_use[1], 1),
+            "r": round(math.hypot(tip_use[0] - ox, tip_use[1] - oy), 1),
+            "angle_deg": round(math.degrees(math.atan2(uy, ux)) % 360.0, 1),
+            "crossings": {"global": g1},
+            "delta": {"global": int(delta)},
+            "stretch": round(step / max(base_step, 1.0), 3),
+            "expand_scale": round(float(scale), 3),
+            "step": round(step, 1),
+            "placements": {
+                k: (round(v[0], 1), round(v[1], 1)) for k, v in placed.items()
+            },
+            "origin": [round(ox, 1), round(oy, 1)],
+        }
+    return None
+
+
+def orbit_bundle(
+    state: LayoutState,
+    bundle: Bundle,
+    *,
+    max_jump: float = 4000.0,
+    angle_step: int = 20,
+    cand_cap: int = 220,
+    nn_floor: float = 28.0,
+    min_delta: int = 1,
+    y_min: float | None = None,
+    y_max: float | None = None,
+) -> dict[str, Any]:
+    """Sample tip directions; probe minimized expands; keep improving only."""
+    pos = dict(state.positions)
+    names = dict(state.names)
+    links = list(state.links)
+    members = tuple(n for n in bundle.member_ids if n in pos)
+    if len(members) < 2:
+        return {"ok": False, "error": "bundle_too_small", "bundle": bundle.key}
+
+    c0 = _centroid(pos, members)
+    if c0 is None:
+        return {"ok": False, "error": "no_centroid", "bundle": bundle.key}
+    cx, cy = c0
+    jump = max(200.0, min(float(max_jump), _MAX_JUMP_CAP))
+    global0 = count_edge_crossings(pos, links)
+
+    # Tip seed samples around current tip / centroid.
+    tip0 = pos.get(bundle.tip_id, (cx, cy))
+    ext_nbs: set[str] = set()
+    mem_set = set(members)
+    for n in members:
+        for v in state.adj.get(n, ()):
+            if v not in mem_set and v in pos:
+                ext_nbs.add(v)
+    pseudo = f"__bundle__{bundle.tip_id}"
+    c_adj = {pseudo: set(ext_nbs)}
+    for v in ext_nbs:
+        c_adj.setdefault(v, set()).add(pseudo)
+    c_pos = {pseudo: tip0, **{v: pos[v] for v in ext_nbs}}
+
+    samples: list[tuple[float, float]] = []
+    for sx, sy, _r, _ang in _far_field_guides(c_pos, pseudo, c_adj, jump):
+        samples.append((sx, sy))
+    for sx, sy, _r, _ang in _polar_grid(
+        tip0[0], tip0[1], jump=jump, angle_step=max(12, int(angle_step))
+    ):
+        samples.append((sx, sy))
+    samples.append((cx, cy))
+    if bundle.base_ids:
+        base_pts = [pos[n] for n in bundle.base_ids if n in pos]
+        if base_pts:
+            bx = sum(p[0] for p in base_pts) / len(base_pts)
+            by = sum(p[1] for p in base_pts) / len(base_pts)
+            dx, dy = tip0[0] - bx, tip0[1] - by
+            L = math.hypot(dx, dy) or 1.0
+            ux, uy = dx / L, dy / L
+            for dist in (0.35 * jump, 0.6 * jump, 0.9 * jump):
+                samples.append((tip0[0] + ux * dist, tip0[1] + uy * dist))
+                samples.append((tip0[0] - ux * dist, tip0[1] - uy * dist))
+
+    seen: set[tuple[int, int]] = set()
+    scored: list[dict[str, Any]] = []
+    tip_cap = min(int(cand_cap), _TIP_SAMPLE_CAP)
+    for sx, sy in samples:
+        if y_min is not None and sy < y_min:
+            continue
+        if y_max is not None and sy > y_max:
+            continue
+        key = (int(round(sx / 12.0) * 12), int(round(sy / 12.0) * 12))
+        if key in seen:
+            continue
+        seen.add(key)
+        if len(seen) > tip_cap:
+            break
+        if math.hypot(sx - tip0[0], sy - tip0[1]) < 40.0 and math.hypot(
+            sx - cx, sy - cy
+        ) < 40.0:
+            continue
+        probed = _probe_expand(
+            pos,
+            links,
+            names,
+            bundle,
+            tip_xy=(sx, sy),
+            global0=global0,
+            nn_floor=nn_floor,
+            min_delta=min_delta,
+        )
+        if probed is None:
+            continue
+        if int(probed["delta"]["global"]) >= 0:
+            continue
+        scored.append(probed)
+        # Enough improving tips — rank later.
+        if len(scored) >= 8:
+            break
+
+    scored.sort(
+        key=lambda c: (
+            int(c["delta"]["global"]),
+            float(c.get("expand_scale") or 1.0),
+            float(c.get("r") or 0.0),
+        )
+    )
+    improving = [
+        c
+        for c in scored
+        if int(c["delta"]["global"]) <= -max(1, int(min_delta))
+    ]
+    top = improving[:5]
+    for i, c in enumerate(top, start=1):
+        c["rank"] = i
+    return {
+        "ok": True,
+        "bundle": bundle.key,
+        "kind": bundle.kind,
+        "tip_id": bundle.tip_id,
+        "tip_name": names.get(bundle.tip_id, bundle.tip_id),
+        "member_n": len(members),
+        "member_ids": list(members)[:40],
+        "base_ids": list(bundle.base_ids)[:12],
+        "anchor_id": bundle.anchor_id,
+        "centroid0": [round(cx, 1), round(cy, 1)],
+        "crossings_before": global0,
+        "candidates": top,
+        "improving_n": len(improving),
+        "sampled": len(seen),
+        "max_jump": jump,
+        "expand_mode": "minimize_probe",
+    }
+
+
+def apply_bundle_pick(
+    state: LayoutState,
+    bundle: Bundle,
+    sweep: dict[str, Any],
+    *,
+    pick: int = 1,
+) -> OpResult:
+    pos = dict(state.positions)
+    cands = list(sweep.get("candidates") or [])
+    if not cands:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="bundle_orbit",
+            params={"error": "no_candidates", "bundle": bundle.key},
+            note="bundle_orbit:no_candidates",
+        )
+    idx = max(1, min(len(cands), int(pick))) - 1
+    cand = cands[idx]
+    placements = cand.get("placements") or {}
+    if not placements:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="bundle_orbit",
+            params={"error": "no_placements", "bundle": bundle.key},
+            note="bundle_orbit:no_placements",
+        )
+
+    # Safety: re-score expand; refuse if crossings rose (stale / race).
+    trial = dict(pos)
+    moved: set[str] = set()
+    for nid, xy in placements.items():
+        if nid not in trial:
+            continue
+        trial[nid] = (float(xy[0]), float(xy[1]))
+        moved.add(nid)
+    g0 = count_edge_crossings(pos, state.links)
+    g1 = count_edge_crossings(trial, state.links)
+    if g1 > g0:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="bundle_orbit",
+            params={
+                "error": "expand_raises_crossings",
+                "bundle": bundle.key,
+                "start_crossings": g0,
+                "end_crossings": g1,
+                "hint": "Expand probe failed gate; try smaller scale / other tip.",
+            },
+            note="bundle_orbit:expand_raises_crossings",
+        )
+
+    # Minimize-expand must not leave footprint overlaps (apply gate).
+    from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import _has_any_footprint_overlap
+
+    if _has_any_footprint_overlap(trial, state.names):
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="bundle_orbit",
+            params={
+                "error": "expand_invades_space",
+                "bundle": bundle.key,
+                "start_crossings": g0,
+                "end_crossings": g1,
+                "expand_scale": cand.get("expand_scale"),
+                "hint": "Space too tight; tip/placement wrong — probe smaller scale or other tip.",
+            },
+            note="bundle_orbit:expand_invades_space",
+        )
+
+    st = state.copy()
+    st.positions = trial
+    st.last_moved = moved
+    meta = {
+        "mode": "bundle_orbit",
+        "kind": bundle.kind,
+        "bundle": bundle.key,
+        "tip_id": bundle.tip_id,
+        "pick": idx + 1,
+        "member_n": len(moved),
+        "delta": int(g1 - g0),
+        "crossings": int(g1),
+        "expand_scale": cand.get("expand_scale"),
+        "step": cand.get("step"),
+        "expand_mode": "minimize_probe",
+    }
+    st.meta["bundle_orbit"] = meta
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="bundle_orbit",
+        params=meta,
+        note=(
+            f"bundle_orbit {bundle.kind} tip={bundle.tip_id} "
+            f"n={len(moved)} scale={cand.get('expand_scale')} Δ={meta['delta']}"
+        ),
+    )
+
+
+def rank_bundles_by_hotspots(
+    bundles: list[Bundle],
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    names: dict[str, str],
+    *,
+    top_n: int = 12,
+) -> list[Bundle]:
+    """Prefer bundles whose tip / members participate in crossings."""
+    if not bundles:
+        return []
+    from netx_api.mcp_remote.topology.layout_metrics import crossing_participation
+
+    _n, node_hit = crossing_participation(pos, links)[:2]
+    hot = {
+        str(r["fabric_node_id"]): int(r.get("crossing_hits") or 0)
+        for r in top_crossing_nodes(
+            pos, links, names=names, top_n=40, participation=node_hit
+        )
+    }
+
+    def score(b: Bundle) -> tuple[int, int, int]:
+        tip_h = int(node_hit.get(b.tip_id) or hot.get(b.tip_id) or 0)
+        mem_h = sum(int(node_hit.get(n) or 0) for n in b.member_ids)
+        return (tip_h + mem_h, len(b.member_ids), 1 if b.kind == "ring_chain" else 0)
+
+    ranked = sorted(bundles, key=score, reverse=True)
+    return ranked[: max(1, int(top_n))]
+
+
+def bundle_orbit_until_progress(
+    state: LayoutState,
+    *,
+    frozen_ids: set[str] | None = None,
+    max_jump: float = 5000.0,
+    max_bundles: int = 10,
+    min_delta: int = 1,
+    cand_cap: int = 200,
+    angle_step: int = 20,
+    nn_floor: float = 28.0,
+    params: LayoutParams | None = None,
+) -> OpResult:
+    """Try hotspot-ranked bundles until one expand-gated move applies."""
+    del params
+    st = state.copy()
+    frozen = set(frozen_ids or ())
+    bundles = detect_bundles(st, frozen=frozen)
+    ranked = rank_bundles_by_hotspots(
+        bundles, st.positions, st.links, st.names, top_n=max_bundles
+    )
+    global0 = count_edge_crossings(st.positions, st.links)
+    tried: list[dict[str, Any]] = []
+    for b in ranked:
+        if any(n in frozen for n in b.member_ids):
+            continue
+        sweep = orbit_bundle(
+            st,
+            b,
+            max_jump=max_jump,
+            angle_step=angle_step,
+            cand_cap=cand_cap,
+            nn_floor=nn_floor,
+            min_delta=min_delta,
+        )
+        tried.append(
+            {
+                "bundle": b.key,
+                "kind": b.kind,
+                "improving_n": int(sweep.get("improving_n") or 0),
+            }
+        )
+        if int(sweep.get("improving_n") or 0) <= 0:
+            continue
+        op = apply_bundle_pick(st, b, sweep, pick=1)
+        if not op.moved:
+            tried[-1]["apply_error"] = (op.params or {}).get("error")
+            continue
+        end_g = count_edge_crossings(op.state.positions, op.state.links)
+        if end_g > global0:
+            # Should be unreachable due to apply gate; skip defensively.
+            continue
+        meta = {
+            **(op.params or {}),
+            "start_crossings": global0,
+            "end_crossings": end_g,
+            "tried": tried[:20],
+            "bundle_n": len(bundles),
+        }
+        op.state.meta["bundle_orbit"] = meta
+        return OpResult(
+            state=op.state,
+            moved=op.moved,
+            op="bundle_orbit",
+            params=meta,
+            note=op.note,
+        )
+
+    return OpResult(
+        state=st,
+        moved=set(),
+        op="bundle_orbit",
+        params={
+            "mode": "bundle_orbit",
+            "start_crossings": global0,
+            "end_crossings": global0,
+            "delta": 0,
+            "tried": tried[:20],
+            "bundle_n": len(bundles),
+            "stop_reason": "no_candidates",
+            "expand_mode": "minimize_probe",
+        },
+        note="bundle_orbit:no_candidates",
+    )

netx_api/mcp_remote/topology/layout_ops/channel_metro.py

@@ -0,0 +1,72 @@
+"""channel_metro: corridor skeleton → core beam pin → channel soft petals.
+
+Composes proven atoms for giant star / core_bar canvases where ~90% of
+nodes sit on deg≤2 corridors.
+"""
+
+from __future__ import annotations
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.channels import extract_channels
+from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local
+from netx_api.mcp_remote.topology.layout_ops.pin_beam import pin_beam_rigid
+from netx_api.mcp_remote.topology.layout_ops.ring_faces import eject_intruders, extract_ring_faces
+from netx_api.mcp_remote.topology.layout_ops.skeleton import build_skeleton
+from netx_api.mcp_remote.topology.layout_ops.soft_petals import soft_petals_greedy
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_ops.transforms import normalize_origin
+
+
+def build_channel_metro_skeleton(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """Compose: Tutte skeleton → pin_beam → soft_petals → ring eject → fix ov."""
+    params = params or LayoutParams()
+    channels = extract_channels(state)
+    rings = extract_ring_faces(state, max_len=5, max_cycles=40)
+
+    sk = build_skeleton(state, params)
+    st = sk.state
+
+    beam = pin_beam_rigid(st, params)
+    st = beam.state
+
+    petals = soft_petals_greedy(st, params)
+    st = petals.state
+
+    cores = {n for n, ly in st.layers.items() if ly == "core"}
+    aggs = {n for n, ly in st.layers.items() if ly == "agg"}
+    st.positions = eject_intruders(
+        dict(st.positions),
+        rings,
+        push=max(params.side * 0.25, 36.0),
+        protected=cores | aggs,
+    )
+
+    st = fix_overlaps_local(st, params).state
+    st = normalize_origin(st, params).state
+
+    cross = count_edge_crossings(st.positions, st.links)
+    st.meta = dict(st.meta or {})
+    st.meta["rings_mode"] = "channel_metro"
+    st.meta["channel_metro"] = {
+        "channels": len(channels),
+        "rings": len(rings),
+        "pin_beam": beam.note,
+        "soft_petals": petals.note,
+        "crossings": cross,
+        "skeleton": sk.note,
+    }
+    return OpResult(
+        state=st,
+        moved=set(st.positions.keys()),
+        op="build_channel_metro_skeleton",
+        params={
+            "channel_count": len(channels),
+            "ring_count": len(rings),
+            "crossings": cross,
+            "pin_beam": beam.params,
+            "soft_petals": petals.params,
+        },
+        note=f"channel_metro ch={len(channels)} {beam.note}; {petals.note} x={cross}",
+    )

netx_api/mcp_remote/topology/layout_ops/channels.py

@@ -0,0 +1,404 @@
+"""Path-based channels: maximal deg≤2 corridors as atomic layout units.
+
+Inspired by Path-Based Framework (PBF): treat corridors as first-class
+geometry (straight spines), not independent force-directed points.
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState
+from netx_api.mcp_remote.topology.layout_topology_quality import extract_chain_paths
+
+
+def order_stubs_crossing_aware(
+    hub: str,
+    stubs: list[str],
+    adj: dict[str, set[str]],
+    member_set: set[str],
+    owner: dict[str, str],
+) -> list[str]:
+    """Stable stub order by foreign-neighbor hash angle (crossing-aware proxy)."""
+    if len(stubs) <= 2:
+        return sorted(stubs)
+
+    def score(s: str) -> tuple[float, str]:
+        terr = {n for n, o in owner.items() if o == s} | {s}
+        foreign: list[str] = []
+        for n in terr:
+            for v in adj.get(n, ()):
+                if v == hub or v in terr or v in member_set:
+                    continue
+                foreign.append(v)
+        ang = (hash(s) % 1000) / 1000.0 * 2 * math.pi
+        if foreign:
+            ang = (hash(min(foreign)) % 1000) / 1000.0 * 2 * math.pi
+        return (ang, s)
+
+    return [s for _, s in sorted((score(s) for s in stubs), key=lambda t: t[0])]
+
+
+@dataclass(frozen=True)
+class Channel:
+    """Ordered path hub…access… (portals may be high-degree)."""
+
+    node_ids: tuple[str, ...]
+
+    @property
+    def length(self) -> int:
+        return len(self.node_ids)
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "n": self.length,
+            "ends": [self.node_ids[0], self.node_ids[-1]] if self.node_ids else [],
+            "node_ids": list(self.node_ids)[:40],
+        }
+
+
+def extract_channels(state: LayoutState) -> list[Channel]:
+    """All maximal deg≤2 corridors (with portal hubs when unique)."""
+    paths = extract_chain_paths(state.adj)
+    out = [Channel(tuple(p)) for p in paths if len(p) >= 3]
+    out.sort(key=lambda c: (-c.length, c.node_ids[0] if c.node_ids else ""))
+    return out
+
+
+def channels_touching(
+    channels: list[Channel], node_set: set[str]
+) -> list[Channel]:
+    """Channels with ≥2 nodes inside ``node_set`` (block-local)."""
+    return [c for c in channels if sum(1 for n in c.node_ids if n in node_set) >= 2]
+
+
+def place_channel_ray(
+    path: list[str],
+    *,
+    origin: tuple[float, float],
+    ux: float,
+    uy: float,
+    step: float,
+    pinned: set[str] | None = None,
+) -> dict[str, tuple[float, float]]:
+    """Lay path along a unit ray from origin; skip pinned nodes (keep them).
+
+    Distance index: if path[0] is pinned (hub at origin), node at path[i]
+    sits at i*step; otherwise path[0] is at 1*step.
+    """
+    pinned = pinned or set()
+    ox, oy = origin
+    out: dict[str, tuple[float, float]] = {}
+    hub_first = bool(path and path[0] in pinned)
+    for i, n in enumerate(path):
+        if n in pinned:
+            continue
+        k = i if hub_first else i + 1
+        out[n] = (ox + ux * step * k, oy + uy * step * k)
+    return out
+
+
+def channels_report(state: LayoutState) -> dict[str, Any]:
+    ch = extract_channels(state)
+    nodes = {n for c in ch for n in c.node_ids}
+    return {
+        "channel_count": len(ch),
+        "channel_nodes": len(nodes),
+        "longest": ch[0].length if ch else 0,
+        "sample": [c.as_dict() for c in ch[:12]],
+        "tip": "通道是 deg≤2 走廊原子；channel_metro 按射线拉直，勿逐点力导。",
+    }
+
+
+def _straighten_path_positions(
+    path: list[str],
+    pos: dict[str, tuple[float, float]],
+    *,
+    mode: str = "chord",
+    step: float | None = None,
+    bend_frac: float = 0.5,
+) -> dict[str, tuple[float, float]] | None:
+    """Reposition interior (and optionally ends) of path. Ends stay fixed for chord/L."""
+    pts = [pos[n] for n in path if n in pos]
+    if len(pts) < 3 or len(pts) != len(path):
+        return None
+    x0, y0 = pts[0]
+    x1, y1 = pts[-1]
+    n = len(path) - 1
+    out: dict[str, tuple[float, float]] = {}
+    if mode == "chord" or mode.startswith("chord_shift"):
+        dx, dy = x1 - x0, y1 - y0
+        plen = math.hypot(dx, dy) or 1.0
+        # lateral offset for chord_shift± (keeps ends fixed via taper)
+        off = 0.0
+        if mode.startswith("chord_shift"):
+            try:
+                off = float(mode.split(":", 1)[1])
+            except (IndexError, ValueError):
+                off = 0.0
+        px, py = (-dy / plen) * off, (dx / plen) * off
+        for i, nid in enumerate(path):
+            if i == 0 or i == len(path) - 1:
+                continue
+            t = i / n
+            # taper offset to 0 at ends (already skipping ends)
+            taper = math.sin(math.pi * t)
+            out[nid] = (
+                x0 + dx * t + px * taper,
+                y0 + dy * t + py * taper,
+            )
+        return out
+    if mode in {"L_hv", "L_vh"}:
+        # Orthogonal elbow; both ends fixed. bend_frac ∈ (0,1) picks knee along path.
+        bf = min(0.85, max(0.15, float(bend_frac)))
+        bend_i = max(1, min(n - 1, int(round(n * bf))))
+        for i, nid in enumerate(path):
+            if i == 0 or i == len(path) - 1:
+                continue
+            if mode == "L_hv":
+                # (x0,y0) → (x1,y0) → (x1,y1)
+                if i <= bend_i:
+                    t = i / bend_i
+                    out[nid] = (x0 + (x1 - x0) * t, y0)
+                else:
+                    t = (i - bend_i) / (n - bend_i)
+                    out[nid] = (x1, y0 + (y1 - y0) * t)
+            else:
+                # (x0,y0) → (x0,y1) → (x1,y1)
+                if i <= bend_i:
+                    t = i / bend_i
+                    out[nid] = (x0, y0 + (y1 - y0) * t)
+                else:
+                    t = (i - bend_i) / (n - bend_i)
+                    out[nid] = (x0 + (x1 - x0) * t, y1)
+        return out
+    if mode in {"horizontal", "vertical"}:
+        # Keep first endpoint; lay along axis with equal step
+        st = step or (
+            math.hypot(x1 - x0, y1 - y0) / max(n, 1)
+            if math.hypot(x1 - x0, y1 - y0) > 1
+            else 180.0
+        )
+        st = max(120.0, min(st, 280.0))
+        for i, nid in enumerate(path):
+            if i == 0:
+                continue
+            if mode == "horizontal":
+                out[nid] = (x0 + st * i, y0)
+            else:
+                out[nid] = (x0, y0 + st * i)
+        return out
+    return None
+
+
+def _path_axis_credit(
+    path: list[str],
+    pos: dict[str, tuple[float, float]],
+    *,
+    tol_deg: float = 8.0,
+    tol_px: float = 4.0,
+) -> float:
+    """Mean H/V credit along path edges (H=1, V=0.75, diagonal=0)."""
+    import math
+
+    if len(path) < 2:
+        return 1.0
+    tol_rad = math.radians(max(0.1, float(tol_deg)))
+    tol_p = max(0.0, float(tol_px))
+    credits: list[float] = []
+    for i in range(len(path) - 1):
+        a, b = path[i], path[i + 1]
+        if a not in pos or b not in pos:
+            continue
+        ax, ay = pos[a]
+        bx, by = pos[b]
+        dx, dy = bx - ax, by - ay
+        length = math.hypot(dx, dy)
+        if length < 1e-9:
+            credits.append(1.0)
+            continue
+        adx, ady = abs(dx), abs(dy)
+        ang_h = math.atan2(ady, adx)
+        ang_v = abs(math.pi / 2 - ang_h)
+        if ady <= tol_p or ang_h <= tol_rad:
+            credits.append(1.0)
+        elif adx <= tol_p or ang_v <= tol_rad:
+            credits.append(0.75)
+        else:
+            credits.append(0.0)
+    return sum(credits) / max(len(credits), 1)
+
+
+def straighten_channels_greedy(
+    state: LayoutState,
+    params: Any | None = None,
+    *,
+    step: float | None = None,
+    min_len: int = 3,
+    max_passes: int = 3,
+):
+    """Straighten deg≤2 channels.
+
+    Prefer fewer global crossings; when crossings stay equal (incl. already 0),
+    accept H/V / L modes that raise path axis credit (metro look).
+    """
+    from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+    from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local
+    from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, OpResult
+
+    params = params or LayoutParams()
+    step_px = float(step if step is not None else max(getattr(params, "pitch", 180.0), 160.0))
+    pos = dict(state.positions)
+    links = list(state.links)
+    cross = count_edge_crossings(pos, links)
+    accepted: list[dict[str, Any]] = []
+    base_modes = (
+        "chord",
+        "horizontal",
+        "vertical",
+        "L_hv",
+        "L_vh",
+    )
+    bend_fracs = (0.35, 0.5, 0.65)
+    shift_offs = sorted(
+        {
+            -2.0 * step_px,
+            -1.5 * step_px,
+            -step_px,
+            -0.5 * step_px,
+            0.5 * step_px,
+            step_px,
+            1.5 * step_px,
+            2.0 * step_px,
+            -400.0,
+            -280.0,
+            280.0,
+            400.0,
+        }
+    )
+
+    from netx_api.mcp_remote.topology.layout_jobs import raise_if_cancelled, report_progress
+
+    for _pass in range(max(1, int(max_passes))):
+        raise_if_cancelled()
+        channels = extract_channels(state)
+        # Prefer longer kinked channels first
+        accepted_pass = 0
+        n_ch = max(1, len(channels))
+        for ci, ch in enumerate(channels):
+            if ci % 8 == 0:
+                raise_if_cancelled()
+                report_progress(
+                    "straighten_channels",
+                    pct=48.0 + 6.0 * ((_pass + ci / n_ch) / max(1, int(max_passes))),
+                    message=f"pass {_pass + 1} ch {ci + 1}/{n_ch} x={cross}",
+                    step=ci + 1,
+                    total_steps=n_ch,
+                    crossings=cross,
+                )
+            path = list(ch.node_ids)
+            if len(path) < min_len:
+                continue
+            if any(n not in pos for n in path):
+                continue
+            axis0 = _path_axis_credit(path, pos)
+            # Lexicographic key: fewer crossings, then higher axis credit.
+            best_key = (cross, -axis0)
+            best_pos = None
+            best_mode = None
+            modes = list(base_modes) + [f"chord_shift:{o:.1f}" for o in shift_offs]
+            for mode in modes:
+                fracs = bend_fracs if mode.startswith("L_") else (0.5,)
+                for bf in fracs:
+                    delta = _straighten_path_positions(
+                        path, pos, mode=mode, step=step_px, bend_frac=bf
+                    )
+                    if not delta:
+                        continue
+                    trial = dict(pos)
+                    trial.update(delta)
+                    c1 = count_edge_crossings(trial, links)
+                    if c1 > cross:
+                        continue
+                    a1 = _path_axis_credit(path, trial)
+                    key = (c1, -a1)
+                    if key < best_key:
+                        best_key = key
+                        best_pos = trial
+                        best_mode = f"{mode}@{bf:.2f}" if mode.startswith("L_") else mode
+            if best_pos is None:
+                continue
+            # light overlap fix; keep if still improved
+            st_try = state.copy()
+            st_try.positions = best_pos
+            stf = fix_overlaps_local(st_try, params).state
+            # restore pinned hubs (core/agg endpoints)
+            for end in (path[0], path[-1]):
+                if state.layers.get(end) in ("core", "agg") and end in pos:
+                    stf.positions[end] = pos[end]
+            trial2 = {k: (float(v[0]), float(v[1])) for k, v in stf.positions.items()}
+            c2 = count_edge_crossings(trial2, links)
+            a2 = _path_axis_credit(path, trial2)
+            if (c2, -a2) >= (cross, -axis0):
+                continue
+            pos = trial2
+            cross = c2
+            accepted_pass += 1
+            accepted.append(
+                {
+                    "ends": [path[0], path[-1]],
+                    "n": len(path),
+                    "mode": best_mode,
+                    "crossings": cross,
+                    "axis": round(a2, 3),
+                }
+            )
+        if accepted_pass == 0:
+            break
+
+    if not accepted:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="straighten_channels",
+            params={"accepted_n": 0},
+            note="no_channel_improved",
+        )
+    out = state.copy()
+    out.positions = pos
+    moved = {
+        n for n, p in pos.items() if n in state.positions and p != state.positions[n]
+    }
+    out.meta = dict(out.meta or {})
+    out.meta["straighten_channels"] = {"accepted": accepted, "crossings": cross}
+    return OpResult(
+        state=out,
+        moved=moved,
+        op="straighten_channels",
+        params={"accepted_n": len(accepted), "accepted": accepted[:20], "crossings": cross},
+        note=f"straighten_channels:{len(accepted)} x->{cross}",
+    )
+
+
+def straighten_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    out: dict[str, Any] = {}
+    if not overrides:
+        return out
+    if overrides.get("step") is not None:
+        try:
+            out["step"] = float(overrides["step"])
+        except (TypeError, ValueError):
+            pass
+    if overrides.get("min_len") is not None:
+        try:
+            out["min_len"] = int(overrides["min_len"])
+        except (TypeError, ValueError):
+            pass
+    if overrides.get("max_passes") is not None:
+        try:
+            out["max_passes"] = int(overrides["max_passes"])
+        except (TypeError, ValueError):
+            pass
+    return out

netx_api/mcp_remote/topology/layout_ops/clear_edge_hits.py

@@ -0,0 +1,652 @@
+"""Eject nodes that sit on / too close to non-incident edges.
+
+Default: orthogonal eject. With ``preserve_axis`` + grid pitch/side, only accept
+moves that keep incident H/V edges and snap to the metro grid — safe after
+``ortho_metro`` (the old 60px nudge was breaking right angles).
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    EDGE_CLEARANCE_THR,
+    REC_CENTER_DX,
+    REC_CENTER_DY,
+    compute_edge_clearance,
+    count_edge_crossings,
+    node_footprint,
+    point_segment_dist,
+)
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+_AXIS_TOL = 8.0
+
+
+def clear_edge_params_from_overrides(params: dict[str, Any] | None) -> dict[str, Any]:
+    p = params or {}
+    out: dict[str, Any] = {
+        "top_n": int(p.get("top_n") or 12),
+        "thr": float(p.get("thr") or EDGE_CLEARANCE_THR),
+        "margin": float(p.get("margin") or 20.0),
+        "max_moves": int(p.get("max_moves") or 24),
+        "preserve_axis": bool(p.get("preserve_axis", False)),
+        "rounds": int(p.get("rounds") or 1),
+    }
+    if p.get("pitch") is not None:
+        out["pitch"] = float(p["pitch"])
+    if p.get("side") is not None:
+        out["side"] = float(p["side"])
+    portals = p.get("portal_ids") or p.get("portals") or []
+    if isinstance(portals, str):
+        portals = [portals]
+    frozen = set(str(x) for x in (p.get("frozen_ids") or []) if str(x).strip())
+    frozen |= {str(x) for x in portals if str(x).strip()}
+    out["frozen_ids"] = frozen
+    out["max_eject_degree"] = int(p.get("max_eject_degree") or 5)
+    return out
+
+
+def _axis_ok(a: tuple[float, float], b: tuple[float, float], tol: float = _AXIS_TOL) -> bool:
+    return abs(a[0] - b[0]) <= tol or abs(a[1] - b[1]) <= tol
+
+
+def _overlaps_any(
+    node: str,
+    pos: dict[str, tuple[float, float]],
+    names: dict[str, str],
+) -> bool:
+    x, y = pos[node]
+    fa = node_footprint(names.get(node, ""))
+    ax0, ay0, ax1, ay1 = x + fa[0], y + fa[1], x + fa[2], y + fa[3]
+    for b, (bx, by) in pos.items():
+        if b == node:
+            continue
+        if abs(bx - x) > 120 and abs(by - y) > 100:
+            continue
+        fb = node_footprint(names.get(b, ""))
+        bx0, by0, bx1, by1 = bx + fb[0], by + fb[1], bx + fb[2], by + fb[3]
+        if ax0 < bx1 and ax1 > bx0 and ay0 < by1 and ay1 > by0:
+            return True
+    return False
+
+
+def _incident_axis_score(
+    nid: str,
+    pos: dict[str, tuple[float, float]],
+    adj: dict[str, set[str]],
+) -> int:
+    s = 0
+    for nb in adj.get(nid) or ():
+        if nb in pos and _axis_ok(pos[nid], pos[nb]):
+            s += 1
+    return s
+
+
+def _global_axis_score(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+) -> int:
+    return sum(
+        1
+        for a, b in links
+        if a in pos and b in pos and _axis_ok(pos[a], pos[b])
+    )
+
+
+def _on_open_segment(
+    p: tuple[float, float],
+    a: tuple[float, float],
+    b: tuple[float, float],
+    *,
+    thr: float,
+) -> bool:
+    d, t = point_segment_dist(p, a, b)
+    return d < thr and 0.05 < t < 0.95
+
+
+def _candidates_orthogonal(
+    p: tuple[float, float],
+    a: tuple[float, float],
+    b: tuple[float, float],
+    *,
+    target: float,
+) -> list[tuple[float, float]]:
+    """Axis-aligned eject candidates at distance ``target`` from segment AB."""
+    ax, ay = a
+    bx, by = b
+    px, py = p
+    dx, dy = bx - ax, by - ay
+    L = math.hypot(dx, dy)
+    cands: list[tuple[float, float]] = []
+    if L < 1e-9:
+        cands.extend(
+            [
+                (px + target, py),
+                (px - target, py),
+                (px, py + target),
+                (px, py - target),
+            ]
+        )
+        return cands
+
+    t = max(0.0, min(1.0, ((px - ax) * dx + (py - ay) * dy) / (L * L)))
+    fx, fy = ax + t * dx, ay + t * dy
+
+    cands.extend(
+        [
+            (fx, fy + target),
+            (fx, fy - target),
+            (fx + target, fy),
+            (fx - target, fy),
+        ]
+    )
+    ux, uy = -dy / L, dx / L
+    cands.append((fx + ux * target, fy + uy * target))
+    cands.append((fx - ux * target, fy - uy * target))
+
+    if abs(dx) >= abs(dy):
+        cands.insert(0, (px, fy + target if py >= fy else fy - target))
+        cands.insert(1, (px, fy - target if py >= fy else fy + target))
+    else:
+        cands.insert(0, (fx + target if px >= fx else fx - target, py))
+        cands.insert(1, (fx - target if px >= fx else fx + target, py))
+
+    seen: set[tuple[float, float]] = set()
+    out: list[tuple[float, float]] = []
+    for xy in cands:
+        key = (round(xy[0], 2), round(xy[1], 2))
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append((float(xy[0]), float(xy[1])))
+    return out
+
+
+def _candidates_grid_ortho(
+    p: tuple[float, float],
+    a: tuple[float, float],
+    b: tuple[float, float],
+    *,
+    pitch: float,
+    side: float,
+    max_steps: int = 8,
+    neighbor_xy: list[tuple[float, float]] | None = None,
+) -> list[tuple[float, float]]:
+    """Eject perpendicular to H/V trunk by whole grid steps (preserve metro)."""
+    ax, ay = a
+    bx, by = b
+    px, py = p
+    horiz = abs(ay - by) <= _AXIS_TOL
+    vert = abs(ax - bx) <= _AXIS_TOL
+    cands: list[tuple[float, float]] = []
+    # Prefer landing that still shares H/V with a neighbor (keep incident ortho).
+    for nxy in neighbor_xy or ():
+        nx, ny = nxy
+        if horiz:
+            for sign in (1, -1):
+                cands.append((nx, py + sign * side))  # V to neighbor, off row
+                cands.append((nx, py + sign * 2 * side))
+        elif vert:
+            for sign in (1, -1):
+                cands.append((px + sign * pitch, ny))  # H to neighbor, off col
+                cands.append((px + sign * 2 * pitch, ny))
+    if horiz:
+        for s in range(1, max_steps + 1):
+            for sign in (1, -1):
+                cands.append((px, py + sign * s * side))
+                cands.append((px + sign * s * pitch, py + sign * side))
+                cands.append((px + sign * s * pitch, py - sign * side))
+    elif vert:
+        for s in range(1, max_steps + 1):
+            for sign in (1, -1):
+                cands.append((px + sign * s * pitch, py))
+                cands.append((px + sign * pitch, py + sign * s * side))
+                cands.append((px - sign * pitch, py + sign * s * side))
+    else:
+        return _candidates_orthogonal(p, a, b, target=max(side, pitch) * 0.5)
+
+    if horiz:
+        cands = [(px, py + side), (px, py - side)] + cands
+    elif vert:
+        cands = [(px + pitch, py), (px - pitch, py)] + cands
+
+    seen: set[tuple[float, float]] = set()
+    out: list[tuple[float, float]] = []
+    for xy in cands:
+        key = (round(xy[0], 1), round(xy[1], 1))
+        if key in seen:
+            continue
+        seen.add(key)
+        out.append(xy)
+    return out
+
+
+def clear_edge_hits(
+    state: LayoutState,
+    params: LayoutParams,
+    *,
+    top_n: int = 12,
+    thr: float = EDGE_CLEARANCE_THR,
+    margin: float = 20.0,
+    max_moves: int = 24,
+    preserve_axis: bool = False,
+    pitch: float | None = None,
+    side: float | None = None,
+    rounds: int = 1,
+    frozen_ids: set[str] | None = None,
+    max_eject_degree: int = 5,
+) -> OpResult:
+    """Move nodes off non-incident edges; gate on crossings + overlaps.
+
+    ``preserve_axis=True``: snap to pitch/side grid, keep incident H/V count,
+    multi-round until no progress — use after ``ortho_metro``.
+    ``frozen_ids``: never eject these (eye portals).
+    ``max_eject_degree``: skip hubs at/above this degree (default 5).
+    """
+    del params
+    st = state.copy()
+    pos = dict(st.positions)
+    names = st.names
+    links = list(st.links)
+    adj = st.adj
+    frozen = {str(x) for x in (frozen_ids or ()) if str(x).strip()}
+    max_eject_degree = max(2, int(max_eject_degree))
+    pitch_f = float(pitch if pitch is not None else REC_CENTER_DX)
+    side_f = float(side if side is not None else REC_CENTER_DY)
+    rounds_n = max(1, int(rounds))
+    if preserve_axis:
+        rounds_n = max(rounds_n, 6)
+        top_n = max(top_n, 40)
+        max_moves = max(max_moves, 80)
+
+    clr0 = compute_edge_clearance(pos, links, names=names, thr=thr, top_n=max(top_n, 5))
+    hits_before = int(clr0.get("edge_clearance_hits") or 0)
+    if clr0.get("edge_clearance_skipped"):
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="clear_edge_hits",
+            note="clear_edge_hits:skipped_large",
+            params={"hits_before": hits_before, "skipped": True},
+        )
+    if hits_before <= 0:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="clear_edge_hits",
+            note="clear_edge_hits:noop",
+            params={"hits_before": 0, "hits_after": 0, "moved_n": 0},
+        )
+
+    moved: set[str] = set()
+    accepted: list[dict[str, Any]] = []
+    target = float(thr) + float(margin)
+    x0 = count_edge_crossings(pos, links)
+    ax0 = _global_axis_score(pos, links)
+    # Never allow crossing rise — even with preserve_axis (metro snap).
+    x_slack = 0
+    deg = {n: len(adj.get(n) or ()) for n in pos}
+
+    def _move_budget(nid: str) -> float:
+        d = deg.get(nid, 0)
+        if d >= 5:
+            return max(pitch_f, side_f) * 1.5
+        if d >= 3:
+            return max(pitch_f, side_f) * 3.0
+        return max(pitch_f, side_f) * 8.0
+
+    def _within_budget(nid: str, xy: tuple[float, float], origin: tuple[float, float]) -> bool:
+        return math.hypot(xy[0] - origin[0], xy[1] - origin[1]) <= _move_budget(nid) + 1e-6
+
+    for _rnd in range(rounds_n):
+        full = compute_edge_clearance(pos, links, names=names, thr=thr, top_n=top_n)
+        ordered = list(full.get("hit_nodes") or full.get("top_edge_hits") or [])
+        if not ordered:
+            break
+        # Prefer ejecting low-degree obstacles first (keep core hubs stable).
+        ordered.sort(
+            key=lambda h: (
+                float(h.get("dist") or 0.0),
+                deg.get(str(h.get("fabric_node_id")), 99),
+                str(h.get("fabric_node_id")),
+            )
+        )
+        progress = False
+        moves_this = 0
+        for h in ordered:
+            if moves_this >= max_moves:
+                break
+            nid = str(h["fabric_node_id"])
+            if nid not in pos:
+                continue
+            if nid in frozen:
+                continue
+            a = str(h["a_node_id"])
+            b = str(h["b_node_id"])
+            if a not in pos or b not in pos:
+                continue
+            p0 = pos[nid]
+            if not _on_open_segment(p0, pos[a], pos[b], thr=thr) and float(h.get("dist") or 99) >= thr:
+                continue
+            # High-degree hubs on a chord: skip unless caller raises cap.
+            if deg.get(nid, 0) >= int(max_eject_degree):
+                continue
+            if preserve_axis:
+                nbr_xy = [
+                    pos[nb]
+                    for nb in (adj.get(nid) or ())
+                    if nb in pos and nb not in (a, b)
+                ]
+                cands = _candidates_grid_ortho(
+                    p0,
+                    pos[a],
+                    pos[b],
+                    pitch=pitch_f,
+                    side=side_f,
+                    neighbor_xy=nbr_xy,
+                )
+            else:
+                cands = _candidates_orthogonal(p0, pos[a], pos[b], target=target)
+
+            inc0 = _incident_axis_score(nid, pos, adj)
+            for xy in cands:
+                if not _within_budget(nid, xy, p0):
+                    continue
+                trial = dict(pos)
+                trial[nid] = xy
+                if _overlaps_any(nid, trial, names):
+                    continue
+                x1 = count_edge_crossings(trial, links)
+                if x1 > x0 + x_slack:
+                    continue
+                d1, t1 = point_segment_dist(xy, pos[a], pos[b])
+                if 0.05 < t1 < 0.95 and d1 < thr:
+                    continue
+                # Must leave ALL non-incident open segments (not just a-b).
+                still_hit = False
+                for ea, eb in links:
+                    if nid in (ea, eb) or ea not in trial or eb not in trial:
+                        continue
+                    if _on_open_segment(xy, trial[ea], trial[eb], thr=thr):
+                        still_hit = True
+                        break
+                if still_hit:
+                    continue
+                if preserve_axis:
+                    inc1 = _incident_axis_score(nid, trial, adj)
+                    if inc1 < inc0:
+                        continue
+                    ax1 = _global_axis_score(trial, links)
+                    if ax1 < ax0 - 2:  # allow small axis trade for clearance
+                        continue
+                pos[nid] = xy
+                moved.add(nid)
+                moves_this += 1
+                progress = True
+                x0 = x1
+                if preserve_axis:
+                    ax0 = _global_axis_score(pos, links)
+                accepted.append(
+                    {
+                        "fabric_node_id": nid,
+                        "from": [round(p0[0], 1), round(p0[1], 1)],
+                        "to": [round(xy[0], 1), round(xy[1], 1)],
+                        "edge": [a, b],
+                        "dist_before": round(float(h["dist"]), 2),
+                        "dist_after": round(d1, 2),
+                    }
+                )
+                break
+        if not progress and preserve_axis:
+            # Phase 2: translate the whole H/V trunk off the obstacle row/col
+            # (keeps the edge axis-aligned; frees every node that sat on it).
+            for h in ordered:
+                if moves_this >= max_moves:
+                    break
+                nid = str(h["fabric_node_id"])
+                a = str(h["a_node_id"])
+                b = str(h["b_node_id"])
+                if nid not in pos or a not in pos or b not in pos:
+                    continue
+                if not _on_open_segment(pos[nid], pos[a], pos[b], thr=thr):
+                    continue
+                ax, ay = pos[a]
+                bx, by = pos[b]
+                horiz = abs(ay - by) <= _AXIS_TOL
+                vert = abs(ax - bx) <= _AXIS_TOL
+                if not horiz and not vert:
+                    continue
+                # Prefer moving the lower-degree endpoint pair as a rigid H/V bar
+                max_trunk_steps = 2 if max(deg.get(a, 0), deg.get(b, 0)) >= 4 else 4
+                for s in range(1, max_trunk_steps + 1):
+                    deltas = (
+                        [(0.0, s * side_f), (0.0, -s * side_f)]
+                        if horiz
+                        else [(s * pitch_f, 0.0), (-s * pitch_f, 0.0)]
+                    )
+                    for dx, dy in deltas:
+                        trial = dict(pos)
+                        trial[a] = (ax + dx, ay + dy)
+                        trial[b] = (bx + dx, by + dy)
+                        if _overlaps_any(a, trial, names) or _overlaps_any(b, trial, names):
+                            continue
+                        if _global_axis_score(trial, links) < ax0 - 2:
+                            continue
+                        x1 = count_edge_crossings(trial, links)
+                        if x1 > x0 + x_slack:
+                            continue
+                        d1, t1 = point_segment_dist(pos[nid], trial[a], trial[b])
+                        # obstacle stays put; edge moved away
+                        if 0.05 < t1 < 0.95 and d1 < thr:
+                            continue
+                        # also: no OTHER node should sit on the moved trunk
+                        blocked = False
+                        for n2, p2 in trial.items():
+                            if n2 in (a, b):
+                                continue
+                            dd, tt = point_segment_dist(p2, trial[a], trial[b])
+                            if dd < thr and 0.05 < tt < 0.95:
+                                blocked = True
+                                break
+                        if blocked:
+                            continue
+                        pos[a], pos[b] = trial[a], trial[b]
+                        moved.add(a)
+                        moved.add(b)
+                        moves_this += 2
+                        progress = True
+                        x0 = x1
+                        ax0 = _global_axis_score(pos, links)
+                        accepted.append(
+                            {
+                                "fabric_node_id": a,
+                                "with": b,
+                                "from": [round(ax, 1), round(ay, 1)],
+                                "to": [round(pos[a][0], 1), round(pos[a][1], 1)],
+                                "edge": [a, b],
+                                "mode": "translate_trunk",
+                                "cleared": nid,
+                            }
+                        )
+                        break
+                    if progress:
+                        break
+                if progress:
+                    break
+        if not progress and preserve_axis:
+            # Phase 3: break the occluding H/V chord (prefer low-deg endpoint).
+            # Prefer this over ejecting hubs — layout sense > pure clearance force.
+            for h in ordered:
+                if moves_this >= max_moves:
+                    break
+                nid = str(h["fabric_node_id"])
+                a = str(h["a_node_id"])
+                b = str(h["b_node_id"])
+                if nid not in pos or a not in pos or b not in pos:
+                    continue
+                if not _on_open_segment(pos[nid], pos[a], pos[b], thr=thr):
+                    continue
+                ax, ay = pos[a]
+                bx, by = pos[b]
+                horiz = abs(ay - by) <= _AXIS_TOL
+                vert = abs(ax - bx) <= _AXIS_TOL
+                if not horiz and not vert:
+                    continue
+                ends = sorted([a, b], key=lambda n: (deg.get(n, 0), n))
+                cleared_local = False
+                for end in ends:
+                    ox, oy = pos[end]
+                    # Hubs: only 1 grid step off the shared track (keep layout sense).
+                    max_step = 1 if deg.get(end, 0) >= 5 else (2 if deg.get(end, 0) >= 3 else 4)
+                    for step in range(1, max_step + 1):
+                        step_deltas = (
+                            [(0.0, step * side_f), (0.0, -step * side_f)]
+                            if horiz
+                            else [(step * pitch_f, 0.0), (-step * pitch_f, 0.0)]
+                        )
+                        for dx, dy in step_deltas:
+                            trial = dict(pos)
+                            trial[end] = (ox + dx, oy + dy)
+                            if _overlaps_any(end, trial, names):
+                                continue
+                            x1 = count_edge_crossings(trial, links)
+                            if x1 > x0 + x_slack + 6:
+                                continue
+                            if _on_open_segment(pos[nid], trial[a], trial[b], thr=thr):
+                                continue
+                            blocked = False
+                            for n2, p2 in trial.items():
+                                if n2 in (a, b):
+                                    continue
+                                if _on_open_segment(p2, trial[a], trial[b], thr=thr):
+                                    blocked = True
+                                    break
+                            if blocked:
+                                continue
+                            pos[end] = trial[end]
+                            moved.add(end)
+                            moves_this += 1
+                            progress = True
+                            cleared_local = True
+                            x0 = x1
+                            ax0 = _global_axis_score(pos, links)
+                            accepted.append(
+                                {
+                                    "fabric_node_id": end,
+                                    "from": [round(ox, 1), round(oy, 1)],
+                                    "to": [
+                                        round(pos[end][0], 1),
+                                        round(pos[end][1], 1),
+                                    ],
+                                    "edge": [a, b],
+                                    "mode": "break_chord",
+                                    "cleared": nid,
+                                }
+                            )
+                            break
+                        if cleared_local:
+                            break
+                    if cleared_local:
+                        break
+                if progress:
+                    break
+        if not progress and preserve_axis:
+            # Phase 4: local force-eject low-deg obstacles only.
+            for h in ordered:
+                if moves_this >= max_moves:
+                    break
+                nid = str(h["fabric_node_id"])
+                a = str(h["a_node_id"])
+                b = str(h["b_node_id"])
+                if nid not in pos or a not in pos or b not in pos:
+                    continue
+                if deg.get(nid, 0) >= 4:
+                    continue
+                if not _on_open_segment(pos[nid], pos[a], pos[b], thr=thr):
+                    continue
+                p0 = pos[nid]
+                nbr_xy = [pos[nb] for nb in (adj.get(nid) or ()) if nb in pos]
+                cands = _candidates_grid_ortho(
+                    p0,
+                    pos[a],
+                    pos[b],
+                    pitch=pitch_f,
+                    side=side_f,
+                    neighbor_xy=nbr_xy,
+                    max_steps=6,
+                )
+                for xy in cands:
+                    if not _within_budget(nid, xy, p0):
+                        continue
+                    trial = dict(pos)
+                    trial[nid] = xy
+                    if _overlaps_any(nid, trial, names):
+                        continue
+                    x1 = count_edge_crossings(trial, links)
+                    if x1 > x0 + x_slack + 4:
+                        continue
+                    if _on_open_segment(xy, trial[a], trial[b], thr=thr):
+                        continue
+                    still_hit = False
+                    for ea, eb in links:
+                        if nid in (ea, eb) or ea not in trial or eb not in trial:
+                            continue
+                        if _on_open_segment(xy, trial[ea], trial[eb], thr=thr):
+                            still_hit = True
+                            break
+                    if still_hit:
+                        continue
+                    pos[nid] = xy
+                    moved.add(nid)
+                    moves_this += 1
+                    progress = True
+                    x0 = x1
+                    ax0 = _global_axis_score(pos, links)
+                    accepted.append(
+                        {
+                            "fabric_node_id": nid,
+                            "from": [round(p0[0], 1), round(p0[1], 1)],
+                            "to": [round(xy[0], 1), round(xy[1], 1)],
+                            "edge": [a, b],
+                            "mode": "force_eject",
+                            "dist_before": round(float(h["dist"]), 2),
+                        }
+                    )
+                    break
+                if progress:
+                    break
+        if not progress:
+            break
+
+    st.positions = pos
+    clr1 = compute_edge_clearance(pos, links, names=names, thr=thr)
+    hits_after = int(clr1.get("edge_clearance_hits") or 0)
+    st.meta = dict(st.meta or {})
+    st.meta["clear_edge_hits"] = {
+        "hits_before": hits_before,
+        "hits_after": hits_after,
+        "moved_n": len(moved),
+        "accepted": accepted[:40],
+        "thr": thr,
+        "margin": margin,
+        "preserve_axis": preserve_axis,
+    }
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="clear_edge_hits",
+        note=f"clear_edge_hits {hits_before}->{hits_after} moved={len(moved)}"
+        + (" axis" if preserve_axis else ""),
+        params={
+            "hits_before": hits_before,
+            "hits_after": hits_after,
+            "moved_n": len(moved),
+            "accepted": accepted[:40],
+            "thr": thr,
+            "margin": margin,
+            "top_n": top_n,
+            "preserve_axis": preserve_axis,
+        },
+    )

netx_api/mcp_remote/topology/layout_ops/compact_bbox.py

@@ -0,0 +1,233 @@
+"""Uniform bbox shrink toward eye portals — eye-safe compactness.
+
+Hard gates (must all pass to accept a scale):
+- global crossings must not rise
+- footprint overlaps must stay zero
+- frozen portal_ids stay put
+
+Optional soft: prefer scales that do not worsen edge_clearance hits.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    count_edge_crossings,
+    compute_edge_clearance,
+)
+from netx_api.mcp_remote.topology.layout_ops.graph_util import bbox
+from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import _has_any_footprint_overlap
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _anchor(
+    pos: dict[str, tuple[float, float]],
+    frozen: set[str],
+) -> tuple[float, float]:
+    pts = [pos[n] for n in frozen if n in pos]
+    if len(pts) >= 1:
+        return (
+            sum(p[0] for p in pts) / len(pts),
+            sum(p[1] for p in pts) / len(pts),
+        )
+    if not pos:
+        return (0.0, 0.0)
+    xs = [p[0] for p in pos.values()]
+    ys = [p[1] for p in pos.values()]
+    return ((min(xs) + max(xs)) / 2.0, (min(ys) + max(ys)) / 2.0)
+
+
+def _area(pos: dict[str, tuple[float, float]]) -> float:
+    if len(pos) < 2:
+        return 1.0
+    x0, y0, x1, y1 = bbox(pos)
+    return max((x1 - x0) * (y1 - y0), 1.0)
+
+
+def _clr_hits(pos: dict[str, tuple[float, float]], links, names) -> int:
+    ec = compute_edge_clearance(pos, links, names=names, top_n=1)
+    if ec.get("edge_clearance_skipped"):
+        return 10**9
+    return int(ec.get("edge_clearance_hits") or 0)
+
+
+def compact_bbox(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    frozen_ids: set[str] | None = None,
+    portal_ids: list[str] | None = None,
+    min_scale: float = 0.72,
+    step: float = 0.03,
+    max_clearance_slack: int = 80,
+    outlier_only: bool = True,
+) -> OpResult:
+    """Probe shrink toward portals; keep best gated layout.
+
+    ``outlier_only``: only move nodes farther than ~median radius from the
+    portal anchor — avoids crushing the already-dense eye core into overlaps.
+    """
+    import math
+
+    del params
+    st = state.copy()
+    pos0 = dict(st.positions)
+    names = st.names
+    links = list(st.links)
+    frozen: set[str] = set(frozen_ids or ())
+    for p in portal_ids or []:
+        if p:
+            frozen.add(str(p))
+    if not pos0:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="compact_bbox",
+            note="compact_bbox:empty",
+            params={"error": "empty"},
+        )
+
+    g0 = count_edge_crossings(pos0, links)
+    if _has_any_footprint_overlap(pos0, names):
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="compact_bbox",
+            note="compact_bbox:overlaps_before",
+            params={
+                "error": "overlaps_before",
+                "hint": "Refuse shrink while footprints already overlap.",
+            },
+        )
+    clr0 = _clr_hits(pos0, links, names)
+    area0 = _area(pos0)
+    cx, cy = _anchor(pos0, frozen)
+
+    dists = {
+        n: math.hypot(x - cx, y - cy)
+        for n, (x, y) in pos0.items()
+        if n not in frozen
+    }
+    mobile: set[str]
+    if outlier_only and dists:
+        # Farthest-K only: percentile bands often crush mid-ring nodes into overlaps.
+        k = max(16, min(40, len(dists) // 6))
+        mobile = {
+            n
+            for n, _ in sorted(dists.items(), key=lambda kv: kv[1], reverse=True)[:k]
+        }
+    else:
+        mobile = set(dists.keys())
+
+    best_pos = pos0
+    best_meta: dict[str, Any] = {
+        "scale": 1.0,
+        "crossings": g0,
+        "clearance_hits": clr0,
+        "area": round(area0, 1),
+        "accepted": False,
+    }
+    best_key = (g0, area0, clr0)
+
+    scales: list[float] = []
+    s = 1.0 - float(step)
+    lo = max(0.5, float(min_scale))
+    while s >= lo - 1e-9:
+        scales.append(round(s, 4))
+        s -= float(step)
+
+    for scale in scales:
+        trial: dict[str, tuple[float, float]] = {}
+        for nid, (x, y) in pos0.items():
+            if nid in frozen or nid not in mobile:
+                trial[nid] = (x, y)
+            else:
+                trial[nid] = (
+                    cx + (x - cx) * scale,
+                    cy + (y - cy) * scale,
+                )
+        if _has_any_footprint_overlap(trial, names):
+            continue
+        g1 = count_edge_crossings(trial, links)
+        if g1 > g0:
+            continue
+        clr1 = _clr_hits(trial, links, names)
+        if clr1 > clr0 + max(0, int(max_clearance_slack)):
+            continue
+        area1 = _area(trial)
+        key = (g1, area1, clr1)
+        if key < best_key:
+            best_key = key
+            best_pos = trial
+            best_meta = {
+                "scale": scale,
+                "crossings": g1,
+                "clearance_hits": clr1,
+                "area": round(area1, 1),
+                "accepted": True,
+            }
+
+    moved = {
+        n
+        for n, xy in best_pos.items()
+        if n in pos0
+        and (
+            abs(xy[0] - pos0[n][0]) > 0.05
+            or abs(xy[1] - pos0[n][1]) > 0.05
+        )
+    }
+    st.positions = best_pos
+    st.last_moved = moved
+    meta = {
+        "mode": "compact_bbox",
+        "anchor": [round(cx, 1), round(cy, 1)],
+        "frozen_n": len(frozen),
+        "mobile_n": len(mobile),
+        "outlier_only": bool(outlier_only),
+        "start_crossings": g0,
+        "start_clearance_hits": clr0,
+        "start_area": round(area0, 1),
+        "end_crossings": int(best_meta["crossings"]),
+        "end_clearance_hits": int(best_meta["clearance_hits"]),
+        "end_area": best_meta["area"],
+        "scale": best_meta["scale"],
+        "moved_n": len(moved),
+        "accepted": bool(best_meta["accepted"]),
+    }
+    st.meta["compact_bbox"] = meta
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="compact_bbox",
+        params=meta,
+        note=(
+            f"compact_bbox scale={meta['scale']} "
+            f"Δx={meta['end_crossings'] - g0} "
+            f"clr={clr0}→{meta['end_clearance_hits']} "
+            f"area={meta['start_area']}→{meta['end_area']}"
+        ),
+    )
+
+
+def compact_bbox_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    o = overrides or {}
+    portals = o.get("portal_ids") or o.get("portals") or []
+    if isinstance(portals, str):
+        portals = [portals]
+    frozen = o.get("frozen_ids") or []
+    if isinstance(frozen, str):
+        frozen = [frozen]
+    outlier = o.get("outlier_only")
+    if outlier is None:
+        outlier_only = True
+    else:
+        outlier_only = str(outlier).strip().lower() not in {"0", "false", "no", "off"}
+    return {
+        "portal_ids": [str(x) for x in portals if str(x).strip()],
+        "frozen_ids": {str(x) for x in frozen if str(x).strip()},
+        "min_scale": float(o.get("min_scale") or 0.72),
+        "step": float(o.get("step") or 0.03),
+        "max_clearance_slack": int(o.get("max_clearance_slack") or 80),
+        "outlier_only": outlier_only,
+    }

netx_api/mcp_remote/topology/layout_ops/compose_orbit.py

@@ -0,0 +1,628 @@
+"""Incremental compose: attach staging blocks one-by-one with orbit search.
+
+Unlike spring strip-pack ``compose_views``, this grows the world in Prim portal
+order. After each block is rigidly aligned onto shared portals, exclusive nodes
+are swept on concentric circles / angle grids about the portal pivot (same
+spirit as ``orbit_sweep``) and the candidate with fewest partial crossings is
+kept.
+
+Degrees of freedom by glue:
+- 0 shared: place near hull, then orbit whole block about nearest bridge tip
+- 1 shared: rotate+scale-radius of exclusive members about the portal
+- 2+ shared: chord lock + flip bank + small bulge rotations about chord mid
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    count_edge_crossings,
+    segments_properly_intersect,
+)
+from netx_api.mcp_remote.topology.layout_ops.compose_views import (
+    ComposeBlock,
+    _membership_counts,
+    _portal_grow_order,
+    _rank_shared_pivots,
+    _reflect_about_axis,
+    _rigid_align_to_world,
+    compose_params_from_overrides,
+)
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+_DEFAULT_ANGLE_STEP = 30
+_DEFAULT_RADII = (0.75, 1.0, 1.25, 1.55, 1.95)
+
+
+def _seg_bbox(
+    p: tuple[float, float], q: tuple[float, float]
+) -> tuple[float, float, float, float]:
+    return (min(p[0], q[0]), min(p[1], q[1]), max(p[0], q[0]), max(p[1], q[1]))
+
+
+def _bbox_overlap(
+    a: tuple[float, float, float, float], b: tuple[float, float, float, float]
+) -> bool:
+    return not (a[2] < b[0] or b[2] < a[0] or a[3] < b[1] or b[3] < a[1])
+
+
+def crossings_touching(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    focus: set[str],
+) -> int:
+    """Count crossings where at least one endpoint is in ``focus`` (partial QA)."""
+    if not focus or not links:
+        return 0
+    focus_segs: list[
+        tuple[tuple[float, float], tuple[float, float], tuple[float, float, float, float]]
+    ] = []
+    other_segs: list[
+        tuple[tuple[float, float], tuple[float, float], tuple[float, float, float, float]]
+    ] = []
+    for u, v in links:
+        if u not in pos or v not in pos:
+            continue
+        p, q = pos[u], pos[v]
+        bb = _seg_bbox(p, q)
+        if u in focus or v in focus:
+            focus_segs.append((p, q, bb))
+        else:
+            other_segs.append((p, q, bb))
+    n = 0
+    # focus vs other
+    for p1, p2, b1 in focus_segs:
+        for p3, p4, b2 in other_segs:
+            if not _bbox_overlap(b1, b2):
+                continue
+            if segments_properly_intersect(p1, p2, p3, p4):
+                n += 1
+    # focus vs focus (exclusive internal)
+    for i in range(len(focus_segs)):
+        p1, p2, b1 = focus_segs[i]
+        for j in range(i + 1, len(focus_segs)):
+            p3, p4, b2 = focus_segs[j]
+            if not _bbox_overlap(b1, b2):
+                continue
+            if segments_properly_intersect(p1, p2, p3, p4):
+                n += 1
+    return n
+
+
+def _bridge_len(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    new_ids: set[str],
+    old_ids: set[str],
+) -> float:
+    total = 0.0
+    for a, b in links:
+        if a in new_ids and b in old_ids:
+            if a in pos and b in pos:
+                total += math.hypot(pos[a][0] - pos[b][0], pos[a][1] - pos[b][1])
+        elif b in new_ids and a in old_ids:
+            if a in pos and b in pos:
+                total += math.hypot(pos[a][0] - pos[b][0], pos[a][1] - pos[b][1])
+    return total
+
+
+def _apply_polar(
+    base: dict[str, tuple[float, float]],
+    members: list[str],
+    pivot: tuple[float, float],
+    *,
+    angle: float,
+    radius_scale: float,
+) -> dict[str, tuple[float, float]]:
+    """Rotate members about pivot and scale their distance from pivot."""
+    cos_a, sin_a = math.cos(angle), math.sin(angle)
+    cx, cy = pivot
+    out = dict(base)
+    for n in members:
+        if n not in out:
+            continue
+        x, y = out[n]
+        dx, dy = x - cx, y - cy
+        rx = (dx * cos_a - dy * sin_a) * radius_scale
+        ry = (dx * sin_a + dy * cos_a) * radius_scale
+        out[n] = (cx + rx, cy + ry)
+    return out
+
+
+def _footprint_hits(
+    pos: dict[str, tuple[float, float]],
+    new_ids: set[str],
+    old_ids: set[str],
+    *,
+    min_dist: float = 90.0,
+) -> int:
+    """Cheap exclusive-vs-old center collisions (not full AABB)."""
+    hits = 0
+    md2 = min_dist * min_dist
+    old_pts = [(pos[n][0], pos[n][1]) for n in old_ids if n in pos]
+    for n in new_ids:
+        if n not in pos:
+            continue
+        x, y = pos[n]
+        for ox, oy in old_pts:
+            dx, dy = x - ox, y - oy
+            if dx * dx + dy * dy < md2:
+                hits += 1
+                break
+    return hits
+
+
+def _score_attach(
+    trial: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    new_ids: set[str],
+    old_ids: set[str],
+) -> tuple[int, int, float]:
+    x = crossings_touching(trial, links, new_ids)
+    ov = _footprint_hits(trial, new_ids, old_ids)
+    br = _bridge_len(trial, links, new_ids, old_ids)
+    return (x, ov, br)
+
+
+def _orbit_candidates_one_portal(
+    world: dict[str, tuple[float, float]],
+    aligned: dict[str, tuple[float, float]],
+    portal: str,
+    exclusive: list[str],
+    *,
+    angle_step: int,
+    radii: tuple[float, ...],
+) -> list[dict[str, tuple[float, float]]]:
+    if portal not in world:
+        return [aligned]
+    pivot = world[portal]
+    base = dict(world)
+    for nid, xy in aligned.items():
+        if nid == portal:
+            continue
+        base[nid] = xy
+    # Keep portal frozen.
+    base[portal] = world[portal]
+    cands = [dict(base)]
+    step = max(10, min(90, int(angle_step)))
+    for deg in range(0, 360, step):
+        if deg == 0:
+            angles = [0.0]
+        else:
+            angles = [math.radians(deg)]
+        for ang in angles:
+            for rs in radii:
+                if deg == 0 and abs(rs - 1.0) < 1e-9:
+                    continue
+                trial = _apply_polar(base, exclusive, pivot, angle=ang, radius_scale=rs)
+                trial[portal] = world[portal]
+                cands.append(trial)
+    return cands
+
+
+def _orbit_candidates_two_portal(
+    world: dict[str, tuple[float, float]],
+    aligned: dict[str, tuple[float, float]],
+    portals: list[str],
+    exclusive: list[str],
+    local: dict[str, tuple[float, float]],
+    *,
+    angle_step: int,
+) -> list[dict[str, tuple[float, float]]]:
+    """Chord-locked: base + flip + small bulge twists about chord midpoint."""
+    p0, p1 = portals[0], portals[1]
+    if p0 not in world or p1 not in world:
+        return [aligned]
+    base = dict(world)
+    for nid, xy in aligned.items():
+        if nid in (p0, p1):
+            continue
+        base[nid] = xy
+    base[p0] = world[p0]
+    base[p1] = world[p1]
+    cands = [dict(base)]
+
+    flipped = _reflect_about_axis(base, exclusive + [p0, p1], world[p0], world[p1])
+    flipped[p0] = world[p0]
+    flipped[p1] = world[p1]
+    cands.append(flipped)
+
+    # Small rotations of exclusive bulge about chord midpoint (keep portals).
+    mx = 0.5 * (world[p0][0] + world[p1][0])
+    my = 0.5 * (world[p0][1] + world[p1][1])
+    step = max(15, min(45, int(angle_step)))
+    for src in (base, flipped):
+        for deg in (-step, step, 2 * step, -2 * step):
+            trial = _apply_polar(
+                src, exclusive, (mx, my), angle=math.radians(deg), radius_scale=1.0
+            )
+            trial[p0] = world[p0]
+            trial[p1] = world[p1]
+            cands.append(trial)
+    return cands
+
+
+def _orbit_candidates_orphan(
+    world: dict[str, tuple[float, float]],
+    local: dict[str, tuple[float, float]],
+    *,
+    pad: float,
+    angle_step: int,
+    radii: tuple[float, ...],
+    links: list[tuple[str, str]],
+) -> list[dict[str, tuple[float, float]]]:
+    """No shared portal: park outside bbox then orbit about nearest tip."""
+    if not world:
+        # Seed: place local as-is near origin.
+        xs = [p[0] for p in local.values()]
+        ys = [p[1] for p in local.values()]
+        min_x, min_y = min(xs), min(ys)
+        placed = {n: (x - min_x + 40.0, y - min_y + 40.0) for n, (x, y) in local.items()}
+        return [placed]
+
+    wxs = [p[0] for p in world.values()]
+    wys = [p[1] for p in world.values()]
+    min_x, max_x = min(wxs), max(wxs)
+    min_y, max_y = min(wys), max(wys)
+    # Default park below bbox.
+    lxs = [p[0] for p in local.values()]
+    lys = [p[1] for p in local.values()]
+    lmin_x, lmin_y = min(lxs), min(lys)
+    ox, oy = min_x, max_y + float(pad)
+    placed0 = {
+        n: (ox + (x - lmin_x), oy + (y - lmin_y)) for n, (x, y) in local.items()
+    }
+
+    # Bridge tip: fabric edge from local ids into world.
+    local_ids = set(local)
+    tip = None
+    for a, b in links:
+        if a in local_ids and b in world:
+            tip = world[b]
+            break
+        if b in local_ids and a in world:
+            tip = world[a]
+            break
+    if tip is None:
+        tip = (0.5 * (min_x + max_x), max_y)
+
+    members = list(local.keys())
+    # Always tip-orbit sweep + score (no sticky/default park index).
+    cx0 = sum(placed0[n][0] for n in members) / len(members)
+    cy0 = sum(placed0[n][1] for n in members) / len(members)
+    step = max(15, min(60, int(angle_step)))
+    base_r = max(200.0, float(pad))
+    cands: list[dict[str, tuple[float, float]]] = []
+    for deg in range(0, 360, step):
+        ang = math.radians(deg)
+        for rs in radii:
+            r = base_r * rs
+            tx = tip[0] + r * math.cos(ang)
+            ty = tip[1] + r * math.sin(ang)
+            dx, dy = tx - cx0, ty - cy0
+            trial = dict(world)
+            for n in members:
+                x, y = placed0[n]
+                trial[n] = (x + dx, y + dy)
+            cands.append(trial)
+    return cands or [dict(world) | placed0]
+
+
+def _pick_best(
+    cands: list[dict[str, tuple[float, float]]],
+    links: list[tuple[str, str]],
+    new_ids: set[str],
+    old_ids: set[str],
+) -> tuple[dict[str, tuple[float, float]], tuple[int, int, float], int]:
+    """Rescore every candidate; return the best (no sticky pick)."""
+    if not cands:
+        return {}, (0, 0, 0.0), -1
+    best = cands[0]
+    best_sc = _score_attach(best, links, new_ids, old_ids)
+    best_i = 0
+    for i, c in enumerate(cands[1:], start=1):
+        sc = _score_attach(c, links, new_ids, old_ids)
+        if sc < best_sc:
+            best, best_sc, best_i = c, sc, i
+    return best, best_sc, best_i
+
+
+def _thin_candidates(
+    cands: list[dict[str, tuple[float, float]]],
+    cand_cap: int,
+) -> list[dict[str, tuple[float, float]]]:
+    """Even subsample — do not privilege index 0."""
+    if len(cands) <= cand_cap:
+        return cands
+    step = max(1, (len(cands) + cand_cap - 1) // cand_cap)
+    return cands[::step][:cand_cap]
+
+
+def orbit_pack_blocks(
+    blocks: list[ComposeBlock],
+    *,
+    pad: float = 500.0,
+    links: list[tuple[str, str]] | None = None,
+    fabric_bridges: bool = True,
+    angle_step: int = _DEFAULT_ANGLE_STEP,
+    radii: tuple[float, ...] = _DEFAULT_RADII,
+    cand_cap: int = 80,
+) -> tuple[dict[str, tuple[float, float]], dict[str, Any]]:
+    """Grow world block-by-block with orbit attach search."""
+    if not blocks:
+        return {}, {"slots": 0, "nodes": 0, "mode": "compose_orbit"}
+
+    valid = [b for b in blocks if b.positions]
+    if not valid:
+        return {}, {"slots": 0, "nodes": 0, "mode": "compose_orbit"}
+
+    slot_meta: dict[str, dict[str, float]] = {}
+    for b in valid:
+        xs = [p[0] for p in b.positions.values()]
+        ys = [p[1] for p in b.positions.values()]
+        w = max(max(xs) - min(xs), 1.0) + float(pad)
+        h = max(max(ys) - min(ys), 1.0) + float(pad)
+        slot_meta[b.key] = {"w": w, "h": h, "area": w * h, "n": float(len(b.positions))}
+
+    membership = _membership_counts(valid)
+    link_list = list(links or [])
+    key_order = _portal_grow_order(
+        valid,
+        slot_meta,
+        membership,
+        links=link_list if fabric_bridges else None,
+        fabric_bridges=fabric_bridges,
+    )
+    by_key = {b.key: b for b in valid}
+
+    world: dict[str, tuple[float, float]] = {}
+    owner: dict[str, str] = {}
+    group_nodes: dict[str, list[str]] = {k: [] for k in key_order}
+    group_pivots: dict[str, list[str]] = {k: [] for k in key_order}
+    attach_trace: list[dict[str, Any]] = []
+    merged_via: dict[str, str] = {}
+
+    for key in key_order:
+        block = by_key[key]
+        local = dict(block.positions)
+        shared_raw = [nid for nid in local if nid in world]
+        shared = _rank_shared_pivots(shared_raw, membership)
+        old_ids = set(world)
+        exclusive = [n for n in local if n not in set(shared)]
+
+        if not world:
+            # Seed
+            xs = [p[0] for p in local.values()]
+            ys = [p[1] for p in local.values()]
+            min_x, min_y = min(xs), min(ys)
+            for nid, (x, y) in local.items():
+                world[nid] = (x - min_x + 40.0, y - min_y + 40.0)
+                owner[nid] = key
+                group_nodes[key].append(nid)
+            group_pivots[key] = list(shared[:2]) if shared else []
+            merged_via[key] = "seed"
+            attach_trace.append(
+                {
+                    "key": key,
+                    "via": "seed",
+                    "shared_n": len(shared),
+                    "cands": 1,
+                    "pick": 0,
+                    "score": [0, 0, 0.0],
+                }
+            )
+            continue
+
+        if shared:
+            prefer = None
+            if world:
+                wxs = [p[0] for p in world.values()]
+                wys = [p[1] for p in world.values()]
+                prefer = (0.5 * (min(wxs) + max(wxs)), 0.5 * (min(wys) + max(wys)))
+            aligned = _rigid_align_to_world(
+                local,
+                world,
+                shared,
+                prefer_center=prefer,
+                links=link_list,
+            )
+            if len(shared) >= 2:
+                cands = _orbit_candidates_two_portal(
+                    world,
+                    aligned,
+                    shared[:2],
+                    exclusive,
+                    local,
+                    angle_step=angle_step,
+                )
+                via = "orbit_dual"
+            else:
+                cands = _orbit_candidates_one_portal(
+                    world,
+                    aligned,
+                    shared[0],
+                    exclusive,
+                    angle_step=angle_step,
+                    radii=radii,
+                )
+                via = "orbit_portal"
+            portal_pivots = [n for n in shared if membership.get(n, 1) >= 2][:8]
+            group_pivots[key] = portal_pivots or list(shared[:2])
+        else:
+            cands = _orbit_candidates_orphan(
+                world,
+                local,
+                pad=pad,
+                angle_step=angle_step,
+                radii=radii,
+                links=link_list,
+            )
+            via = "orbit_orphan"
+            group_pivots[key] = []
+
+        cands = _thin_candidates(cands, cand_cap)
+
+        new_ids = set(exclusive) if exclusive else set(local) - old_ids
+        best, best_sc, best_i = _pick_best(cands, link_list, new_ids, old_ids)
+
+        for nid, xy in best.items():
+            if nid not in world:
+                world[nid] = xy
+                owner[nid] = key
+            # portals already in world keep first-owner coords
+            group_nodes[key].append(nid)
+        # Ensure shared listed
+        for nid in shared:
+            if nid not in group_nodes[key]:
+                group_nodes[key].append(nid)
+
+        merged_via[key] = via
+        attach_trace.append(
+            {
+                "key": key,
+                "via": via,
+                "shared_n": len(shared),
+                "shared": shared[:4],
+                "cands": len(cands),
+                "best_i": best_i,
+                "score": [best_sc[0], best_sc[1], round(best_sc[2], 1)],
+                "exclusive_n": len(exclusive),
+            }
+        )
+
+    rigid_groups = [
+        {
+            "key": k,
+            "node_ids": sorted(set(group_nodes[k])),
+            "pivots": sorted(set(group_pivots.get(k) or [])),
+            "via": merged_via.get(k),
+            "soft": True,
+        }
+        for k in key_order
+        if group_nodes.get(k)
+    ]
+    mass_groups = [
+        {
+            **g,
+            "cores": list(g.get("pivots") or []),
+            "soft": True,
+        }
+        for g in rigid_groups
+    ]
+
+    # Final global crossing for meta (cheap enough once).
+    final_x = count_edge_crossings(world, link_list) if link_list else 0
+    meta: dict[str, Any] = {
+        "slots": len(key_order),
+        "nodes": len(world),
+        "pad": float(pad),
+        "merge_shared": True,
+        "mode": "compose_orbit",
+        "order_mode": "portal_grow",
+        "pack_mode": "orbit_attach",
+        "order": key_order,
+        "angle_step": int(angle_step),
+        "radii": list(radii),
+        "attach_trace": attach_trace[:80],
+        "rigid_groups": rigid_groups,
+        "mass_groups": mass_groups,
+        "soft": True,
+        "merged_via": merged_via,
+        "final_crossings": final_x,
+        "tip": (
+            "compose_orbit: Prim portal order + portal align seed; prefer "
+            "mass_merge (core/ring/chain attract) over exclusive rigid polish."
+        ),
+    }
+    return world, meta
+
+
+def compose_orbit_into_state(
+    state: LayoutState,
+    blocks: list[ComposeBlock],
+    params: LayoutParams | None = None,
+    *,
+    pad: float = 500.0,
+    fabric_bridges: bool = True,
+    angle_step: int = _DEFAULT_ANGLE_STEP,
+    radii: tuple[float, ...] | None = None,
+    cand_cap: int = 80,
+) -> OpResult:
+    del params
+    merged, meta = orbit_pack_blocks(
+        blocks,
+        pad=pad,
+        links=list(state.links or []),
+        fabric_bridges=fabric_bridges,
+        angle_step=angle_step,
+        radii=radii or _DEFAULT_RADII,
+        cand_cap=cand_cap,
+    )
+    if not merged:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="compose_orbit",
+            params=meta,
+            note="no_blocks",
+        )
+    out = state.copy()
+    moved: set[str] = set()
+    for nid, xy in merged.items():
+        if nid not in out.positions:
+            out.positions[nid] = xy
+            moved.add(nid)
+            continue
+        if out.positions[nid] != xy:
+            out.positions[nid] = xy
+            moved.add(nid)
+    out.meta = dict(out.meta or {})
+    out.meta["compose_views"] = meta
+    try:
+        from netx_api.mcp_remote.topology.layout_ops.mass_field import (
+            attach_mass_to_compose_meta,
+            build_mass_field,
+        )
+
+        mass = build_mass_field(out, groups=meta.get("mass_groups") or [])
+        out.meta["mass_field"] = mass
+        out.meta["compose_views"] = attach_mass_to_compose_meta(meta, mass)
+    except Exception:
+        pass
+    out.last_moved = moved
+    return OpResult(
+        state=out,
+        moved=moved,
+        op="compose_orbit",
+        params={**meta, "moved_n": len(moved)},
+        note=(
+            f"compose_orbit:slots={meta.get('slots')} nodes={meta.get('nodes')} "
+            f"x={meta.get('final_crossings')}"
+        ),
+    )
+
+
+def compose_orbit_params_from_overrides(
+    overrides: dict[str, Any] | None,
+) -> dict[str, Any]:
+    base = compose_params_from_overrides(overrides)
+    o = overrides or {}
+    try:
+        base["angle_step"] = int(o.get("angle_step") or _DEFAULT_ANGLE_STEP)
+    except (TypeError, ValueError):
+        base["angle_step"] = _DEFAULT_ANGLE_STEP
+    raw_r = o.get("radii")
+    if isinstance(raw_r, (list, tuple)) and raw_r:
+        try:
+            base["radii"] = tuple(float(x) for x in raw_r)
+        except (TypeError, ValueError):
+            base["radii"] = _DEFAULT_RADII
+    else:
+        base["radii"] = _DEFAULT_RADII
+    try:
+        base["cand_cap"] = max(12, min(200, int(o.get("cand_cap") or 80)))
+    except (TypeError, ValueError):
+        base["cand_cap"] = 80
+    return base

netx_api/mcp_remote/topology/layout_ops/compose_views.py

@@ -0,0 +1,796 @@
+"""Compose multiple view layouts via portal-aware packing + shared-node merge.
+
+Dual-unit staging views intentionally overlap on portals. Pack order grows by
+shared membership (Prim-style), then block origins come from a spring layout on
+the portal-weighted block graph so units that share hubs sit near each other.
+Orphan/misc blocks strip-pack below the glued component — cutting long
+cross-unit edges that area-first square packing leaves behind.
+
+When ``merge_shared`` (default), later blocks that share fabric_node_ids are
+rigidly translated/rotated onto the first owner's world coordinates.
+"""
+
+from __future__ import annotations
+
+import math
+from collections import defaultdict
+from dataclasses import dataclass
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+DEFAULT_SLOT_PAD = 600.0
+
+
+@dataclass(frozen=True)
+class ComposeBlock:
+    """One staging canvas / dual-unit view to pack."""
+
+    key: str
+    positions: dict[str, tuple[float, float]]  # fabric_node_id -> local xy
+
+
+def _membership_counts(blocks: list[ComposeBlock]) -> dict[str, int]:
+    counts: dict[str, int] = {}
+    for b in blocks:
+        for nid in b.positions:
+            counts[nid] = counts.get(nid, 0) + 1
+    return counts
+
+
+def _shared_weight(
+    ids_a: set[str],
+    ids_b: set[str],
+    membership: dict[str, int],
+) -> float:
+    """Higher when blocks share portal-like hubs (multi-membership nodes)."""
+    shared = ids_a & ids_b
+    if not shared:
+        return 0.0
+    # membership>=2 are glue points; boost hubs that appear in many units.
+    return float(sum(max(1, membership.get(n, 1)) ** 2 for n in shared))
+
+
+def _fabric_bridge_hits(
+    ids_a: set[str],
+    ids_b: set[str],
+    links: list[tuple[str, str]],
+) -> int:
+    """Count fabric edges with one end in A and the other in B."""
+    if not links or not ids_a or not ids_b:
+        return 0
+    n = 0
+    for a, b in links:
+        if (a in ids_a and b in ids_b) or (a in ids_b and b in ids_a):
+            n += 1
+    return n
+
+
+def _portal_grow_order(
+    blocks: list[ComposeBlock],
+    slot_meta: dict[str, dict[str, float]],
+    membership: dict[str, int],
+    *,
+    links: list[tuple[str, str]] | None = None,
+    fabric_bridges: bool = True,
+) -> list[str]:
+    """Order blocks by shared-portal growth (Prim), not raw area.
+
+    Seed = block with strongest multi-membership glue (ignore orphan-only
+    misc), then attach max shared-weight neighbor. Fabric bridges (optional)
+    also count as glue so long-edge peers attach before pure orphans.
+    """
+    if not blocks:
+        return []
+    id_sets = {b.key: set(b.positions) for b in blocks}
+    remaining = {b.key for b in blocks}
+    link_list = list(links or []) if fabric_bridges else []
+
+    def glue_mass(key: str) -> float:
+        return float(
+            sum(
+                membership.get(n, 1) ** 2
+                for n in id_sets[key]
+                if membership.get(n, 1) >= 2
+            )
+        )
+
+    def fabric_to(ids: set[str], other: set[str]) -> float:
+        if not link_list:
+            return 0.0
+        return float(_fabric_bridge_hits(ids, other, link_list))
+
+    glued = [k for k in remaining if glue_mass(k) > 0]
+    if not glued and link_list:
+        # Prefer a block that participates in any inter-block fabric edge.
+        seeded = [
+            k
+            for k in remaining
+            if any(
+                _fabric_bridge_hits(id_sets[k], id_sets[o], link_list) > 0
+                for o in remaining
+                if o != k
+            )
+        ]
+        glued = seeded
+    if glued:
+        seed = max(glued, key=lambda k: (glue_mass(k), slot_meta[k]["area"], k))
+    else:
+        seed = max(remaining, key=lambda k: (slot_meta[k]["area"], k))
+    order = [seed]
+    remaining.remove(seed)
+    placed_nodes = set(id_sets[seed])
+
+    while remaining:
+        best_key = None
+        best_score: tuple[float, float, float, float, str] | None = None
+        for k in remaining:
+            w = _shared_weight(id_sets[k], placed_nodes, membership)
+            fb = fabric_to(id_sets[k], placed_nodes)
+            rest_nodes: set[str] = set()
+            for o in remaining:
+                if o != k:
+                    rest_nodes |= id_sets[o]
+            w_rest = (
+                _shared_weight(id_sets[k], rest_nodes, membership)
+                if w <= 0 and fb <= 0
+                else 0.0
+            )
+            fb_rest = (
+                fabric_to(id_sets[k], rest_nodes) if w <= 0 and fb <= 0 else 0.0
+            )
+            # Orphans (no portal/fabric glue): larger → later.
+            if w <= 0 and fb <= 0 and w_rest <= 0 and fb_rest <= 0:
+                area_term = -slot_meta[k]["area"]
+            else:
+                area_term = slot_meta[k]["area"]
+            score = (w + fb * 4.0, w_rest + fb_rest * 2.0, area_term, fb, k)
+            if best_score is None or score > best_score:
+                best_score = score
+                best_key = k
+        assert best_key is not None
+        order.append(best_key)
+        remaining.remove(best_key)
+        placed_nodes |= id_sets[best_key]
+    return order
+
+
+def _rank_shared_pivots(
+    shared: list[str], membership: dict[str, int]
+) -> list[str]:
+    """Prefer multi-unit hubs as rigid-align pivots (not corridor alpha order)."""
+    return sorted(shared, key=lambda n: (-membership.get(n, 1), n))
+
+
+def _block_graph(
+    blocks: list[ComposeBlock],
+    membership: dict[str, int],
+    *,
+    links: list[tuple[str, str]] | None = None,
+    fabric_bridges: bool = True,
+    bridge_boost: float = 8.0,
+) -> dict[str, dict[str, float]]:
+    """Undirected weighted graph: portal glue + optional fabric-bridge boost.
+
+    ``bridge_boost`` is MCP-tunable: each inter-block fabric edge adds this
+    much weight so long-spoke peers sit near each other in the spring pack.
+    """
+    id_sets = {b.key: set(b.positions) for b in blocks}
+    keys = [b.key for b in blocks]
+    adj: dict[str, dict[str, float]] = {k: {} for k in keys}
+    boost = max(0.0, float(bridge_boost))
+    link_list = list(links or []) if fabric_bridges and boost > 0 else []
+    for i, ka in enumerate(keys):
+        for kb in keys[i + 1 :]:
+            w = _shared_weight(id_sets[ka], id_sets[kb], membership)
+            if link_list:
+                w += boost * float(
+                    _fabric_bridge_hits(id_sets[ka], id_sets[kb], link_list)
+                )
+            if w <= 0:
+                continue
+            adj[ka][kb] = w
+            adj[kb][ka] = w
+    return adj
+
+
+def _reflect_about_axis(
+    pos: dict[str, tuple[float, float]],
+    members: list[str],
+    p0: tuple[float, float],
+    p1: tuple[float, float],
+) -> dict[str, tuple[float, float]]:
+    ax, ay = p0
+    bx, by = p1
+    dx, dy = bx - ax, by - ay
+    L2 = dx * dx + dy * dy
+    if L2 < 1e-12:
+        return dict(pos)
+    out = dict(pos)
+    for n in members:
+        if n not in out:
+            continue
+        x, y = out[n]
+        t = ((x - ax) * dx + (y - ay) * dy) / L2
+        px, py = ax + t * dx, ay + t * dy
+        out[n] = (2 * px - x, 2 * py - y)
+    return out
+
+
+def _external_bridge_cost(
+    placed: dict[str, tuple[float, float]],
+    world: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    new_ids: set[str],
+) -> float:
+    """Sum of squared lengths for edges that leave this block into ``world``."""
+    if not links or not new_ids:
+        return 0.0
+    cost = 0.0
+    for a, b in links:
+        a_new = a in new_ids
+        b_new = b in new_ids
+        if a_new == b_new:
+            continue
+        pa = placed.get(a) if a_new else world.get(a)
+        pb = placed.get(b) if b_new else world.get(b)
+        if pa is None or pb is None:
+            continue
+        dx = pa[0] - pb[0]
+        dy = pa[1] - pb[1]
+        cost += dx * dx + dy * dy
+    return cost
+
+
+def _portal_centroid_origins(
+    key_order: list[str],
+    slot_meta: dict[str, dict[str, float]],
+    graph: dict[str, dict[str, float]],
+    *,
+    pad: float,
+    ideal_scale: float = 0.55,
+    spring_iters: int = 80,
+) -> dict[str, tuple[float, float]]:
+    """Spring-embed block centroids on the portal/fabric graph; orphans strip below.
+
+    Origins are used for strip_pack seeds (and as a soft hint before merge).
+    ``ideal_scale`` / ``spring_iters`` are MCP-tunable (smaller scale → denser).
+    """
+    if not key_order:
+        return {}
+    glued = [k for k in key_order if graph.get(k)]
+    orphans = [k for k in key_order if k not in set(glued)]
+
+    origins: dict[str, tuple[float, float]] = {}
+    if not glued:
+        # Fall back to square strip for everything.
+        n = len(key_order)
+        cols = max(1, int(math.ceil(math.sqrt(n))))
+        col_w = [0.0] * cols
+        row_h: dict[int, float] = defaultdict(float)
+        for i, key in enumerate(key_order):
+            c, r = i % cols, i // cols
+            col_w[c] = max(col_w[c], slot_meta[key]["w"])
+            row_h[r] = max(row_h[r], slot_meta[key]["h"])
+        cox = [0.0]
+        for c in range(cols - 1):
+            cox.append(cox[-1] + col_w[c])
+        roy = {0: 0.0}
+        for r in range(1, (n // cols) + 2):
+            roy[r] = roy.get(r - 1, 0.0) + row_h.get(r - 1, 0.0)
+        for i, key in enumerate(key_order):
+            c, r = i % cols, i // cols
+            origins[key] = (cox[c], roy[r])
+        return origins
+
+    scale = max(0.25, min(float(ideal_scale), 1.2))
+    iters = max(20, min(int(spring_iters), 240))
+
+    # Ideal edge length ~ mean diagonal of the two slots.
+    def ideal(a: str, b: str) -> float:
+        da = math.hypot(slot_meta[a]["w"], slot_meta[a]["h"])
+        db = math.hypot(slot_meta[b]["w"], slot_meta[b]["h"])
+        return scale * (da + db) + float(pad)
+
+    # Init on a circle scaled by total size.
+    n_g = len(glued)
+    span = sum(math.hypot(slot_meta[k]["w"], slot_meta[k]["h"]) for k in glued) / max(
+        n_g, 1
+    )
+    radius = max(span * 0.7 * math.sqrt(n_g), 400.0)
+    pos: dict[str, list[float]] = {}
+    for i, key in enumerate(glued):
+        ang = 2.0 * math.pi * i / n_g
+        pos[key] = [radius * math.cos(ang), radius * math.sin(ang)]
+
+    # Lightweight spring / Fruchterman-style iterations (no numpy).
+    max_w = max((w for nbrs in graph.values() for w in nbrs.values()), default=1.0)
+    for _ in range(iters):
+        disp = {k: [0.0, 0.0] for k in glued}
+        # Attractive along portal / fabric edges.
+        for a in glued:
+            for b, w in graph.get(a, {}).items():
+                if b not in pos or a >= b:
+                    continue
+                dx = pos[b][0] - pos[a][0]
+                dy = pos[b][1] - pos[a][1]
+                dist = math.hypot(dx, dy) or 1e-6
+                L = ideal(a, b)
+                # Stronger weight → stronger pull toward ideal length.
+                force = (dist - L) * (0.15 + 0.35 * (w / max_w))
+                ux, uy = dx / dist, dy / dist
+                disp[a][0] += ux * force
+                disp[a][1] += uy * force
+                disp[b][0] -= ux * force
+                disp[b][1] -= uy * force
+        # Mild repulsion so slots do not stack.
+        for i, a in enumerate(glued):
+            for b in glued[i + 1 :]:
+                dx = pos[b][0] - pos[a][0]
+                dy = pos[b][1] - pos[a][1]
+                dist = math.hypot(dx, dy) or 1e-6
+                min_d = 0.45 * (
+                    math.hypot(slot_meta[a]["w"], slot_meta[a]["h"])
+                    + math.hypot(slot_meta[b]["w"], slot_meta[b]["h"])
+                )
+                if dist >= min_d:
+                    continue
+                push = (min_d - dist) * 0.35
+                ux, uy = dx / dist, dy / dist
+                disp[a][0] -= ux * push
+                disp[a][1] -= uy * push
+                disp[b][0] += ux * push
+                disp[b][1] += uy * push
+        for k in glued:
+            pos[k][0] += max(-800.0, min(800.0, disp[k][0]))
+            pos[k][1] += max(-800.0, min(800.0, disp[k][1]))
+
+    # Convert centers → top-left origins (slot min corner).
+    for key in glued:
+        cx, cy = pos[key]
+        origins[key] = (
+            cx - 0.5 * slot_meta[key]["w"],
+            cy - 0.5 * slot_meta[key]["h"],
+        )
+
+    # Orphans: horizontal strip under the glued bbox.
+    if orphans:
+        xs = [origins[k][0] for k in glued]
+        ys = [origins[k][1] for k in glued]
+        ys2 = [origins[k][1] + slot_meta[k]["h"] for k in glued]
+        base_y = max(ys2) + float(pad)
+        base_x = min(xs)
+        x_cursor = base_x
+        for key in orphans:
+            origins[key] = (x_cursor, base_y)
+            x_cursor += slot_meta[key]["w"] + 0.25 * float(pad)
+
+    # Normalize so min corner is near origin.
+    min_x = min(o[0] for o in origins.values())
+    min_y = min(o[1] for o in origins.values())
+    return {k: (x - min_x + 40.0, y - min_y + 40.0) for k, (x, y) in origins.items()}
+
+
+def _rigid_align_to_world(
+    local: dict[str, tuple[float, float]],
+    world: dict[str, tuple[float, float]],
+    shared: list[str],
+    *,
+    prefer_center: tuple[float, float] | None = None,
+    links: list[tuple[str, str]] | None = None,
+) -> dict[str, tuple[float, float]]:
+    """Translate (+ rotate about first shared) so shared nodes match world.
+
+    With two+ shared pivots, orientation follows the portal chord; also try the
+    flip across that chord and keep the side with fewer crossings on the
+    partial world (then shorter external bridges / closer to ``prefer_center``).
+    With a single pivot, rotate so the block centroid aims at ``prefer_center``.
+    """
+    if not shared:
+        return dict(local)
+    p0 = shared[0]
+    lx0, ly0 = local[p0]
+    wx0, wy0 = world[p0]
+    angle = 0.0
+    if len(shared) >= 2:
+        p1 = shared[1]
+        ldx, ldy = local[p1][0] - lx0, local[p1][1] - ly0
+        wdx, wdy = world[p1][0] - wx0, world[p1][1] - wy0
+        if (ldx * ldx + ldy * ldy) > 1e-12 and (wdx * wdx + wdy * wdy) > 1e-12:
+            angle = math.atan2(wdy, wdx) - math.atan2(ldy, ldx)
+    elif prefer_center is not None and len(local) >= 2:
+        # Local centroid relative to portal.
+        cx = sum(p[0] for p in local.values()) / len(local)
+        cy = sum(p[1] for p in local.values()) / len(local)
+        ldx, ldy = cx - lx0, cy - ly0
+        tdx = prefer_center[0] - wx0
+        tdy = prefer_center[1] - wy0
+        if (ldx * ldx + ldy * ldy) > 1e-8 and (tdx * tdx + tdy * tdy) > 1e-8:
+            angle = math.atan2(tdy, tdx) - math.atan2(ldy, ldx)
+    cos_a, sin_a = math.cos(angle), math.sin(angle)
+    out: dict[str, tuple[float, float]] = {}
+    for nid, (lx, ly) in local.items():
+        dx, dy = lx - lx0, ly - ly0
+        rx = dx * cos_a - dy * sin_a
+        ry = dx * sin_a + dy * cos_a
+        out[nid] = (wx0 + rx, wy0 + ry)
+
+    if len(shared) < 2:
+        return out
+
+    # Dual-portal flip: same chord, opposite bank.
+    shared_set = set(shared)
+    new_ids = {n for n in local if n not in shared_set}
+    link_list = list(links or [])
+
+    def _score(cand: dict[str, tuple[float, float]]) -> tuple[int, float, float]:
+        trial = dict(world)
+        for nid, xy in cand.items():
+            if nid in shared_set:
+                continue
+            trial[nid] = xy
+        xcount = (
+            count_edge_crossings(trial, link_list) if link_list else 0
+        )
+        bridge = _external_bridge_cost(cand, world, link_list, new_ids)
+        if prefer_center is None:
+            aim = 0.0
+        else:
+            excl = [cand[n] for n in new_ids if n in cand]
+            if not excl:
+                aim = 0.0
+            else:
+                cx = sum(p[0] for p in excl) / len(excl)
+                cy = sum(p[1] for p in excl) / len(excl)
+                aim = (cx - prefer_center[0]) ** 2 + (cy - prefer_center[1]) ** 2
+        return (int(xcount), bridge, aim)
+
+    flipped = _reflect_about_axis(
+        out, list(local.keys()), world[shared[0]], world[shared[1]]
+    )
+    for p in shared:
+        if p in world:
+            flipped[p] = world[p]
+    if _score(flipped) < _score(out):
+        return flipped
+    return out
+
+
+def strip_pack_blocks(
+    blocks: list[ComposeBlock],
+    *,
+    pad: float = DEFAULT_SLOT_PAD,
+    merge_shared: bool = True,
+    links: list[tuple[str, str]] | None = None,
+    fabric_bridges: bool = True,
+    bridge_boost: float = 8.0,
+    ideal_scale: float = 0.55,
+    spring_iters: int = 80,
+) -> tuple[dict[str, tuple[float, float]], dict[str, Any]]:
+    """Pack blocks; return merged positions + meta.
+
+    With ``merge_shared`` (default): later blocks that share node ids with
+    already-placed nodes are rigidly aligned (portal merge). First owner wins
+    for shared coordinates. Blocks without shared anchors use strip-pack slots.
+
+    Pack order prefers portal connectivity growth so dual units that share
+    hubs stay glued; large orphan/misc blocks are placed last. Optional
+    ``links`` + ``fabric_bridges`` add fabric-bridge weight to the spring and
+    guide dual-portal flip by partial-graph crossings (then bridge length).
+
+    MCP knobs: ``pad``, ``bridge_boost``, ``ideal_scale``, ``spring_iters``,
+    ``fabric_bridges``.
+    """
+    if not blocks:
+        return {}, {"slots": 0, "nodes": 0, "pad": pad, "merge_shared": merge_shared}
+
+    slot_meta: dict[str, dict[str, float]] = {}
+    valid: list[ComposeBlock] = []
+    for b in blocks:
+        if not b.positions:
+            continue
+        xs = [p[0] for p in b.positions.values()]
+        ys = [p[1] for p in b.positions.values()]
+        min_x, max_x = min(xs), max(xs)
+        min_y, max_y = min(ys), max(ys)
+        w = max(max_x - min_x, 1.0) + float(pad)
+        h = max(max_y - min_y, 1.0) + float(pad)
+        slot_meta[b.key] = {
+            "min_x": min_x,
+            "min_y": min_y,
+            "w": w,
+            "h": h,
+            "area": w * h,
+            "n": float(len(b.positions)),
+        }
+        valid.append(b)
+
+    if not valid:
+        return {}, {"slots": 0, "nodes": 0, "pad": pad, "merge_shared": merge_shared}
+
+    membership = _membership_counts(valid)
+    link_list = list(links or [])
+    do_fabric = bool(fabric_bridges)
+    boost = max(0.0, float(bridge_boost))
+    if merge_shared:
+        key_order = _portal_grow_order(
+            valid,
+            slot_meta,
+            membership,
+            links=link_list,
+            fabric_bridges=do_fabric,
+        )
+        order_mode = "portal_grow"
+        graph = _block_graph(
+            valid,
+            membership,
+            links=link_list,
+            fabric_bridges=do_fabric,
+            bridge_boost=boost,
+        )
+        origins = _portal_centroid_origins(
+            key_order,
+            slot_meta,
+            graph,
+            pad=float(pad),
+            ideal_scale=float(ideal_scale),
+            spring_iters=int(spring_iters),
+        )
+        pack_mode = "portal_centroid"
+        cols = 0
+    else:
+        key_order = [
+            b.key
+            for b in sorted(valid, key=lambda b: (-slot_meta[b.key]["area"], b.key))
+        ]
+        order_mode = "area"
+        pack_mode = "strip"
+        n = max(1, len(key_order))
+        cols = max(1, int(math.ceil(math.sqrt(n))))
+        col_widths: list[float] = [0.0] * cols
+        row_heights: dict[int, float] = defaultdict(float)
+        for i, key in enumerate(key_order):
+            c, r = i % cols, i // cols
+            meta = slot_meta[key]
+            col_widths[c] = max(col_widths[c], meta["w"])
+            row_heights[r] = max(row_heights[r], meta["h"])
+
+        col_origin = [0.0]
+        for c in range(cols - 1):
+            col_origin.append(col_origin[-1] + col_widths[c])
+        row_origin: dict[int, float] = {0: 0.0}
+        max_row = max((i // cols for i in range(n)), default=0)
+        for r in range(1, max_row + 2):
+            row_origin[r] = row_origin.get(r - 1, 0.0) + row_heights.get(r - 1, 0.0)
+
+        origins = {}
+        for i, key in enumerate(key_order):
+            c, r = i % cols, i // cols
+            origins[key] = (col_origin[c], row_origin[r])
+
+    by_key = {b.key: b for b in valid}
+    out: dict[str, tuple[float, float]] = {}
+    owner: dict[str, str] = {}
+    merged_via: dict[str, str] = {}
+    align_meta: list[dict[str, Any]] = []
+    # Per-block members as placed (whole sub-region = rigid body).
+    group_nodes: dict[str, list[str]] = {k: [] for k in key_order}
+    group_pivots: dict[str, list[str]] = {k: [] for k in key_order}
+
+    for key in key_order:
+        block = by_key[key]
+        meta = slot_meta[key]
+        shared_raw = [nid for nid in block.positions if nid in out]
+        shared = _rank_shared_pivots(shared_raw, membership)
+        if merge_shared and shared:
+            ox, oy = origins[key]
+            prefer = (
+                ox + 0.5 * meta["w"],
+                oy + 0.5 * meta["h"],
+            )
+            aligned = _rigid_align_to_world(
+                block.positions,
+                out,
+                shared,
+                prefer_center=prefer,
+                links=link_list,
+            )
+            placed_new = 0
+            for nid, xy in aligned.items():
+                group_nodes[key].append(nid)
+                if nid in out:
+                    continue
+                out[nid] = xy
+                owner[nid] = key
+                placed_new += 1
+            # Freeze pivots = high-membership shared (true portals), not every
+            # overlapping corridor id — soft polish needs corridors movable.
+            portal_pivots = [n for n in shared if membership.get(n, 1) >= 2][:8]
+            group_pivots[key] = portal_pivots or list(shared[:2])
+            merged_via[key] = "rigid_shared"
+            align_meta.append(
+                {
+                    "key": key,
+                    "shared": shared[:6],
+                    "shared_n": len(shared),
+                    "placed_new": placed_new,
+                    "pivot_n": len(group_pivots[key]),
+                }
+            )
+            continue
+
+        ox, oy = origins[key]
+        for nid, (lx, ly) in block.positions.items():
+            group_nodes[key].append(nid)
+            if nid in out:
+                group_pivots[key].append(nid)
+                continue
+            out[nid] = (ox + (lx - meta["min_x"]), oy + (ly - meta["min_y"]))
+            owner[nid] = key
+        merged_via[key] = "strip_pack"
+
+    rigid_groups = [
+        {
+            "key": k,
+            "node_ids": sorted(set(group_nodes[k])),
+            "pivots": sorted(set(group_pivots.get(k) or [])),
+            "via": merged_via.get(k),
+            "soft": True,
+        }
+        for k in key_order
+        if group_nodes.get(k)
+    ]
+    mass_groups = [
+        {**g, "cores": list(g.get("pivots") or []), "soft": True}
+        for g in rigid_groups
+    ]
+
+    return out, {
+        "slots": len(key_order),
+        "nodes": len(out),
+        "pad": float(pad),
+        "merge_shared": bool(merge_shared),
+        "fabric_bridges": do_fabric if merge_shared else False,
+        "bridge_boost": boost if merge_shared else 0.0,
+        "ideal_scale": float(ideal_scale) if merge_shared else None,
+        "spring_iters": int(spring_iters) if merge_shared else None,
+        "order_mode": order_mode,
+        "pack_mode": pack_mode,
+        "cols": cols,
+        "order": key_order,
+        "origins": {k: [round(v[0], 2), round(v[1], 2)] for k, v in origins.items()},
+        "owner": {k: owner[k] for k in list(owner)[:40]},
+        "merged_via": merged_via,
+        "alignments": align_meta[:20],
+        "rigid_groups": rigid_groups,
+        "mass_groups": mass_groups,
+        "soft": True,
+        "tip": (
+            "Compose: portal+fabric spring seed; prefer mass_merge "
+            "(core/ring/chain attract) then polish_crossings."
+        ),
+        "slot_meta": {
+            k: {
+                "w": round(v["w"], 1),
+                "h": round(v["h"], 1),
+                "area": round(v["area"], 1),
+                "n": int(v["n"]),
+            }
+            for k, v in slot_meta.items()
+        },
+    }
+
+
+def blocks_from_position_maps(
+    maps: list[tuple[str, dict[str, tuple[float, float]]]],
+) -> list[ComposeBlock]:
+    return [ComposeBlock(key=k, positions=dict(pos)) for k, pos in maps if pos]
+
+
+def compose_into_state(
+    state: LayoutState,
+    blocks: list[ComposeBlock],
+    params: LayoutParams | None = None,
+    *,
+    pad: float = DEFAULT_SLOT_PAD,
+    merge_shared: bool = True,
+    fabric_bridges: bool = True,
+    bridge_boost: float = 8.0,
+    ideal_scale: float = 0.55,
+    spring_iters: int = 80,
+) -> OpResult:
+    """Write strip-packed / merge-aligned coords onto ``state``."""
+    del params
+    merged, meta = strip_pack_blocks(
+        blocks,
+        pad=pad,
+        merge_shared=merge_shared,
+        links=list(state.links or []),
+        fabric_bridges=fabric_bridges,
+        bridge_boost=bridge_boost,
+        ideal_scale=ideal_scale,
+        spring_iters=spring_iters,
+    )
+    if not merged:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="compose_views",
+            params=meta,
+            note="no_blocks",
+        )
+    out = state.copy()
+    moved: set[str] = set()
+    for nid, xy in merged.items():
+        if nid not in out.positions:
+            out.positions[nid] = xy
+            moved.add(nid)
+            continue
+        if out.positions[nid] != xy:
+            out.positions[nid] = xy
+            moved.add(nid)
+    out.meta = dict(out.meta or {})
+    out.meta["compose_views"] = meta
+    try:
+        from netx_api.mcp_remote.topology.layout_ops.mass_field import (
+            attach_mass_to_compose_meta,
+            build_mass_field,
+        )
+
+        mass = build_mass_field(out, groups=meta.get("mass_groups") or [])
+        out.meta["mass_field"] = mass
+        out.meta["compose_views"] = attach_mass_to_compose_meta(meta, mass)
+    except Exception:
+        pass
+    out.last_moved = moved
+    return OpResult(
+        state=out,
+        moved=moved,
+        op="compose_views",
+        params={**meta, "moved_n": len(moved)},
+        note=(
+            f"compose_views:slots={meta.get('slots')} nodes={meta.get('nodes')} "
+            f"merge_shared={meta.get('merge_shared')} order={meta.get('order_mode')}"
+        ),
+    )
+
+
+def compose_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    out: dict[str, Any] = {}
+    if not overrides:
+        return out
+    if overrides.get("pad") is not None:
+        try:
+            out["pad"] = float(overrides["pad"])
+        except (TypeError, ValueError):
+            pass
+    for flag in ("merge_shared", "fabric_bridges"):
+        if flag not in overrides:
+            continue
+        v = overrides[flag]
+        if isinstance(v, bool):
+            out[flag] = v
+        else:
+            out[flag] = str(v).strip().lower() in {"1", "true", "yes", "on"}
+    if overrides.get("bridge_boost") is not None:
+        try:
+            out["bridge_boost"] = max(0.0, float(overrides["bridge_boost"]))
+        except (TypeError, ValueError):
+            pass
+    if overrides.get("ideal_scale") is not None:
+        try:
+            out["ideal_scale"] = max(0.25, min(1.2, float(overrides["ideal_scale"])))
+        except (TypeError, ValueError):
+            pass
+    if overrides.get("spring_iters") is not None:
+        try:
+            out["spring_iters"] = max(20, min(240, int(overrides["spring_iters"])))
+        except (TypeError, ValueError):
+            pass
+    src = overrides.get("source_view_ids")
+    if isinstance(src, list):
+        out["source_view_ids"] = [str(x).strip() for x in src if str(x).strip()]
+    elif isinstance(src, str) and src.strip():
+        out["source_view_ids"] = [s.strip() for s in src.split(",") if s.strip()]
+    return out

netx_api/mcp_remote/topology/layout_ops/densify_sweep.py

@@ -0,0 +1,781 @@
+"""Inward densify sweep: top-3 single-node pulls + corridor_cap scan.
+
+Agent workflow: preview → pick rank 1..3 → apply; round=true auto-applies #1
+when global crossings do not rise and stretch falls; phase=corridor / corridor=true
+sweeps shrink_long_corridors params for util gain.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    count_edge_crossings,
+    crossings_involving_node,
+)
+from netx_api.mcp_remote.topology.layout_ops.hotspots import overlapping_nodes
+from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import (
+    _diversify_top,
+    _eval_candidate,
+    _incident_stretch,
+)
+from netx_api.mcp_remote.topology.layout_ops.rigid_units import (
+    frozen_ids_for_protect,
+    groups_from_compose_meta,
+    shrink_long_corridors,
+)
+from netx_api.mcp_remote.topology.layout_ops.score import score_state
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+_MAX_PULL = 900.0
+_MAX_PULL_CAP = 2200.0
+_PULL_FRACS = (0.12, 0.22, 0.35, 0.5, 0.7)
+_ANGLE_JITTERS = (-30.0, -15.0, 0.0, 15.0, 30.0)
+_DEFAULT_CORRIDOR_CAPS = (1200.0, 1600.0, 2200.0, 3200.0)
+_DEFAULT_PULLS = (0.35, 0.5, 0.65)
+
+
+def _protect_is_off(protect_rigid: bool | str) -> bool:
+    return protect_rigid in (False, "false", "off", "none", "0")
+
+
+def _resolve_frozen(
+    st: LayoutState,
+    protect_rigid: bool | str,
+    frozen_ids: set[str] | None,
+) -> set[str]:
+    """Portal/rigid freeze only when protect is on; off ignores portal_ids inject."""
+    if _protect_is_off(protect_rigid):
+        return set()
+    frozen: set[str] = set(frozen_ids or ())
+    if not frozen:
+        frozen = frozen_ids_for_protect(st, protect_rigid)
+    return frozen
+
+
+def _groups_from_state(state: LayoutState) -> list[dict[str, Any]]:
+    return groups_from_compose_meta((state.meta or {}).get("compose_views"))
+
+
+def _membership_maps(
+    groups: list[dict[str, Any]],
+    valid: set[str],
+) -> tuple[dict[str, list[str]], set[str], dict[str, tuple[float, float]]]:
+    """Return node→group_keys, shared portals, group_key→exclusive centroid xy."""
+    counts: dict[str, int] = {}
+    g_members: dict[str, list[str]] = {}
+    for g in groups:
+        key = str(g.get("key") or "")
+        members = [str(n) for n in (g.get("node_ids") or []) if str(n) in valid]
+        if len(members) < 2:
+            continue
+        g_members[key] = members
+        for n in members:
+            counts[n] = counts.get(n, 0) + 1
+    shared = {n for n, c in counts.items() if c > 1}
+    # Need positions later — centroids filled by caller.
+    return (
+        {n: [k for k, ms in g_members.items() if n in ms] for n in counts},
+        shared,
+        {},
+    )
+
+
+def _exclusive_centroids(
+    groups: list[dict[str, Any]],
+    pos: dict[str, tuple[float, float]],
+    shared: set[str],
+) -> dict[str, tuple[float, float]]:
+    out: dict[str, tuple[float, float]] = {}
+    for g in groups:
+        key = str(g.get("key") or "")
+        members = [str(n) for n in (g.get("node_ids") or []) if str(n) in pos]
+        exclusive = [n for n in members if n not in shared]
+        use = exclusive if len(exclusive) >= 1 else members
+        if not use:
+            continue
+        cx = sum(pos[n][0] for n in use) / len(use)
+        cy = sum(pos[n][1] for n in use) / len(use)
+        out[key] = (cx, cy)
+    return out
+
+
+def _anchor_for_node(
+    nid: str,
+    pos: dict[str, tuple[float, float]],
+    adj: dict[str, set[str]],
+    *,
+    node_groups: dict[str, list[str]],
+    centroids: dict[str, tuple[float, float]],
+    global_cx: float,
+    global_cy: float,
+) -> tuple[float, float, str]:
+    nbs = [pos[v] for v in adj.get(nid, ()) if v in pos]
+    if nbs:
+        ax = sum(p[0] for p in nbs) / len(nbs)
+        ay = sum(p[1] for p in nbs) / len(nbs)
+        return ax, ay, "neighbors"
+    for gk in node_groups.get(nid, ()):
+        if gk in centroids:
+            cx, cy = centroids[gk]
+            return cx, cy, "unit"
+    return global_cx, global_cy, "global"
+
+
+def _inward_samples(
+    x0: float,
+    y0: float,
+    ax: float,
+    ay: float,
+    *,
+    max_pull: float,
+) -> list[tuple[float, float, float, float]]:
+    """Samples on ray toward anchor; never past the anchor; capped by max_pull."""
+    dx, dy = ax - x0, ay - y0
+    dist = math.hypot(dx, dy)
+    if dist < 8.0:
+        return []
+    ux, uy = dx / dist, dy / dist
+    px, py = -uy, ux
+    out: list[tuple[float, float, float, float]] = []
+    base_ang = math.degrees(math.atan2(uy, ux)) % 360.0
+    for t in _PULL_FRACS:
+        pull = min(dist * t, max_pull)
+        if pull < 12.0:
+            continue
+        # Stay short of the anchor (leave 4px slack).
+        pull = min(pull, dist - 4.0)
+        if pull < 12.0:
+            continue
+        for dang in _ANGLE_JITTERS:
+            if dang == 0.0:
+                nx = x0 + ux * pull
+                ny = y0 + uy * pull
+                ang = base_ang
+            else:
+                rad = math.radians(base_ang + dang)
+                c, s = math.cos(rad), math.sin(rad)
+                nx = x0 + c * pull
+                ny = y0 + s * pull
+                # Reject if farther from anchor than start.
+                if math.hypot(nx - ax, ny - ay) >= dist - 1e-6:
+                    continue
+                ang = (base_ang + dang) % 360.0
+            r = math.hypot(nx - x0, ny - y0)
+            if r < 12.0 or r > max_pull + 1:
+                continue
+            out.append((nx, ny, r, ang))
+    # Mild perpendicular nudges at mid pull (still closer to anchor).
+    mid = min(dist * 0.35, max_pull)
+    if mid >= 20.0:
+        for sign in (-1.0, 1.0):
+            nx = x0 + ux * mid + px * sign * mid * 0.25
+            ny = y0 + uy * mid + py * sign * mid * 0.25
+            if math.hypot(nx - ax, ny - ay) >= dist - 1e-6:
+                continue
+            r = math.hypot(nx - x0, ny - y0)
+            if 12.0 <= r <= max_pull + 1:
+                ang = math.degrees(math.atan2(ny - y0, nx - x0)) % 360.0
+                out.append((nx, ny, r, ang))
+    return out
+
+
+def _densify_score_key(c: dict[str, Any]) -> tuple:
+    # Crossings first; then shorter incident stretch; prefer larger inward pull.
+    return (
+        int(c["crossings"]["global"]),
+        int(c["crossings"]["incident"]),
+        float(c.get("stretch") or 1.0),
+        -float(c.get("r") or 0.0),
+    )
+
+
+def densify_sweep_node(
+    state: LayoutState,
+    node_id: str,
+    *,
+    params: LayoutParams | None = None,
+    max_pull: float | None = None,
+    nn_floor: float = 60.0,
+    min_angle_sep: float = 35.0,
+    cand_cap: int = 180,
+    protect_rigid: bool | str = "off",
+    frozen_ids: set[str] | None = None,
+    top_k: int = 3,
+    groups: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    """Inward pull candidates for one node; return diversified top-k.
+
+    Default ``protect_rigid=off`` so multi-round densify may move portals;
+    other layout actions keep portal freeze. Pass ``protect_rigid=portals``
+    to opt back into rigid protection.
+    """
+    params = params or LayoutParams()
+    st = state
+    nid = str(node_id).strip()
+    pos = dict(st.positions)
+    names = dict(st.names)
+    links = list(st.links)
+    adj = {n: set(st.adj.get(n, ())) for n in pos}
+    if nid not in pos:
+        return {"ok": False, "error": "node_not_on_view", "node_id": nid}
+
+    frozen = _resolve_frozen(st, protect_rigid, frozen_ids)
+    if nid in frozen:
+        return {
+            "ok": False,
+            "error": "frozen",
+            "node_id": nid,
+            "hint": "portal/rigid frozen; protect_rigid=off or pick a corridor node",
+        }
+
+    pull = float(max_pull if max_pull is not None else _MAX_PULL)
+    pull = max(80.0, min(pull, _MAX_PULL_CAP))
+    nbs_cap = pull * 2.5
+    target_nn = float(getattr(params, "target_nn", 155.0) or 155.0)
+
+    groups = groups if groups is not None else _groups_from_state(st)
+    valid = set(pos)
+    node_groups, shared, _ = _membership_maps(groups, valid)
+    # With groups + protect on: shared portals stay frozen.
+    if groups and nid in shared and not _protect_is_off(protect_rigid):
+        return {
+            "ok": False,
+            "error": "shared_portal",
+            "node_id": nid,
+            "hint": "shared portals frozen; densify defaults protect_rigid=off",
+        }
+
+    centroids = _exclusive_centroids(groups, pos, shared)
+    gcx = sum(p[0] for p in pos.values()) / max(len(pos), 1)
+    gcy = sum(p[1] for p in pos.values()) / max(len(pos), 1)
+    ax, ay, anchor_kind = _anchor_for_node(
+        nid,
+        pos,
+        adj,
+        node_groups=node_groups,
+        centroids=centroids,
+        global_cx=gcx,
+        global_cy=gcy,
+    )
+
+    x0, y0 = pos[nid]
+    global0 = count_edge_crossings(pos, links)
+    local0 = crossings_involving_node(nid, pos, links, adj)
+    stretch0 = _incident_stretch(nid, pos, adj, target_nn)
+
+    samples = _inward_samples(x0, y0, ax, ay, max_pull=pull)
+    # Also try unit centroid if neighbor anchor was used.
+    if anchor_kind == "neighbors":
+        for gk in node_groups.get(nid, ()):
+            if gk in centroids:
+                cx, cy = centroids[gk]
+                samples.extend(_inward_samples(x0, y0, cx, cy, max_pull=pull))
+
+    seen: set[tuple[int, int]] = set()
+    uniq: list[tuple[float, float, float, float]] = []
+    for sx, sy, r, ang in samples:
+        key = (int(round(sx)), int(round(sy)))
+        if key in seen:
+            continue
+        seen.add(key)
+        uniq.append((sx, sy, r, ang))
+        if len(uniq) >= cand_cap:
+            break
+
+    scored: list[dict[str, Any]] = []
+    for sx, sy, r, ang in uniq:
+        c = _eval_candidate(
+            nid,
+            (sx, sy),
+            r,
+            ang,
+            pos=pos,
+            names=names,
+            links=links,
+            adj=adj,
+            global0=global0,
+            local0=local0,
+            target_nn=target_nn,
+            nn_floor=nn_floor,
+            nbs_cap=nbs_cap,
+        )
+        if c is None:
+            continue
+        # Densify: reject moves that raise global crossings.
+        if int(c["delta"]["global"]) > 0:
+            continue
+        scored.append(c)
+
+    scored.sort(key=_densify_score_key)
+    # Prefer stretch drop among non-rising-x candidates.
+    improving = [
+        c
+        for c in scored
+        if c["delta"]["global"] <= 0 and float(c.get("stretch") or 99) < stretch0 - 1e-4
+    ]
+    pool = improving if improving else scored
+    top = _diversify_top(pool, k=max(1, int(top_k)), min_angle_sep=min_angle_sep)
+
+    return {
+        "ok": True,
+        "node_id": nid,
+        "name": names.get(nid, nid),
+        "x0": round(x0, 1),
+        "y0": round(y0, 1),
+        "degree": len(adj.get(nid, ())),
+        "anchor": {"x": round(ax, 1), "y": round(ay, 1), "kind": anchor_kind},
+        "stretch_before": round(stretch0, 3),
+        "crossings_before": {"global": int(global0), "incident": int(local0)},
+        "candidates": top,
+        "sampled": len(seen),
+        "improving_n": len(improving),
+        "max_pull": pull,
+        "nn_floor": nn_floor,
+        "hint": (
+            "prefer rank1 (inward, x not up, stretch down); pick 2/3 if label/util. "
+            "apply with params.pick=1|2|3."
+        ),
+    }
+
+
+def apply_densify_pick(
+    state: LayoutState,
+    sweep: dict[str, Any],
+    *,
+    pick: int = 1,
+) -> OpResult:
+    st = state.copy()
+    nid = str(sweep.get("node_id") or "")
+    cands = list(sweep.get("candidates") or [])
+    if not nid or nid not in st.positions or not cands:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="densify_sweep",
+            note="densify_sweep:noop",
+            params={"error": "no_candidates"},
+        )
+    idx = max(1, min(int(pick), len(cands))) - 1
+    chosen = cands[idx]
+    st.positions[nid] = (float(chosen["x"]), float(chosen["y"]))
+    st.last_moved = {nid}
+    st.meta["densify_sweep"] = {
+        "node_id": nid,
+        "pick": idx + 1,
+        "candidate": chosen,
+        "crossings_before": sweep.get("crossings_before"),
+    }
+    return OpResult(
+        state=st,
+        moved={nid},
+        op="densify_sweep",
+        params={
+            "node_id": nid,
+            "pick": idx + 1,
+            "candidate": chosen,
+            "crossings_before": sweep.get("crossings_before"),
+        },
+        note=(
+            f"densify_sweep pick={idx + 1} "
+            f"g{sweep.get('crossings_before', {}).get('global')}->"
+            f"{chosen['crossings']['global']}"
+        ),
+    )
+
+
+def densify_sweep_round(
+    state: LayoutState,
+    *,
+    params: LayoutParams | None = None,
+    top_n: int = 16,
+    max_degree: int = 8,
+    max_pull: float | None = None,
+    nn_floor: float = 60.0,
+    min_angle_sep: float = 35.0,
+    protect_rigid: bool | str = "off",
+    frozen_ids: set[str] | None = None,
+    focus_ids: list[str] | None = None,
+    groups: list[dict[str, Any]] | None = None,
+) -> OpResult:
+    """Scan sparse/high-stretch nodes; auto-apply #1 if x not up and stretch drops.
+
+    Default ``protect_rigid=off`` (may move portals). Opt in with portals/all.
+    """
+    params = params or LayoutParams()
+    st = state.copy()
+    pos = dict(st.positions)
+    names = dict(st.names)
+    links = list(st.links)
+    adj = {n: set(st.adj.get(n, ())) for n in pos}
+    target_nn = float(getattr(params, "target_nn", 155.0) or 155.0)
+
+    frozen = _resolve_frozen(st, protect_rigid, frozen_ids)
+
+    groups = groups if groups is not None else _groups_from_state(st)
+    valid = set(pos)
+    node_groups, shared, _ = _membership_maps(groups, valid)
+    centroids = _exclusive_centroids(groups, pos, shared)
+    if groups and not _protect_is_off(protect_rigid):
+        frozen |= shared
+
+    gcx = sum(p[0] for p in pos.values()) / max(len(pos), 1)
+    gcy = sum(p[1] for p in pos.values()) / max(len(pos), 1)
+    global0 = count_edge_crossings(pos, links)
+
+    focus = {str(x) for x in (focus_ids or []) if str(x)}
+    scored_nodes: list[tuple[float, str]] = []
+    for nid in pos:
+        if nid in frozen:
+            continue
+        deg = len(adj.get(nid, ()))
+        if deg >= max_degree or deg == 0:
+            continue
+        if groups and nid not in node_groups and not focus:
+            continue
+        stretch = _incident_stretch(nid, pos, adj, target_nn)
+        ax, ay, _ = _anchor_for_node(
+            nid,
+            pos,
+            adj,
+            node_groups=node_groups,
+            centroids=centroids,
+            global_cx=gcx,
+            global_cy=gcy,
+        )
+        dist = math.hypot(pos[nid][0] - ax, pos[nid][1] - ay)
+        if stretch < 1.15 and dist < 180 and nid not in focus:
+            continue
+        # Higher stretch / farther from anchor first; focus boost.
+        pri = 0.0 if nid in focus else 1.0
+        scored_nodes.append((-stretch * 10 - dist / 500.0 + pri * 100, nid))
+    scored_nodes.sort()
+    movable = [nid for _, nid in scored_nodes[: max(1, int(top_n))]]
+
+    moved: set[str] = set()
+    trace: list[dict[str, Any]] = []
+    for nid in movable:
+        st.positions = pos
+        sweep = densify_sweep_node(
+            st,
+            nid,
+            params=params,
+            max_pull=max_pull,
+            nn_floor=nn_floor,
+            min_angle_sep=min_angle_sep,
+            protect_rigid="off",
+            frozen_ids=frozen,
+            groups=groups,
+        )
+        if not sweep.get("ok"):
+            trace.append({"node_id": nid, "skipped": sweep.get("error")})
+            continue
+        cands = list(sweep.get("candidates") or [])
+        if not cands:
+            trace.append({"node_id": nid, "skipped": "no_candidates"})
+            continue
+        best = cands[0]
+        stretch_b = float(sweep.get("stretch_before") or 0)
+        stretch_a = float(best.get("stretch") or 0)
+        if int(best["delta"]["global"]) > 0:
+            trace.append(
+                {
+                    "node_id": nid,
+                    "skipped": "crossing_up",
+                    "best_delta": best["delta"],
+                }
+            )
+            continue
+        if stretch_a >= stretch_b - 1e-4:
+            # Allow pure global drop even if stretch flat.
+            if int(best["delta"]["global"]) >= 0:
+                trace.append(
+                    {
+                        "node_id": nid,
+                        "skipped": "no_stretch_gain",
+                        "stretch_before": stretch_b,
+                        "stretch_after": stretch_a,
+                    }
+                )
+                continue
+        pos[nid] = (float(best["x"]), float(best["y"]))
+        moved.add(nid)
+        trace.append(
+            {
+                "node_id": nid,
+                "name": names.get(nid, nid),
+                "applied": True,
+                "pick": 1,
+                "xy": [best["x"], best["y"]],
+                "delta": best["delta"],
+                "stretch": {"before": stretch_b, "after": stretch_a},
+            }
+        )
+
+    st.positions = pos
+    st.last_moved = moved
+    end_g = count_edge_crossings(pos, links)
+    meta = {
+        "start_crossings": global0,
+        "end_crossings": end_g,
+        "moved_n": len(moved),
+        "scanned_n": len(movable),
+        "trace": trace,
+        "top_n": top_n,
+        "max_degree": max_degree,
+        "nn_floor": nn_floor,
+    }
+    st.meta["densify_sweep"] = meta
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="densify_sweep_round",
+        params=meta,
+        note=f"densify_sweep_round {global0}->{end_g} moved={len(moved)}/{len(movable)}",
+    )
+
+
+def densify_corridor_scan(
+    state: LayoutState,
+    *,
+    groups: list[dict[str, Any]] | None = None,
+    corridor_caps: list[float] | None = None,
+    pulls: list[float] | None = None,
+    iters: int = 6,
+    x_slack: int | None = None,
+) -> OpResult:
+    """Sweep corridor_cap×pull; keep best util with ov=0 and tight crossing slack."""
+    st0 = state.copy()
+    groups = groups if groups is not None else _groups_from_state(st0)
+    before = score_state(st0)
+    before_util = float((before.get("summary") or {}).get("util") or 0.0)
+    before_x = int((before.get("summary") or {}).get("crossings") or 0)
+    before_ov = int((before.get("summary") or {}).get("overlaps") or 0)
+    if before_ov == 0:
+        before_ov = len(overlapping_nodes(st0))
+    xs = [p[0] for p in st0.positions.values()]
+    ys = [p[1] for p in st0.positions.values()]
+    area0 = max(max(xs) - min(xs), 1e-6) * max(max(ys) - min(ys), 1e-6)
+
+    caps = list(corridor_caps) if corridor_caps else list(_DEFAULT_CORRIDOR_CAPS)
+    pull_list = list(pulls) if pulls else list(_DEFAULT_PULLS)
+    slack = (
+        max(5, int(before_x * 0.05))
+        if x_slack is None
+        else max(0, int(x_slack))
+    )
+
+    trials: list[dict[str, Any]] = []
+    best: dict[str, Any] | None = None
+    for cap in caps:
+        for pull in pull_list:
+            op = shrink_long_corridors(
+                st0,
+                edge_len_cap=float(cap),
+                pull=float(pull),
+                iters=max(1, int(iters)),
+                max_bridges=8 if groups else 12,
+                min_island=2 if groups else 6,
+                groups=groups or None,
+                accept_crossings=True,
+            )
+            fin = score_state(op.state)
+            util = float((fin.get("summary") or {}).get("util") or 0.0)
+            x1 = int((fin.get("summary") or {}).get("crossings") or 0)
+            ov = len(overlapping_nodes(op.state))
+            xs1 = [p[0] for p in op.state.positions.values()]
+            ys1 = [p[1] for p in op.state.positions.values()]
+            area1 = max(max(xs1) - min(xs1), 1e-6) * max(max(ys1) - min(ys1), 1e-6)
+            area_ratio = area0 / area1
+            row = {
+                "corridor_cap": float(cap),
+                "pull": float(pull),
+                "util": util,
+                "crossings": x1,
+                "overlaps": ov,
+                "moved_n": len(op.moved),
+                "bbox_area_ratio": round(area_ratio, 4),
+                "note": op.note,
+            }
+            trials.append(row)
+            if ov > 0:
+                continue
+            if x1 > before_x + slack:
+                continue
+            # Accept if util rises at all, or bbox shrinks ≥0.8% (metro
+            # corridors often move area×1.01 before util clears 1%).
+            util_up = util > before_util + 1e-6
+            area_up = area_ratio >= 1.008
+            if not util_up and not area_up:
+                continue
+            rank = (util, area_ratio, -x1)
+            if best is None or rank > best["rank"]:
+                best = {
+                    "rank": rank,
+                    "state": op.state,
+                    "moved": set(op.moved),
+                    "row": row,
+                }
+
+    if best is None:
+        meta = {
+            "start_crossings": before_x,
+            "end_crossings": before_x,
+            "start_util": before_util,
+            "end_util": before_util,
+            "reverted": True,
+            "reason": "no_util_gain",
+            "trials": trials,
+            "x_slack": slack,
+        }
+        st0.meta["densify_corridor"] = meta
+        return OpResult(
+            state=st0,
+            moved=set(),
+            op="densify_corridor_scan",
+            params=meta,
+            note="densify_corridor_scan:reverted no_util_gain",
+        )
+
+    st = best["state"]
+    fin = score_state(st)
+    end_util = float((fin.get("summary") or {}).get("util") or 0.0)
+    end_x = int((fin.get("summary") or {}).get("crossings") or 0)
+    meta = {
+        "start_crossings": before_x,
+        "end_crossings": end_x,
+        "start_util": before_util,
+        "end_util": end_util,
+        "reverted": False,
+        "chosen": best["row"],
+        "trials": trials,
+        "x_slack": slack,
+        "moved_n": len(best["moved"]),
+    }
+    st.meta = dict(st.meta or {})
+    st.meta["densify_corridor"] = meta
+    st.last_moved = best["moved"]
+    return OpResult(
+        state=st,
+        moved=best["moved"],
+        op="densify_corridor_scan",
+        params=meta,
+        note=(
+            f"densify_corridor_scan util {before_util:.4f}->{end_util:.4f} "
+            f"x {before_x}->{end_x} "
+            f"cap={best['row']['corridor_cap']:.0f} pull={best['row']['pull']}"
+        ),
+    )
+
+
+def densify_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    o = overrides or {}
+    out: dict[str, Any] = {}
+    if o.get("node_id") is not None:
+        out["node_id"] = str(o.get("node_id") or "").strip()
+    elif o.get("fabric_node_id") is not None:
+        out["node_id"] = str(o.get("fabric_node_id") or "").strip()
+    if o.get("pick") is not None:
+        try:
+            out["pick"] = max(1, min(3, int(o["pick"])))
+        except (TypeError, ValueError):
+            out["pick"] = 1
+    for flag in ("round", "corridor"):
+        if o.get(flag) is not None:
+            out[flag] = str(o.get(flag)).lower() not in {
+                "0",
+                "false",
+                "no",
+                "off",
+                "",
+            } or o.get(flag) is True
+    phase = str(o.get("phase") or "").strip().lower()
+    if phase in {"corridor", "corridors", "shrink"}:
+        out["corridor"] = True
+    elif phase in {"intra", "node", "round"}:
+        out["round"] = out.get("round", True)
+    for key, cast, default in (
+        ("top_n", int, 16),
+        ("max_degree", int, 8),
+        ("cand_cap", int, 180),
+        ("top_k", int, 3),
+        ("iters", int, 6),
+    ):
+        if key not in o or o[key] is None:
+            out[key] = default
+            continue
+        try:
+            out[key] = cast(o[key])
+        except (TypeError, ValueError):
+            out[key] = default
+    if o.get("max_pull") is not None:
+        try:
+            out["max_pull"] = float(o["max_pull"])
+        except (TypeError, ValueError):
+            pass
+    if o.get("nn_floor") is not None:
+        try:
+            out["nn_floor"] = float(o["nn_floor"])
+        except (TypeError, ValueError):
+            out["nn_floor"] = 60.0
+    else:
+        out["nn_floor"] = 60.0
+    if o.get("min_angle_sep") is not None:
+        try:
+            out["min_angle_sep"] = float(o["min_angle_sep"])
+        except (TypeError, ValueError):
+            out["min_angle_sep"] = 35.0
+    else:
+        out["min_angle_sep"] = 35.0
+    if "protect_rigid" in o:
+        v = o["protect_rigid"]
+        if isinstance(v, bool):
+            out["protect_rigid"] = "portals" if v else "off"
+        else:
+            key = str(v).strip().lower()
+            if key in {"0", "false", "no", "off", "none"}:
+                out["protect_rigid"] = "off"
+            elif key in {"1", "true", "yes", "on", "portals", "skeleton"}:
+                out["protect_rigid"] = "portals"
+            elif key in {"all", "full", "rigid"}:
+                out["protect_rigid"] = "all"
+            else:
+                out["protect_rigid"] = key
+    else:
+        # densify breaks rigid by default (opt in with protect_rigid=portals).
+        out["protect_rigid"] = "off"
+    focus = o.get("focus_ids") or o.get("focus_node_ids")
+    if isinstance(focus, list):
+        out["focus_ids"] = [str(x).strip() for x in focus if str(x).strip()]
+    raw_p = o.get("portal_ids")
+    if isinstance(raw_p, list):
+        out["frozen_ids"] = {str(x) for x in raw_p if str(x)}
+    caps = o.get("corridor_caps") or o.get("corridor_cap_list")
+    if isinstance(caps, list) and caps:
+        try:
+            out["corridor_caps"] = [float(x) for x in caps]
+        except (TypeError, ValueError):
+            pass
+    elif o.get("corridor_cap") is not None:
+        try:
+            out["corridor_caps"] = [float(o["corridor_cap"])]
+        except (TypeError, ValueError):
+            pass
+    pulls = o.get("pulls") or o.get("pull_list")
+    if isinstance(pulls, list) and pulls:
+        try:
+            out["pulls"] = [float(x) for x in pulls]
+        except (TypeError, ValueError):
+            pass
+    elif o.get("pull") is not None:
+        try:
+            out["pulls"] = [float(o["pull"])]
+        except (TypeError, ValueError):
+            pass
+    if o.get("x_slack") is not None:
+        try:
+            out["x_slack"] = int(o["x_slack"])
+        except (TypeError, ValueError):
+            pass
+    return out

netx_api/mcp_remote/topology/layout_ops/dual_mass.py

@@ -0,0 +1,451 @@
+"""Same-canvas dual-unit beautify: parallel lanes + straight chains.
+
+Role: after gravity (`mass_merge`) has clustered the canvas, reshape each
+dual-unit neighborhood in place — multi-corridor → H/V lanes; chains →
+straight (no 回字). Does **not** redistribute the whole canvas by default.
+
+Modes:
+  - refine (default / auto when spread): beautify onto current world portals
+  - full / repark: optional orbit_pack (legacy redistribute) then beautify
+
+Typical agent flow:
+  mass_merge (gravity) → polish_crossings → dual_mass ↔ polish
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.compose_orbit import orbit_pack_blocks
+from netx_api.mcp_remote.topology.layout_ops.compose_views import ComposeBlock, _rigid_align_to_world
+from netx_api.mcp_remote.topology.layout_ops.dual_units import (
+    DualUnit,
+    beautify_dual_unit_positions,
+    classify_dual_unit,
+    find_dual_portal_units,
+)
+from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local
+from netx_api.mcp_remote.topology.layout_ops.mass_field import (
+    attach_mass_to_compose_meta,
+    build_mass_field,
+)
+from netx_api.mcp_remote.topology.layout_ops.mass_merge import mass_merge_round
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _unit_links(
+    unit: DualUnit,
+    adj: dict[str, tuple[str, ...]],
+) -> list[tuple[str, str]]:
+    members = unit.member_ids()
+    seen: set[tuple[str, str]] = set()
+    out: list[tuple[str, str]] = []
+    for n in members:
+        for v in adj.get(n, ()):
+            if v not in members:
+                continue
+            pair = (n, v) if n <= v else (v, n)
+            if pair in seen:
+                continue
+            seen.add(pair)
+            out.append(pair)
+    return out
+
+
+def _misc_grid_positions(
+    node_ids: list[str],
+    *,
+    pitch: float = 200.0,
+) -> dict[str, tuple[float, float]]:
+    if not node_ids:
+        return {}
+    cols = max(1, int(math.ceil(math.sqrt(len(node_ids)))))
+    pos: dict[str, tuple[float, float]] = {}
+    for i, nid in enumerate(sorted(node_ids)):
+        r, c = divmod(i, cols)
+        pos[nid] = (c * pitch, r * pitch)
+    return pos
+
+
+def _unit_centroids(
+    positions: dict[str, tuple[float, float]],
+    units: list[DualUnit],
+) -> dict[int, tuple[float, float]]:
+    out: dict[int, tuple[float, float]] = {}
+    for u in units:
+        ids = [n for n in u.member_ids() if n in positions]
+        if not ids:
+            continue
+        xs = [positions[n][0] for n in ids]
+        ys = [positions[n][1] for n in ids]
+        out[int(u.unit_id)] = (sum(xs) / len(xs), sum(ys) / len(ys))
+    return out
+
+
+def _centroid_drift(
+    before: dict[int, tuple[float, float]],
+    after: dict[int, tuple[float, float]],
+) -> dict[str, float]:
+    keys = sorted(set(before) & set(after))
+    if not keys:
+        return {"mean": 0.0, "max": 0.0, "n": 0.0}
+    dists = [
+        math.hypot(after[k][0] - before[k][0], after[k][1] - before[k][1])
+        for k in keys
+    ]
+    return {
+        "mean": round(sum(dists) / len(dists), 2),
+        "max": round(max(dists), 2),
+        "n": float(len(dists)),
+    }
+
+
+def _knobs_from_meta(state: LayoutState) -> dict[str, Any]:
+    raw = (state.meta or {}).get("_dual_mass") or {}
+    return dict(raw) if isinstance(raw, dict) else {}
+
+
+def _resolve_mode(state: LayoutState, mode: str) -> str:
+    m = str(mode or "auto").strip().lower() or "auto"
+    if m in {"full", "seed", "pack", "repark"}:
+        return "full"
+    if m in {"refine", "sweep", "stabilize", "beautify"}:
+        return "refine"
+    # auto: beautify in place once the canvas is already spread / has meta.
+    if (state.meta or {}).get("dual_mass"):
+        return "refine"
+    xs = [p[0] for p in state.positions.values()]
+    ys = [p[1] for p in state.positions.values()]
+    if not xs:
+        return "refine"
+    span = max(max(xs) - min(xs), max(ys) - min(ys))
+    # Collapsed dump → optional repark; otherwise in-place beautify.
+    return "full" if span < 800.0 else "refine"
+
+
+def _beautify_blocks(
+    st: LayoutState,
+    units: list[DualUnit],
+    params: LayoutParams,
+) -> tuple[list[ComposeBlock], int, int, set[str], dict[str, int]]:
+    blocks: list[ComposeBlock] = []
+    unit_ok = 0
+    unit_fail = 0
+    covered: set[str] = set()
+    kinds: dict[str, int] = {"petal": 0, "hui": 0, "straight": 0}
+    for u in units:
+        kind = classify_dual_unit(u)
+        kinds[kind] = kinds.get(kind, 0) + 1
+        local = beautify_dual_unit_positions(st, u, params)
+        links_u = _unit_links(u, st.adj)
+        x_u = count_edge_crossings(local, links_u) if links_u else 0
+        if x_u != 0:
+            unit_fail += 1
+        else:
+            unit_ok += 1
+        covered |= set(local.keys())
+        blocks.append(ComposeBlock(key=f"u{u.unit_id}", positions=local))
+    return blocks, unit_ok, unit_fail, covered, kinds
+
+
+def _refine_beautify_onto_world(
+    st: LayoutState,
+    units: list[DualUnit],
+    params: LayoutParams,
+) -> tuple[LayoutState, set[str], int, int, dict[str, int]]:
+    """Beautify each unit rigidly onto current portal world coords (no re-orbit)."""
+    world = dict(st.positions)
+    moved: set[str] = set()
+    unit_ok = 0
+    unit_fail = 0
+    kinds: dict[str, int] = {"petal": 0, "hui": 0, "straight": 0}
+    for u in units:
+        kind = classify_dual_unit(u)
+        kinds[kind] = kinds.get(kind, 0) + 1
+        local = beautify_dual_unit_positions(st, u, params)
+        links_u = _unit_links(u, st.adj)
+        x_u = count_edge_crossings(local, links_u) if links_u else 0
+        if x_u != 0:
+            unit_fail += 1
+        else:
+            unit_ok += 1
+        shared = [p for p in (u.portal_a, u.portal_b) if p in world and p in local]
+        if not shared:
+            continue
+        prefer = None
+        if len(shared) == 1 and len(local) >= 2:
+            cx = sum(p[0] for p in world.values()) / max(len(world), 1)
+            cy = sum(p[1] for p in world.values()) / max(len(world), 1)
+            prefer = (cx, cy)
+        aligned = _rigid_align_to_world(
+            local,
+            world,
+            shared,
+            prefer_center=prefer,
+            links=list(st.links or []),
+        )
+        for nid, xy in aligned.items():
+            if nid in shared:
+                continue
+            if world.get(nid) != xy:
+                moved.add(nid)
+            world[nid] = xy
+    out = st.copy()
+    out.positions = world
+    return out, moved, unit_ok, unit_fail, kinds
+
+
+def _ensure_mass_meta(
+    st: LayoutState,
+    units: list[DualUnit],
+    groups: list[dict[str, Any]] | None = None,
+) -> tuple[LayoutState, dict[str, Any], list[dict[str, Any]]]:
+    grp = list(groups or [])
+    if not grp:
+        for u in units:
+            ids = sorted(u.member_ids())
+            grp.append(
+                {
+                    "key": f"u{u.unit_id}",
+                    "node_ids": ids,
+                    "pivots": [u.portal_a, u.portal_b],
+                    "cores": [u.portal_a, u.portal_b],
+                    "soft": True,
+                }
+            )
+    mass = build_mass_field(st, units=units, groups=grp)
+    st = st.copy()
+    st.meta = dict(st.meta or {})
+    st.meta["mass_field"] = mass
+    packish = {
+        "mode": "dual_mass",
+        "mass_groups": mass.get("groups") or grp,
+        "rigid_groups": grp,
+        "soft": True,
+    }
+    st.meta["compose_views"] = attach_mass_to_compose_meta(packish, mass)
+    return st, mass, grp
+
+
+def layout_dual_mass(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    pad: float = 500.0,
+    fabric_bridges: bool = True,
+    angle_step: int = 30,
+    mass_merge: bool = False,
+    mass_iters: int = 12,
+    mode: str = "auto",
+    rounds: int = 1,
+    gravity_first: bool = False,
+    stable_drift: float = 120.0,
+) -> OpResult:
+    """Beautify dual-portal neighborhoods (petal / 回 / straight) in place."""
+    params = params or LayoutParams()
+    knobs = _knobs_from_meta(state)
+    if "mode" in knobs:
+        mode = str(knobs["mode"])
+    if "rounds" in knobs:
+        rounds = int(knobs["rounds"] or 1)
+    if "mass_iters" in knobs:
+        mass_iters = int(knobs["mass_iters"] or mass_iters)
+    if "pad" in knobs:
+        pad = float(knobs["pad"] or pad)
+    if "angle_step" in knobs:
+        angle_step = int(knobs["angle_step"] or angle_step)
+    if "mass_merge" in knobs:
+        v = knobs["mass_merge"]
+        mass_merge = bool(v) if not isinstance(v, str) else v.strip().lower() not in {
+            "0",
+            "false",
+            "no",
+            "off",
+        }
+    if "gravity_first" in knobs:
+        v = knobs["gravity_first"]
+        gravity_first = (
+            bool(v)
+            if not isinstance(v, str)
+            else v.strip().lower() in {"1", "true", "yes", "on"}
+        )
+    if "stable_drift" in knobs:
+        stable_drift = float(knobs["stable_drift"] or stable_drift)
+
+    st = state.copy()
+    units = find_dual_portal_units(st)
+    if not units:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="layout_dual_mass",
+            params={"units_n": 0},
+            note="no_dual_units",
+        )
+
+    resolved = _resolve_mode(st, mode)
+    rounds = max(1, min(int(rounds), 8))
+    moved: set[str] = set()
+    unit_ok = 0
+    unit_fail = 0
+    leftovers: list[str] = []
+    pack_meta: dict[str, Any] = {}
+    mass_meta: dict[str, Any] | None = None
+    kinds: dict[str, int] = {"petal": 0, "hui": 0, "straight": 0}
+    drifts: list[dict[str, float]] = []
+    stable = False
+
+    if gravity_first:
+        st, mass, groups = _ensure_mass_meta(st, units)
+        mop = mass_merge_round(
+            st,
+            groups=mass.get("groups") or groups,
+            iters=max(4, int(mass_iters) // 2),
+            protect_rigid="portals",
+            gravity_k=0.45,
+            core_pull_k=1.2,
+        )
+        st = mop.state
+        moved |= mop.moved
+        mass_meta = mop.params if isinstance(mop.params, dict) else None
+
+    for ri in range(rounds):
+        cens_before = _unit_centroids(st.positions, units)
+        pass_mode = resolved if ri == 0 else "refine"
+
+        if pass_mode == "full":
+            blocks, unit_ok, unit_fail, covered, kinds = _beautify_blocks(
+                st, units, params
+            )
+            leftovers = [
+                n
+                for n in st.positions
+                if n not in covered and not str(n).startswith("region:")
+            ]
+            if leftovers:
+                blocks.append(
+                    ComposeBlock(
+                        key="misc",
+                        positions=_misc_grid_positions(
+                            leftovers, pitch=max(float(params.pitch), 180.0)
+                        ),
+                    )
+                )
+            merged, pack_meta = orbit_pack_blocks(
+                blocks,
+                pad=float(pad),
+                links=list(st.links or []),
+                fabric_bridges=bool(fabric_bridges),
+                angle_step=int(angle_step),
+            )
+            if not merged:
+                return OpResult(
+                    state=st,
+                    moved=moved,
+                    op="layout_dual_mass",
+                    params={
+                        "units_n": len(units),
+                        "pack": pack_meta,
+                        "mode": pass_mode,
+                        "kinds": kinds,
+                    },
+                    note="pack_empty",
+                )
+            for nid, xy in merged.items():
+                if st.positions.get(nid) != xy:
+                    moved.add(nid)
+                st.positions[nid] = xy
+            for nid in state.positions:
+                if nid not in st.positions:
+                    st.positions[nid] = state.positions[nid]
+            groups = pack_meta.get("mass_groups") or pack_meta.get("rigid_groups") or []
+            st, mass, groups = _ensure_mass_meta(st, units, groups)
+        else:
+            st, eye_moved, unit_ok, unit_fail, kinds = _refine_beautify_onto_world(
+                st, units, params
+            )
+            moved |= eye_moved
+            st, mass, groups = _ensure_mass_meta(st, units)
+
+        if mass_merge and len(st.positions) >= 3:
+            mop = mass_merge_round(
+                st,
+                groups=mass.get("groups") or groups,
+                iters=max(1, int(mass_iters)),
+                protect_rigid="portals",
+            )
+            st = mop.state
+            moved |= mop.moved
+            mass_meta = mop.params if isinstance(mop.params, dict) else None
+
+        cens_after = _unit_centroids(st.positions, units)
+        drift = _centroid_drift(cens_before, cens_after)
+        drifts.append(drift)
+        if drift["n"] > 0 and drift["mean"] <= float(stable_drift) and ri > 0:
+            stable = True
+            break
+        resolved = "refine"
+
+    fop = fix_overlaps_local(st, params)
+    st = fop.state
+    moved |= fop.moved
+
+    report = {
+        "units_n": len(units),
+        "unit_ok": unit_ok,
+        "unit_fail_cross": unit_fail,
+        "kinds": kinds,
+        "mode": resolved if rounds == 1 else "multi",
+        "mode_first": _resolve_mode(state, mode),
+        "rounds_ran": len(drifts),
+        "rounds_requested": rounds,
+        "centroid_drift": drifts,
+        "stable": stable,
+        "stable_drift": float(stable_drift),
+        "misc_n": len(leftovers),
+        "pack_slots": pack_meta.get("slots"),
+        "pack_final_crossings": pack_meta.get("final_crossings"),
+        "mass_merge": mass_meta,
+        "mass_groups_n": len(
+            (st.meta.get("compose_views") or {}).get("mass_groups") or []
+        ),
+        "gravity_first": bool(gravity_first),
+        "role": "beautify",
+    }
+    st.meta = dict(st.meta or {})
+    st.meta["dual_mass"] = report
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="layout_dual_mass",
+        params=report,
+        note=(
+            f"layout_dual_mass units={len(units)} kinds={kinds} "
+            f"mode={report['mode_first']} rounds={report['rounds_ran']} "
+            f"stable={stable} moved={len(moved)}"
+        ),
+    )
+
+
+def dual_mass_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    """Pick dual_mass knobs from layout params."""
+    if not overrides:
+        return {}
+    out: dict[str, Any] = {}
+    for k in (
+        "mode",
+        "rounds",
+        "mass_iters",
+        "pad",
+        "angle_step",
+        "mass_merge",
+        "gravity_first",
+        "stable_drift",
+        "fabric_bridges",
+    ):
+        if k in overrides:
+            out[k] = overrides[k]
+    return out

netx_api/mcp_remote/topology/layout_ops/fold_chain.py

@@ -0,0 +1,295 @@
+"""Fold a deg≤2 tentacle into the emptiest cheap sector around a hub.
+
+Agent workflow (stage 2): nearest-ring angular sweep → push low-degree blockers
+radially out → place the whole chain on an arc in that sector.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _deg(st: LayoutState) -> dict[str, int]:
+    return {nid: len(st.adj.get(nid, ())) for nid in st.positions}
+
+
+def _chain_from(st: LayoutState, hub: str, stub: str, deg: dict[str, int]) -> list[str]:
+    path = [stub]
+    prev, cur = hub, stub
+    while True:
+        nxt = [x for x in st.adj.get(cur, ()) if x != prev and deg.get(x, 0) <= 2]
+        if len(nxt) != 1:
+            break
+        path.append(nxt[0])
+        prev, cur = cur, nxt[0]
+    return path
+
+
+def _pick_hub(st: LayoutState, deg: dict[str, int], hub_id: str | None) -> str:
+    if hub_id and hub_id in st.positions:
+        return hub_id
+    # Prefer highest degree; break ties by name for stability.
+    ranked = sorted(
+        st.positions.keys(),
+        key=lambda n: (-deg.get(n, 0), st.names.get(n, ""), n),
+    )
+    if not ranked:
+        raise ValueError("fold_chain:empty_graph")
+    return ranked[0]
+
+
+def _first_hop_toward(
+    st: LayoutState, hub: str, target: str
+) -> str | None:
+    """BFS: first neighbor of hub on a path to target."""
+    if target in st.adj.get(hub, ()):
+        return target
+    from collections import deque
+
+    q = deque([hub])
+    prev: dict[str, str | None] = {hub: None}
+    while q:
+        u = q.popleft()
+        for v in st.adj.get(u, ()):
+            if v in prev:
+                continue
+            prev[v] = u
+            if v == target:
+                # Walk back to hop after hub.
+                cur = target
+                while prev[cur] is not None and prev[cur] != hub:
+                    cur = prev[cur]
+                return cur
+            q.append(v)
+    return None
+
+
+def _pick_stub(
+    st: LayoutState,
+    hub: str,
+    deg: dict[str, int],
+    stub_id: str | None,
+    *,
+    min_len: int,
+) -> tuple[str, list[str]]:
+    if stub_id and stub_id in st.positions:
+        hop = _first_hop_toward(st, hub, stub_id)
+        if hop is None:
+            raise ValueError(f"fold_chain:stub_unreachable:{stub_id}")
+        chain = _chain_from(st, hub, hop, deg)
+        return hop, chain
+
+    best: list[str] = []
+    best_stub = ""
+    for nb in sorted(st.adj.get(hub, ()), key=lambda n: (st.names.get(n, ""), n)):
+        if deg.get(nb, 0) > 2:
+            continue
+        chain = _chain_from(st, hub, nb, deg)
+        if len(chain) > len(best):
+            best = chain
+            best_stub = nb
+    if len(best) < min_len:
+        raise ValueError(
+            f"fold_chain:no_tentacle_from_hub:{hub}:need>={min_len},got={len(best)}"
+        )
+    return best_stub, best
+
+
+def _ang_delta(a: float, b: float) -> float:
+    return abs(((a - b + 180.0) % 360.0) - 180.0)
+
+
+def fold_chain_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    """Pull fold_chain knobs from layout params overrides."""
+    o = overrides or {}
+    out: dict[str, Any] = {}
+
+    def _f(key: str) -> float | None:
+        if key not in o or o[key] is None:
+            return None
+        try:
+            return float(o[key])
+        except (TypeError, ValueError):
+            return None
+
+    def _i(key: str) -> int | None:
+        if key not in o or o[key] is None:
+            return None
+        try:
+            return int(o[key])
+        except (TypeError, ValueError):
+            return None
+
+    for key in ("hub_id", "stub_id", "chain_start"):
+        if key in o and o[key] is not None and str(o[key]).strip():
+            out[key] = str(o[key]).strip()
+    for key in ("prefer_mid_deg", "r_arc", "chord", "push_r", "min_gap_deg"):
+        v = _f(key)
+        if v is not None:
+            out[key] = v
+    for key in ("ring_k", "min_chain_len", "protect_deg_ge"):
+        v = _i(key)
+        if v is not None:
+            out[key] = v
+    if "clear_only" in o and o["clear_only"] is not None:
+        out["clear_only"] = str(o["clear_only"]).strip().lower() in {
+            "1",
+            "true",
+            "yes",
+        } or o["clear_only"] is True
+    if isinstance(o.get("protected_ids"), list):
+        out["protected_ids"] = [str(x).strip() for x in o["protected_ids"] if str(x).strip()]
+    return out
+
+
+def fold_chain_into_sector(
+    st: LayoutState,
+    params: LayoutParams,
+    *,
+    hub_id: str | None = None,
+    stub_id: str | None = None,
+    chain_start: str | None = None,
+    prefer_mid_deg: float | None = None,
+    r_arc: float | None = None,
+    chord: float | None = None,
+    push_r: float | None = None,
+    ring_k: int = 14,
+    min_gap_deg: float = 26.0,
+    min_chain_len: int = 3,
+    clear_only: bool = False,
+    protected_ids: list[str] | None = None,
+    protect_deg_ge: int = 4,
+) -> OpResult:
+    """Sweep nearest ring, clear cheapest large gap, fold deg≤2 chain onto arc."""
+    st = st.copy()
+    deg = _deg(st)
+    hub = _pick_hub(st, deg, hub_id)
+    stub = stub_id or chain_start
+    stub, chain = _pick_stub(st, hub, deg, stub, min_len=int(min_chain_len))
+    chain_set = set(chain)
+    hx, hy = st.positions[hub]
+
+    protected = {hub, *chain_set}
+    for pid in protected_ids or []:
+        if str(pid).strip():
+            protected.add(str(pid).strip())
+    # Auto-protect high-degree nodes near hub
+    for nid, d in deg.items():
+        if d >= int(protect_deg_ge):
+            protected.add(nid)
+
+    others: list[tuple[float, float, str]] = []
+    for nid, (x, y) in st.positions.items():
+        if nid == hub or nid in chain_set:
+            continue
+        r = math.hypot(x - hx, y - hy)
+        ang = math.degrees(math.atan2(y - hy, x - hx)) % 360.0
+        others.append((r, ang, nid))
+    others.sort()
+    k = max(4, min(int(ring_k), len(others)))
+    ring = sorted(others[:k], key=lambda t: t[1]) if others else []
+
+    gaps: list[tuple[float, float, float, float, str, str]] = []
+    if len(ring) >= 2:
+        for i in range(len(ring)):
+            a0 = ring[i][1]
+            a1 = ring[(i + 1) % len(ring)][1]
+            gap = (a1 - a0) % 360.0
+            mid = (a0 + gap / 2.0) % 360.0
+            lid, rid = ring[i][2], ring[(i + 1) % len(ring)][2]
+            cost = 0.0
+            for bid in (lid, rid):
+                if bid in protected or deg.get(bid, 0) >= int(protect_deg_ge):
+                    cost += 4.0
+                elif bid in st.adj.get(hub, ()):
+                    cost += 1.0
+            gaps.append((cost, -gap, mid, gap, lid, rid))
+        gaps.sort()
+    else:
+        # Empty / sparse: open southish default sector.
+        mid = float(prefer_mid_deg) if prefer_mid_deg is not None else 90.0
+        gaps = [(0.0, -60.0, mid % 360.0, 60.0, "", "")]
+
+    def mid_penalty(mid: float) -> float:
+        if prefer_mid_deg is None:
+            return 0.0
+        return _ang_delta(mid, float(prefer_mid_deg)) / 30.0
+
+    cand = [g for g in gaps if g[3] >= float(min_gap_deg)] or gaps[:3]
+    cand.sort(key=lambda g: (g[0], mid_penalty(g[2]), -g[3]))
+    cost, _, sector_mid, best_gap, lid, rid = cand[0]
+    sector_half = max(min(best_gap / 2.0 - 2.0, 38.0), 28.0)
+
+    target_nn = float(params.target_nn or 155.0)
+    r_push = float(push_r) if push_r is not None else max(480.0, target_nn * 3.0)
+    arc_r = float(r_arc) if r_arc is not None else max(280.0, target_nn * 2.0)
+    chord_len = float(chord) if chord is not None else max(170.0, target_nn * 1.15)
+
+    moved: set[str] = set()
+    pushed: list[str] = []
+
+    def in_sector(ang: float) -> bool:
+        return _ang_delta(ang, sector_mid) <= sector_half
+
+    for r, ang, nid in others:
+        if not in_sector(ang) or nid in protected or deg.get(nid, 0) >= 3:
+            continue
+        if r >= r_push:
+            continue
+        nr = max(r_push, r + 200.0)
+        rad = math.radians(ang)
+        st.positions[nid] = (hx + nr * math.cos(rad), hy + nr * math.sin(rad))
+        moved.add(nid)
+        pushed.append(nid)
+
+    folded: list[str] = []
+    if not clear_only:
+        n = len(chain)
+        d_ang = math.degrees(2 * math.asin(min(0.95, chord_len / (2.0 * arc_r))))
+        span = d_ang * (n - 1)
+        a0 = sector_mid - span / 2.0
+        for i, nid in enumerate(chain):
+            ang = math.radians((a0 + i * d_ang) % 360.0)
+            st.positions[nid] = (hx + arc_r * math.cos(ang), hy + arc_r * math.sin(ang))
+            moved.add(nid)
+            folded.append(nid)
+
+    st.last_moved = set(moved)
+    st.meta["fold_chain"] = {
+        "hub_id": hub,
+        "stub_id": stub,
+        "chain": chain,
+        "sector_mid": round(sector_mid, 2),
+        "sector_half": round(sector_half, 2),
+        "gap_deg": round(best_gap, 2),
+        "gap_cost": cost,
+        "gap_bounds": [lid, rid],
+        "prefer_mid_deg": prefer_mid_deg,
+        "r_arc": arc_r,
+        "chord": chord_len,
+        "push_r": r_push,
+        "pushed": pushed,
+        "folded": folded,
+        "clear_only": bool(clear_only),
+        "ring_k": k,
+    }
+    note = (
+        f"fold_chain hub={hub[:8]} chain={len(chain)} "
+        f"mid={sector_mid:.0f} gap={best_gap:.0f} pushed={len(pushed)}"
+    )
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="fold_chain_into_sector",
+        params={
+            "hub_id": hub,
+            "stub_id": stub,
+            "sector_mid": sector_mid,
+            "gap_deg": best_gap,
+            "clear_only": bool(clear_only),
+        },
+        note=note,
+    )

netx_api/mcp_remote/topology/layout_ops/force_densify.py

@@ -0,0 +1,599 @@
+"""Semi-rigid force densify: bidirectional edge springs + local repulsion.
+
+Metro util is killed by long bridges; uniform pack crushes nn into overlaps,
+rigid shrink spikes crossings. This runs a few damped force iterations:
+
+- **Edge spring (双向)**: Hookean toward ``ideal_len`` — too long → pull
+  together, too short → push apart (push can also densify by expanding
+  crushed corridors into readable spacing while gravity/long edges shrink
+  the global bbox).
+- **Node repulse**: spatial-hash neighbors push apart below ~1.35×nn_floor.
+- **Gravity**: free nodes gently pull toward free-centroid (bbox extremities).
+- **Semi-rigid**: dual-unit exclusive bodies share a group translation
+  (``rigid_strength``) plus optional per-node deform residual.
+- **Mass / weights**: hubs move less / repulse stronger; leaves follow springs.
+
+Gates: util or bbox gain within soft crossing budget; then polish.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops.hotspots import overlapping_nodes
+from netx_api.mcp_remote.topology.layout_ops.rigid_units import (
+    frozen_ids_for_protect,
+    groups_from_compose_meta,
+)
+from netx_api.mcp_remote.topology.layout_ops.score import score_state
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState, OpResult
+
+_COORD_ABS_MAX = 1.0e6
+_DEFAULT_ITERS = 12
+_DEFAULT_STEP = 0.28
+_DEFAULT_MAX_STEP = 100.0
+_DEFAULT_RIGID = 0.9
+_DEFAULT_DEFORM = 0.12
+_DEFAULT_ATTRACT = 0.85
+_DEFAULT_REPULSE = 1.35
+_DEFAULT_NN_FLOOR = 90.0
+# Only pull edges longer than this multiple of nn_floor (metro bridges).
+_IDEAL_NN_MUL = 6.0
+_MAX_EDGE_PULL = 90.0
+
+
+def _bbox_area(pos: dict[str, tuple[float, float]]) -> float:
+    pts = [
+        p
+        for p in pos.values()
+        if abs(p[0]) <= _COORD_ABS_MAX
+        and abs(p[1]) <= _COORD_ABS_MAX
+        and math.isfinite(p[0])
+        and math.isfinite(p[1])
+    ]
+    if len(pts) < 2:
+        return 1.0
+    xs = [p[0] for p in pts]
+    ys = [p[1] for p in pts]
+    return max(max(xs) - min(xs), 1e-6) * max(max(ys) - min(ys), 1e-6)
+
+
+def _parse_groups(
+    groups: list[dict[str, Any]],
+    valid: set[str],
+) -> list[dict[str, Any]]:
+    counts: dict[str, int] = {}
+    raw: list[tuple[str, list[str], list[str]]] = []
+    for g in groups:
+        key = str(g.get("key") or "")
+        members = [str(n) for n in (g.get("node_ids") or []) if str(n) in valid]
+        if len(members) < 2:
+            continue
+        pivots = [str(p) for p in (g.get("pivots") or []) if str(p) in valid]
+        for n in members:
+            counts[n] = counts.get(n, 0) + 1
+        raw.append((key, members, pivots))
+    shared = {n for n, c in counts.items() if c > 1}
+    out: list[dict[str, Any]] = []
+    for key, members, pivots in raw:
+        exclusive = [n for n in members if n not in shared]
+        out.append(
+            {
+                "key": key,
+                "members": members,
+                "pivots": pivots or [n for n in members if n in shared],
+                "exclusive": exclusive,
+                "shared": [n for n in members if n in shared],
+            }
+        )
+    return out
+
+
+def _mass_and_weights(
+    st: LayoutState,
+    ids: list[str],
+) -> tuple[dict[str, float], dict[str, float], dict[str, float]]:
+    """Return mass, attract_w, repulse_w per node."""
+    mass: dict[str, float] = {}
+    att: dict[str, float] = {}
+    rep: dict[str, float] = {}
+    for n in ids:
+        deg = len(st.adj.get(n, ()))
+        layer = str(st.layers.get(n) or "")
+        # Hubs = heavy + strong territory; leaves = light + strong spring follow.
+        m = 1.0 + 0.28 * float(deg)
+        if layer in ("core", "agg") or deg >= 8:
+            m *= 1.35
+            a_w = 0.65
+            r_w = 1.55
+        elif deg <= 2:
+            m *= 0.75
+            a_w = 1.35
+            r_w = 0.85
+        else:
+            a_w = 1.0
+            r_w = 1.0
+        mass[n] = m
+        att[n] = a_w
+        rep[n] = r_w
+    return mass, att, rep
+
+
+def _spatial_bins(
+    pos: dict[str, tuple[float, float]],
+    ids: list[str],
+    cell: float,
+) -> dict[tuple[int, int], list[str]]:
+    bins: dict[tuple[int, int], list[str]] = {}
+    inv = 1.0 / max(cell, 1e-6)
+    for n in ids:
+        x, y = pos[n]
+        key = (int(math.floor(x * inv)), int(math.floor(y * inv)))
+        bins.setdefault(key, []).append(n)
+    return bins
+
+
+def _accumulate_forces(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    ids: list[str],
+    *,
+    ideal_len: float,
+    nn_floor: float,
+    attract_k: float,
+    repulse_k: float,
+    gravity_k: float,
+    att_w: dict[str, float],
+    rep_w: dict[str, float],
+    frozen: set[str],
+) -> dict[str, tuple[float, float]]:
+    fx = {n: 0.0 for n in ids}
+    fy = {n: 0.0 for n in ids}
+    id_set = set(ids)
+
+    # Weak gravity toward free-node centroid (shrinks bbox extremities).
+    free = [n for n in ids if n not in frozen and n in pos]
+    if gravity_k > 1e-9 and len(free) >= 2:
+        gcx = sum(pos[n][0] for n in free) / len(free)
+        gcy = sum(pos[n][1] for n in free) / len(free)
+        for n in free:
+            dx, dy = gcx - pos[n][0], gcy - pos[n][1]
+            dist = math.hypot(dx, dy)
+            if dist < nn_floor * 2.0:
+                continue
+            # Stronger pull for outliers far from center.
+            mag = gravity_k * min(dist * 0.02, _MAX_EDGE_PULL * 0.8)
+            fx[n] += (dx / dist) * mag
+            fy[n] += (dy / dist) * mag
+
+    # Bidirectional edge spring: L>ideal pull; L<ideal push (Hookean).
+    # Dead-band around ideal avoids jitter; push uses same attract_k scale
+    # (repulse_k still handles non-adjacent crowding).
+    dead = max(ideal_len * 0.06, nn_floor * 0.25)
+    for a, b in links:
+        if a not in pos or b not in pos:
+            continue
+        if a not in id_set and b not in id_set:
+            continue
+        ax, ay = pos[a]
+        bx, by = pos[b]
+        dx, dy = bx - ax, by - ay
+        L = math.hypot(dx, dy)
+        if L < 1e-6:
+            # Coincident endpoints: invent a tiny axis so push can separate.
+            dx, dy, L = 1.0, 0.0, 1.0
+        delta = L - ideal_len
+        if abs(delta) <= dead:
+            continue
+        ux, uy = dx / L, dy / L
+        # Cap |δ| so ultra-long bridges / crushed stubs don't explode.
+        capped = max(-_MAX_EDGE_PULL * 8.0, min(delta, _MAX_EDGE_PULL * 12.0))
+        # Sign: positive δ → pull a toward b (force on a along +u);
+        # negative δ → push a away from b (force on a along -u).
+        mag = attract_k * min(abs(capped) * 0.045, _MAX_EDGE_PULL)
+        if capped < 0:
+            # Slightly softer push than pull (avoid blasting clusters open).
+            mag *= 0.75
+        sign = 1.0 if capped > 0 else -1.0
+        wa = att_w.get(a, 1.0) if a not in frozen else 0.0
+        wb = att_w.get(b, 1.0) if b not in frozen else 0.0
+        wsum = wa + wb
+        if wsum <= 1e-12:
+            continue
+        fa = mag * (wa / wsum)
+        fb = mag * (wb / wsum)
+        # On a: toward b when pulling (sign>0); away when pushing (sign<0).
+        if a in fx and a not in frozen:
+            fx[a] += ux * fa * sign
+            fy[a] += uy * fa * sign
+        if b in fx and b not in frozen:
+            fx[b] -= ux * fb * sign
+            fy[b] -= uy * fb * sign
+
+    # Local repulsion — tight floor only (avoid pushing bbox outward).
+    r0 = max(nn_floor * 1.35, 100.0)
+    cell = max(r0, 1.0)
+    bins = _spatial_bins(pos, ids, cell)
+    for (cx, cy), bucket in bins.items():
+        neighbors: list[str] = []
+        for dx in (-1, 0, 1):
+            for dy in (-1, 0, 1):
+                neighbors.extend(bins.get((cx + dx, cy + dy), ()))
+        seen: set[tuple[str, str]] = set()
+        for a in bucket:
+            if a in frozen:
+                continue
+            ax, ay = pos[a]
+            for b in neighbors:
+                if b == a or b in frozen:
+                    continue
+                pair = (a, b) if a < b else (b, a)
+                if pair in seen:
+                    continue
+                seen.add(pair)
+                bx, by = pos[b]
+                dx, dy = ax - bx, ay - by
+                d = math.hypot(dx, dy)
+                if d >= r0 or d < 1e-6:
+                    continue
+                ux, uy = dx / d, dy / d
+                mag = repulse_k * min(r0 - d, r0) * 0.4 * (
+                    (rep_w.get(a, 1.0) + rep_w.get(b, 1.0)) * 0.5
+                )
+                mag = min(mag, _MAX_EDGE_PULL * 0.9)
+                if a not in frozen:
+                    fx[a] += ux * mag
+                    fy[a] += uy * mag
+                if b not in frozen:
+                    fx[b] -= ux * mag
+                    fy[b] -= uy * mag
+    return {n: (fx[n], fy[n]) for n in ids}
+
+
+def _apply_semi_rigid(
+    deltas: dict[str, tuple[float, float]],
+    groups: list[dict[str, Any]],
+    frozen: set[str],
+    *,
+    rigid_strength: float,
+    deform: float,
+) -> dict[str, tuple[float, float]]:
+    """Blend group mean translation with per-node residual."""
+    out = dict(deltas)
+    rs = max(0.0, min(1.0, float(rigid_strength)))
+    df = max(0.0, min(1.0, float(deform)))
+    # When rigid=1 and deform=0 → pure mean translate for exclusive.
+    for g in groups:
+        excl = [n for n in g.get("exclusive") or [] if n in out and n not in frozen]
+        if len(excl) < 2:
+            continue
+        mx = sum(out[n][0] for n in excl) / len(excl)
+        my = sum(out[n][1] for n in excl) / len(excl)
+        for n in excl:
+            ix, iy = out[n]
+            # rs=1,df=0 → pure group translate; df>0 allows limited shape stretch.
+            out[n] = (
+                rs * mx + (1.0 - rs) * ix + df * rs * (ix - mx),
+                rs * my + (1.0 - rs) * iy + df * rs * (iy - my),
+            )
+    return out
+
+
+def force_densify_round(
+    state: LayoutState,
+    *,
+    groups: list[dict[str, Any]] | None = None,
+    iters: int = _DEFAULT_ITERS,
+    step: float = _DEFAULT_STEP,
+    max_step: float = _DEFAULT_MAX_STEP,
+    ideal_len: float | None = None,
+    nn_floor: float = _DEFAULT_NN_FLOOR,
+    attract_k: float = _DEFAULT_ATTRACT,
+    repulse_k: float = _DEFAULT_REPULSE,
+    gravity_k: float = 0.55,
+    rigid_strength: float = _DEFAULT_RIGID,
+    deform: float = _DEFAULT_DEFORM,
+    protect_rigid: bool | str = "portals",
+    x_slack: int | None = None,
+    damping: float = 0.85,
+) -> OpResult:
+    """Run damped attract/repulse densify with semi-rigid dual-unit bodies."""
+    from netx_api.mcp_remote.topology.layout_jobs import (
+        raise_if_cancelled,
+        report_progress,
+        touch_heartbeat,
+    )
+
+    st = state.copy()
+    groups = groups if groups is not None else groups_from_compose_meta(
+        (st.meta or {}).get("compose_views")
+    )
+    valid = {
+        n
+        for n, (x, y) in st.positions.items()
+        if abs(x) <= _COORD_ABS_MAX
+        and abs(y) <= _COORD_ABS_MAX
+        and math.isfinite(x)
+        and math.isfinite(y)
+    }
+    ids = sorted(valid)
+    if len(ids) < 3:
+        return OpResult(
+            state=st, moved=set(), op="force_densify", note="force_densify:too_few"
+        )
+
+    parsed = _parse_groups(groups or [], valid)
+    frozen = frozen_ids_for_protect(st, protect_rigid) if protect_rigid not in (
+        False,
+        "false",
+        "off",
+        "none",
+        "0",
+    ) else set()
+    # Pin region phantoms.
+    for n in list(ids):
+        if str(n).startswith("region:"):
+            frozen.add(n)
+
+    before = score_state(st, fast=True)
+    before_util = float((before.get("summary") or {}).get("util") or 0.0)
+    before_x = int((before.get("summary") or {}).get("crossings") or 0)
+    before_ov = len(overlapping_nodes(st))
+    area0 = _bbox_area(st.positions)
+    slack = (
+        max(12, int(before_x * 0.1))
+        if x_slack is None
+        else max(0, int(x_slack))
+    )
+    # Default ideal targets long corridors only (not near-nn edges).
+    ideal = (
+        float(ideal_len)
+        if ideal_len is not None
+        else max(float(nn_floor) * _IDEAL_NN_MUL, 520.0)
+    )
+    mass, att_w, rep_w = _mass_and_weights(st, ids)
+
+    pos = {n: (float(st.positions[n][0]), float(st.positions[n][1])) for n in ids}
+    links = list(st.links)
+    n_iters = max(1, min(40, int(iters)))
+    step_k = max(0.05, min(1.0, float(step)))
+    cap = max(20.0, float(max_step))
+    damp = max(0.2, min(0.98, float(damping)))
+
+    report_progress(
+        "force_densify",
+        pct=48.0,
+        message=(
+            f"iters={n_iters} ideal={ideal:.0f} "
+            f"rigid={rigid_strength:.2f} deform={deform:.2f}"
+        ),
+        nodes=len(ids),
+        groups=len(parsed),
+    )
+
+    vel = {n: (0.0, 0.0) for n in ids}
+    moved: set[str] = set()
+    # Keep best checkpoint within x_slack (smaller bbox / higher util proxy).
+    _cell = 90.0 * 90.0
+    util_proxy0 = len(ids) * _cell / max(area0, 1e-6)
+    best_pos = dict(pos)
+    # tier=2 within slack, tier=1 soft densify, tier=0 baseline
+    best_key = (0, util_proxy0, -area0, -before_x)
+    best_meta_x = before_x
+    best_meta_util = util_proxy0
+    best_iter = 0
+
+    from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+
+    for it in range(n_iters):
+        raise_if_cancelled()
+        touch_heartbeat()
+        if it % 2 == 0:
+            report_progress(
+                "force_densify",
+                pct=48.0 + 24.0 * (it / max(n_iters, 1)),
+                message=f"iter {it + 1}/{n_iters}",
+                iter=it + 1,
+            )
+        forces = _accumulate_forces(
+            pos,
+            links,
+            ids,
+            ideal_len=ideal,
+            nn_floor=float(nn_floor),
+            attract_k=float(attract_k),
+            repulse_k=float(repulse_k),
+            gravity_k=float(gravity_k),
+            att_w=att_w,
+            rep_w=rep_w,
+            frozen=frozen,
+        )
+        deltas: dict[str, tuple[float, float]] = {}
+        for n in ids:
+            if n in frozen:
+                deltas[n] = (0.0, 0.0)
+                continue
+            fx, fy = forces[n]
+            m = max(mass.get(n, 1.0), 0.2)
+            dx, dy = (fx / m) * step_k, (fy / m) * step_k
+            vx = damp * vel[n][0] + dx
+            vy = damp * vel[n][1] + dy
+            spd = math.hypot(vx, vy)
+            if spd > cap:
+                s = cap / spd
+                vx, vy = vx * s, vy * s
+            vel[n] = (vx, vy)
+            deltas[n] = (vx, vy)
+
+        deltas = _apply_semi_rigid(
+            deltas,
+            parsed,
+            frozen,
+            rigid_strength=rigid_strength,
+            deform=deform,
+        )
+        for n, (dx, dy) in deltas.items():
+            if n in frozen:
+                continue
+            spd = math.hypot(dx, dy)
+            if spd > cap:
+                s = cap / spd
+                dx, dy = dx * s, dy * s
+            if abs(dx) + abs(dy) < 1e-4:
+                continue
+            x, y = pos[n]
+            pos[n] = (x + dx, y + dy)
+            moved.add(n)
+
+        # Checkpoint every 2 iters (and last). Prefer within x_slack; else
+        # still keep densifying states (metro: short x rise → polish after).
+        if it % 2 == 1 or it == n_iters - 1:
+            raise_if_cancelled()
+            touch_heartbeat()
+            x_now = count_edge_crossings(pos, links)
+            area_now = _bbox_area(pos)
+            util_proxy = len(ids) * _cell / max(area_now, 1e-6)
+            densified = util_proxy > util_proxy0 * 1.002 or area_now < area0 * 0.998
+            if not densified:
+                continue
+            soft_cap = before_x + max(slack * 3, int(before_x * 0.45) + 40)
+            if x_now > soft_cap:
+                continue
+            tier = 2 if x_now <= before_x + slack else 1
+            key = (tier, util_proxy, -area_now, -x_now)
+            if key > best_key:
+                best_key = key
+                best_pos = dict(pos)
+                best_meta_x = x_now
+                best_meta_util = util_proxy
+                best_iter = it + 1
+
+    pos = best_pos
+    st.positions = {**st.positions, **pos}
+    fin = score_state(st, fast=True)
+    end_util = float((fin.get("summary") or {}).get("util") or 0.0)
+    end_x = int((fin.get("summary") or {}).get("crossings") or 0)
+    end_ov = len(overlapping_nodes(st))
+    area1 = _bbox_area(st.positions)
+    area_ratio = area0 / max(area1, 1e-6)
+
+    util_up = end_util > before_util + 1e-6
+    area_up = area_ratio >= 1.004
+    x_down = end_x < before_x
+    # Soft final gate: densify may spend up to soft_cap crossings; polish next.
+    soft_final = before_x + max(slack * 3, int(before_x * 0.45) + 40)
+    x_ok = end_x <= soft_final
+    gain = util_up or area_up or x_down
+
+    meta = {
+        "reverted": False,
+        "start_util": before_util,
+        "end_util": end_util,
+        "start_crossings": before_x,
+        "end_crossings": end_x,
+        "start_overlaps": before_ov,
+        "end_overlaps": end_ov,
+        "bbox_area_ratio": round(area_ratio, 4),
+        "moved_n": len(moved),
+        "iters": n_iters,
+        "best_iter": best_iter,
+        "ideal_len": round(ideal, 1),
+        "nn_floor": float(nn_floor),
+        "attract_k": float(attract_k),
+        "repulse_k": float(repulse_k),
+        "gravity_k": float(gravity_k),
+        "rigid_strength": float(rigid_strength),
+        "deform": float(deform),
+        "x_slack": slack,
+        "x_soft_cap": soft_final,
+        "checkpoint_x": best_meta_x,
+        "checkpoint_util_proxy": round(best_meta_util, 6),
+        "protect_rigid": (
+            protect_rigid
+            if isinstance(protect_rigid, str)
+            else ("portals" if protect_rigid else "off")
+        ),
+        "groups_n": len(parsed),
+        "frozen_n": len(frozen),
+    }
+
+    def _revert(reason: str) -> OpResult:
+        meta["reverted"] = True
+        meta["reason"] = reason
+        st0 = state.copy()
+        st0.meta = dict(st0.meta or {})
+        st0.meta["force_densify"] = meta
+        return OpResult(
+            state=st0,
+            moved=set(),
+            op="force_densify",
+            params=meta,
+            note=f"force_densify:reverted {reason}",
+        )
+
+    if best_iter == 0 and not gain:
+        return _revert("no_gain")
+    if not x_ok:
+        return _revert("crossing_rise")
+    if not gain:
+        return _revert("no_gain")
+    # Residual overlaps OK if densified — layout_tool ensure_zero_overlap repairs.
+
+    st.meta = dict(st.meta or {})
+    st.meta["force_densify"] = meta
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="force_densify",
+        params=meta,
+        note=(
+            f"force_densify util {before_util:.4f}->{end_util:.4f} "
+            f"x {before_x}->{end_x} area×{area_ratio:.3f} moved={len(moved)}"
+        ),
+    )
+
+
+def force_densify_params_from_overrides(
+    overrides: dict[str, Any] | None,
+) -> dict[str, Any]:
+    o = overrides or {}
+    out: dict[str, Any] = {}
+    for key, lo, hi, cast in (
+        ("iters", 1, 40, int),
+        ("step", 0.05, 1.0, float),
+        ("max_step", 20.0, 800.0, float),
+        ("ideal_len", 40.0, 4000.0, float),
+        ("nn_floor", 40.0, 400.0, float),
+        ("attract_k", 0.05, 4.0, float),
+        ("repulse_k", 0.05, 4.0, float),
+        ("gravity_k", 0.0, 3.0, float),
+        ("rigid_strength", 0.0, 1.0, float),
+        ("deform", 0.0, 1.0, float),
+        ("damping", 0.2, 0.98, float),
+        ("x_slack", 0, 5000, int),
+    ):
+        if o.get(key) is None:
+            continue
+        try:
+            v = cast(o[key])
+            out[key] = max(lo, min(hi, v))
+        except (TypeError, ValueError):
+            pass
+    if "protect_rigid" in o:
+        v = o.get("protect_rigid")
+        if isinstance(v, bool):
+            out["protect_rigid"] = "portals" if v else "off"
+        else:
+            key = str(v or "portals").strip().lower()
+            if key in {"0", "false", "no", "off", "none"}:
+                out["protect_rigid"] = "off"
+            elif key in {"all", "full", "rigid"}:
+                out["protect_rigid"] = "all"
+            else:
+                out["protect_rigid"] = "portals"
+    groups = o.get("rigid_groups") or o.get("_rigid_groups")
+    if isinstance(groups, list):
+        out["groups"] = groups
+    return out

netx_api/mcp_remote/topology/layout_ops/graph_util.py

@@ -0,0 +1,200 @@
+"""Graph helpers for layout ops."""
+
+from __future__ import annotations
+
+from collections import deque
+
+from netx_api.mcp_remote.topology.layout_ops.level_util import infer_layer
+
+__all__ = [
+    "infer_layer",
+    "bbox",
+    "connected_components",
+    "order_ans",
+    "chain_order",
+]
+
+
+def bbox(pos: dict[str, tuple[float, float]]) -> tuple[float, float, float, float]:
+    xs = [p[0] for p in pos.values()]
+    ys = [p[1] for p in pos.values()]
+    return min(xs), min(ys), max(xs), max(ys)
+
+
+def connected_components(nodes: set[str], g: dict[str, set[str]]) -> list[list[str]]:
+    seen: set[str] = set()
+    out: list[list[str]] = []
+    for s in sorted(nodes):
+        if s in seen:
+            continue
+        q = deque([s])
+        seen.add(s)
+        comp: list[str] = []
+        while q:
+            u = q.popleft()
+            comp.append(u)
+            for v in g.get(u, ()):
+                if v in nodes and v not in seen:
+                    seen.add(v)
+                    q.append(v)
+        out.append(comp)
+    out.sort(key=lambda c: -len(c))
+    return out
+
+
+def order_ans(
+    ans: list[str], ens: set[str], g: dict[str, set[str]], names: dict[str, str]
+) -> list[str]:
+    if len(ans) <= 1:
+        return list(ans)
+
+    def dist(a: str, b: str) -> int:
+        q = deque([(a, 0)])
+        seen = {a}
+        while q:
+            u, d = q.popleft()
+            for v in g.get(u, ()):
+                if v == b:
+                    return d + 1
+                if v in seen:
+                    continue
+                if v in ens or v in ans:
+                    seen.add(v)
+                    q.append((v, d + 1))
+        return 10**6
+
+    start = max(ans, key=lambda a: (len([x for x in g.get(a, ()) if x in ens]), names[a]))
+    seq = [start]
+    rest = set(ans) - {start}
+    while rest:
+        last = seq[-1]
+        nxt = min(rest, key=lambda a: (dist(last, a), names[a]))
+        seq.append(nxt)
+        rest.remove(nxt)
+    return seq
+
+
+def chain_order(
+    nodes: list[str], g: dict[str, set[str]], names: dict[str, str]
+) -> list[str]:
+    """Covering walk (may include all nodes). Prefer spine_backbone for true spine."""
+    s = set(nodes)
+    if not s:
+        return []
+    sub = {n: [x for x in g.get(n, ()) if x in s] for n in nodes}
+    ends = [n for n in nodes if len(sub[n]) <= 1] or list(nodes)
+    start = sorted(ends, key=lambda n: (len(sub[n]), names.get(n, n)))[0]
+    ordered = [start]
+    prev = None
+    cur = start
+    seen = {start}
+    while len(ordered) < len(nodes):
+        nxts = [x for x in sub[cur] if x != prev and x not in seen]
+        if not nxts:
+            cand = [v for u in ordered for v in sub[u] if v not in seen]
+            if not cand:
+                rest = [n for n in nodes if n not in seen]
+                if not rest:
+                    break
+                nxts = [sorted(rest, key=lambda n: names.get(n, n))[0]]
+            else:
+                nxts = [sorted(cand, key=lambda n: names.get(n, n))[0]]
+            prev = None
+        nxts.sort(key=lambda n: names.get(n, n))
+        prev, cur = cur, nxts[0]
+        ordered.append(cur)
+        seen.add(cur)
+    return ordered
+
+
+def spine_backbone(
+    nodes: list[str], g: dict[str, set[str]], names: dict[str, str]
+) -> list[str]:
+    """Longest shortest-path among `nodes` (approx diameter). Side branches stay off-spine."""
+    s = set(nodes)
+    if not s:
+        return []
+    if len(s) == 1:
+        return list(s)
+
+    def bfs(src: str) -> tuple[dict[str, int], dict[str, str | None]]:
+        dist = {src: 0}
+        parent: dict[str, str | None] = {src: None}
+        q = deque([src])
+        while q:
+            u = q.popleft()
+            for v in g.get(u, ()):
+                if v not in s or v in dist:
+                    continue
+                dist[v] = dist[u] + 1
+                parent[v] = u
+                q.append(v)
+        return dist, parent
+
+    # eccentricity from an arbitrary end, then from farthest
+    seed = sorted(nodes, key=lambda n: (len([x for x in g.get(n, ()) if x in s]), names.get(n, n)))[0]
+    d1, _ = bfs(seed)
+    a = max(d1, key=lambda n: (d1[n], names.get(n, n)))
+    d2, parent = bfs(a)
+    b = max(d2, key=lambda n: (d2[n], names.get(n, n)))
+    path = [b]
+    while parent.get(path[-1]) is not None:
+        path.append(parent[path[-1]] or "")
+    path.reverse()
+    return [n for n in path if n]
+
+
+def build_state_from_nodes_edges(
+    nodes: list[dict], edges: list[dict]
+) -> "LayoutState":  # noqa: F821
+    from netx_api.mcp_remote.topology.layout_ops.state import LayoutState
+    from netx_api.mcp_remote.topology.layout_metrics import collapse_links
+    from netx_api.mcp_remote.topology.layout_ops.level_util import _parse_level
+
+    names: dict[str, str] = {}
+    layers: dict[str, str] = {}
+    levels: dict[str, float] = {}
+    ids: list[str] = []
+    for n in nodes:
+        if not isinstance(n, dict):
+            continue
+        fid = str(n.get("fabric_node_id") or n.get("id") or "").strip()
+        if not fid:
+            continue
+        nm = str(n.get("name") or n.get("label") or fid)
+        ids.append(fid)
+        names[fid] = nm
+        layers[fid] = infer_layer(nm, n.get("role"), n.get("level"))
+        lv = _parse_level(n.get("level"))
+        if lv is not None:
+            levels[fid] = float(lv)
+
+    adj: dict[str, set[str]] = {i: set() for i in ids}
+    links = collapse_links(edges)
+    for a, b in links:
+        if a in adj and b in adj:
+            adj[a].add(b)
+            adj[b].add(a)
+
+    positions: dict[str, tuple[float, float]] = {}
+    for n in nodes:
+        if not isinstance(n, dict):
+            continue
+        fid = str(n.get("fabric_node_id") or n.get("id") or "").strip()
+        if not fid:
+            continue
+        if n.get("x") is not None and n.get("y") is not None:
+            try:
+                positions[fid] = (float(n["x"]), float(n["y"]))
+            except (TypeError, ValueError):
+                pass
+
+    return LayoutState(
+        positions=positions,
+        names=names,
+        layers=layers,
+        levels=levels,
+        links=[(a, b) for a, b in links if a in adj and b in adj],
+        adj=adj,
+        meta={"ids": ids},
+    )

netx_api/mcp_remote/topology/layout_ops/hierarchy.py

@@ -0,0 +1,591 @@
+"""Top-down hierarchy layout: contract → order sectors → expand.
+
+At each hub level we only score crossings on the *contracted* graph
+(parent + one representative per child territory). Internal edges of
+children are ignored until that child is expanded. Accept/reject is
+driven by contracted crossings only — global leaf crossings may rise
+temporarily; that is intentional (fix lower levels later).
+
+Expand is a *rigid* polar remapping of each stub territory onto the new
+sector angle so local corridor geometry survives the reorder.
+"""
+
+from __future__ import annotations
+
+import math
+from collections import deque
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings, segments_properly_intersect
+from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local
+from netx_api.mcp_remote.topology.layout_ops.partition import pick_hub_seeds
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _ang(pos: dict[str, tuple[float, float]], hub: str, nid: str) -> float:
+    hx, hy = pos[hub]
+    x, y = pos[nid]
+    return math.atan2(y - hy, x - hx)
+
+
+def _stub_territories(
+    hub: str,
+    members: set[str],
+    adj: dict[str, set[str]],
+    pinned: set[str],
+) -> tuple[list[str], dict[str, str]]:
+    stubs = [
+        n
+        for n in adj.get(hub, ())
+        if n in members and n not in pinned
+    ]
+    owner: dict[str, str] = {}
+    q: deque[str] = deque()
+    for stub in stubs:
+        owner[stub] = stub
+        q.append(stub)
+    while q:
+        u = q.popleft()
+        for v in adj.get(u, ()):
+            if v not in members or v == hub or v in owner or v in pinned:
+                continue
+            owner[v] = owner[u]
+            q.append(v)
+    return stubs, owner
+
+
+def _owner_hub_map(
+    adj: dict[str, set[str]],
+    hubs: set[str],
+) -> dict[str, str]:
+    """BFS Voronoi: every node maps to nearest hub (ties: first reached)."""
+    owner: dict[str, str] = {h: h for h in hubs}
+    q: deque[str] = deque(hubs)
+    while q:
+        u = q.popleft()
+        ou = owner[u]
+        for v in adj.get(u, ()):
+            if v in owner:
+                continue
+            owner[v] = ou
+            q.append(v)
+    return owner
+
+
+def _contracted_links(
+    hub: str,
+    stubs: list[str],
+    owner: dict[str, str],
+    adj: dict[str, set[str]],
+    pinned: set[str],
+    *,
+    hub_of: dict[str, str] | None = None,
+) -> list[tuple[str, str]]:
+    """Edges among {hub} ∪ stubs after contracting each stub territory.
+
+    Foreign endpoints outside this hub's territories collapse to their
+    owning hub (Voronoi), so cross-block edges are not dropped from the
+    contracted objective.
+    """
+    terr: dict[str, set[str]] = {s: {s} for s in stubs}
+    for n, o in owner.items():
+        terr.setdefault(o, set()).add(n)
+    node_to_rep: dict[str, str] = {hub: hub}
+    for s, nodes in terr.items():
+        for n in nodes:
+            node_to_rep[n] = s
+
+    links: set[tuple[str, str]] = set()
+    for s in stubs:
+        links.add(tuple(sorted((hub, s))))
+    for s, nodes in terr.items():
+        for n in nodes:
+            for v in adj.get(n, ()):
+                if v == hub:
+                    continue
+                if v in node_to_rep:
+                    r = node_to_rep[v]
+                    if r != s:
+                        links.add(tuple(sorted((s, r))))
+                elif v in pinned:
+                    links.add(tuple(sorted((s, v))))
+                elif hub_of is not None and v in hub_of:
+                    fh = hub_of[v]
+                    if fh != hub:
+                        links.add(tuple(sorted((s, fh))))
+    return sorted(links)
+
+
+def _contracted_crossings(
+    hub: str,
+    stubs_ord: list[str],
+    links: list[tuple[str, str]],
+    *,
+    radius: float,
+    hub_xy: tuple[float, float],
+    foreign_pos: dict[str, tuple[float, float]] | None = None,
+    a0: float | None = None,
+    a1: float | None = None,
+) -> int:
+    """Place stub reps on an arc; count crossings of contracted links only."""
+    hx, hy = hub_xy
+    n = len(stubs_ord)
+    if n == 0:
+        return 0
+    if a0 is None or a1 is None:
+        a0, a1 = -math.pi * 0.85, math.pi * 0.85
+    pos: dict[str, tuple[float, float]] = {hub: (hx, hy)}
+    if foreign_pos:
+        pos.update(foreign_pos)
+    for i, s in enumerate(stubs_ord):
+        mid = 0.5 * (a0 + a1) if n == 1 else a0 + (a1 - a0) * i / (n - 1)
+        pos[s] = (hx + math.cos(mid) * radius, hy + math.sin(mid) * radius)
+    segs = [(a, b) for a, b in links if a in pos and b in pos]
+    c = 0
+    for i, (a, b) in enumerate(segs):
+        p1, p2 = pos[a], pos[b]
+        for u, v in segs[i + 1 :]:
+            if len({a, b, u, v}) < 4:
+                continue
+            if segments_properly_intersect(p1, p2, pos[u], pos[v]):
+                c += 1
+    return c
+
+
+def _score_order(
+    hub: str,
+    stubs_ord: list[str],
+    links: list[tuple[str, str]],
+    *,
+    radius: float,
+    hub_xy: tuple[float, float],
+    foreign_pos: dict[str, tuple[float, float]],
+    a0: float,
+    a1: float,
+) -> int:
+    return _contracted_crossings(
+        hub,
+        stubs_ord,
+        links,
+        radius=radius,
+        hub_xy=hub_xy,
+        foreign_pos=foreign_pos,
+        a0=a0,
+        a1=a1,
+    )
+
+
+def _candidate_orders(stubs: list[str], pos: dict[str, tuple[float, float]], hub: str) -> list[list[str]]:
+    if not stubs:
+        return []
+    geo = sorted(stubs, key=lambda s: _ang(pos, hub, s))
+    orders: list[list[str]] = []
+    seen: set[tuple[str, ...]] = set()
+
+    def _add(ord_: list[str]) -> None:
+        key = tuple(ord_)
+        if key not in seen:
+            seen.add(key)
+            orders.append(ord_)
+
+    for base in (geo, list(reversed(geo))):
+        for k in range(len(base)):
+            _add(base[k:] + base[:k])
+    # Adjacent swaps from geo.
+    for i in range(len(geo) - 1):
+        trial = list(geo)
+        trial[i], trial[i + 1] = trial[i + 1], trial[i]
+        _add(trial)
+    return orders
+
+
+def _best_order_2opt(
+    hub: str,
+    stubs: list[str],
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    *,
+    radius: float,
+    hub_xy: tuple[float, float],
+    foreign_pos: dict[str, tuple[float, float]],
+    a0: float,
+    a1: float,
+) -> tuple[list[str], int]:
+    """Greedy 2-opt / insert bakeoff seeded from candidate rotations."""
+    seeds = _candidate_orders(stubs, pos, hub)
+    best_ord = sorted(stubs, key=lambda s: _ang(pos, hub, s))
+    best_cc = _score_order(
+        hub, best_ord, links, radius=radius, hub_xy=hub_xy, foreign_pos=foreign_pos, a0=a0, a1=a1
+    )
+    for seed in seeds:
+        cur = list(seed)
+        cur_cc = _score_order(
+            hub, cur, links, radius=radius, hub_xy=hub_xy, foreign_pos=foreign_pos, a0=a0, a1=a1
+        )
+        improved = True
+        rounds = 0
+        while improved and rounds < 12:
+            improved = False
+            rounds += 1
+            n = len(cur)
+            for i in range(n):
+                for j in range(i + 2, n + (0 if i == 0 else 1)):
+                    # reverse segment [i:j]
+                    jj = j if j <= n else n
+                    if jj - i < 2:
+                        continue
+                    trial = cur[:i] + list(reversed(cur[i:jj])) + cur[jj:]
+                    if len(trial) != n:
+                        continue
+                    cc = _score_order(
+                        hub,
+                        trial,
+                        links,
+                        radius=radius,
+                        hub_xy=hub_xy,
+                        foreign_pos=foreign_pos,
+                        a0=a0,
+                        a1=a1,
+                    )
+                    if cc < cur_cc:
+                        cur, cur_cc = trial, cc
+                        improved = True
+                        break
+                if improved:
+                    break
+        if cur_cc < best_cc:
+            best_cc = cur_cc
+            best_ord = cur
+    return best_ord, best_cc
+
+
+def _contracted_crossings_at_pos(
+    hub: str,
+    stubs: list[str],
+    links: list[tuple[str, str]],
+    pos: dict[str, tuple[float, float]],
+    foreign_pos: dict[str, tuple[float, float]],
+) -> int:
+    """Contracted crossings using *actual* stub coordinates as representatives."""
+    cpos: dict[str, tuple[float, float]] = {hub: pos[hub]}
+    cpos.update(foreign_pos)
+    for s in stubs:
+        if s in pos:
+            cpos[s] = pos[s]
+    segs = [(a, b) for a, b in links if a in cpos and b in cpos]
+    c = 0
+    for i, (a, b) in enumerate(segs):
+        p1, p2 = cpos[a], cpos[b]
+        for u, v in segs[i + 1 :]:
+            if len({a, b, u, v}) < 4:
+                continue
+            if segments_properly_intersect(p1, p2, cpos[u], cpos[v]):
+                c += 1
+    return c
+
+
+def _swap_territories(
+    hub: str,
+    a: str,
+    b: str,
+    owner: dict[str, str],
+    pinned: set[str],
+    pos: dict[str, tuple[float, float]],
+) -> dict[str, tuple[float, float]]:
+    """Exchange polar angles of two stub territories around hub (rigid)."""
+    hx, hy = pos[hub]
+    aa, ab = _ang(pos, hub, a), _ang(pos, hub, b)
+    delta = ab - aa
+    trial = dict(pos)
+    ta = {n for n, o in owner.items() if o == a} | {a}
+    tb = {n for n, o in owner.items() if o == b} | {b}
+    for n in ta:
+        if n not in pos or (n in pinned and n != a):
+            continue
+        x, y = pos[n]
+        dx, dy = x - hx, y - hy
+        r = math.hypot(dx, dy)
+        th = math.atan2(dy, dx) + delta
+        trial[n] = (hx + r * math.cos(th), hy + r * math.sin(th))
+    for n in tb:
+        if n not in pos or (n in pinned and n != b):
+            continue
+        x, y = pos[n]
+        dx, dy = x - hx, y - hy
+        r = math.hypot(dx, dy)
+        th = math.atan2(dy, dx) - delta
+        trial[n] = (hx + r * math.cos(th), hy + r * math.sin(th))
+    trial[hub] = (hx, hy)
+    return trial
+
+
+def _realize_order_by_swaps(
+    hub: str,
+    stubs: list[str],
+    owner: dict[str, str],
+    pinned: set[str],
+    pos: dict[str, tuple[float, float]],
+    clinks: list[tuple[str, str]],
+    foreign: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    *,
+    target_ord: list[str] | None = None,
+    allow_global_rise: bool = False,
+) -> tuple[dict[str, tuple[float, float]], int, int]:
+    """Greedy adjacent territory swaps; primary score = contracted@pos.
+
+    Search is driven by contracted crossings (lower-level edges ignored in
+    the objective). On an already-laid canvas (stage-2 polish) we also refuse
+    swaps that raise *global* crossings — otherwise leaf noise swamps the
+    map before lower levels can be re-expanded. Set allow_global_rise for
+    true build-from-skeleton top-down.
+    """
+    trial = dict(pos)
+    before = _contracted_crossings_at_pos(hub, stubs, clinks, trial, foreign)
+    g_base = count_edge_crossings(trial, links)
+    target_idx = {s: i for i, s in enumerate(target_ord)} if target_ord else {}
+
+    for _ in range(max(2 * len(stubs), 8)):
+        geo = sorted(stubs, key=lambda s: _ang(trial, hub, s))
+        pair_idxs = list(range(len(geo) - 1))
+        if target_idx:
+            inv = [
+                i
+                for i in pair_idxs
+                if target_idx.get(geo[i], 0) > target_idx.get(geo[i + 1], 0)
+            ]
+            pair_idxs = inv + [i for i in pair_idxs if i not in inv]
+        progressed = False
+        c0 = _contracted_crossings_at_pos(hub, stubs, clinks, trial, foreign)
+        g0 = count_edge_crossings(trial, links)
+        # Pick the adjacent swap with best contracted drop; tie-break by global.
+        best_cand = None
+        best_key = None
+        for i in pair_idxs:
+            cand = _swap_territories(hub, geo[i], geo[i + 1], owner, pinned, trial)
+            for h in pinned:
+                if h in pos:
+                    cand[h] = pos[h]
+            cand[hub] = pos[hub]
+            c1 = _contracted_crossings_at_pos(hub, stubs, clinks, cand, foreign)
+            if c1 >= c0:
+                continue
+            g1 = count_edge_crossings(cand, links)
+            if not allow_global_rise and g1 > g0:
+                continue
+            key = (c0 - c1, g0 - g1)
+            if best_key is None or key > best_key:
+                best_key = key
+                best_cand = cand
+        if best_cand is not None:
+            trial = best_cand
+            progressed = True
+        if not progressed:
+            break
+    after = _contracted_crossings_at_pos(hub, stubs, clinks, trial, foreign)
+    del g_base
+    return trial, before, after
+
+
+def hierarchy_sectors_greedy(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    step: float | None = None,
+    max_hubs: int = 20,
+    allow_global_rise: bool = False,
+) -> OpResult:
+    """Top-down: contract → order by contracted crossings → rigid expand.
+
+    Role beam offsets (pin_beam) belong *after* this pass, not inside it.
+    Default stage-2 polish keeps global from rising; pass allow_global_rise
+    for skeleton-time top-down where leaf crossings are deferred.
+    """
+    params = params or LayoutParams()
+    step_px = float(step if step is not None else max(params.pitch * 0.95, 170.0))
+    pos = dict(state.positions)
+    links = list(state.links)
+    adj = state.adj
+    global0 = count_edge_crossings(pos, links)
+
+    hubs = pick_hub_seeds(state, max_hubs=max_hubs)
+    cores = [h for h in hubs if state.layers.get(h) == "core"]
+    aggs = [h for h in hubs if state.layers.get(h) == "agg"]
+    # Top-down: cores first, then aggs, then remaining high-degree hubs.
+    order_hubs = list(cores) + [h for h in aggs if h not in cores]
+    for h in hubs:
+        if h not in order_hubs:
+            order_hubs.append(h)
+
+    pinned = set(hubs)
+    hub_of = _owner_hub_map(adj, pinned)
+    level_notes: list[dict[str, Any]] = []
+    any_level = False
+    contracted_gain = 0
+
+    for hub in order_hubs:
+        if hub not in pos:
+            continue
+        members = {hub}
+        q: deque[str] = deque([hub])
+        while q:
+            u = q.popleft()
+            for v in adj.get(u, ()):
+                if v in members:
+                    continue
+                if v in pinned and v != hub:
+                    continue
+                members.add(v)
+                q.append(v)
+        for v in adj.get(hub, ()):
+            if v not in pinned:
+                members.add(v)
+
+        stubs, owner = _stub_territories(hub, members, adj, pinned)
+        if len(stubs) < 2:
+            continue
+
+        clinks = _contracted_links(
+            hub, stubs, owner, adj, pinned, hub_of=hub_of
+        )
+        foreign = {
+            n: pos[n]
+            for n in pinned
+            if n != hub and n in pos and any(n in e for e in clinks)
+        }
+        angs = [_ang(pos, hub, s) for s in stubs]
+        a0, a1 = min(angs) - 0.15, max(angs) + 0.15
+        if a1 - a0 < 0.8:
+            mid = sum(angs) / len(angs)
+            a0, a1 = mid - 1.1, mid + 1.1
+
+        geo = sorted(stubs, key=lambda s: _ang(pos, hub, s))
+        base_cc = _score_order(
+            hub,
+            geo,
+            clinks,
+            radius=step_px,
+            hub_xy=pos[hub],
+            foreign_pos=foreign,
+            a0=a0,
+            a1=a1,
+        )
+        best_ord, best_cc = _best_order_2opt(
+            hub,
+            stubs,
+            pos,
+            clinks,
+            radius=step_px,
+            hub_xy=pos[hub],
+            foreign_pos=foreign,
+            a0=a0,
+            a1=a1,
+        )
+        # Abstract arc bakeoff only proposes a target cyclic order.
+        if best_cc >= base_cc:
+            continue
+
+        # Realize via adjacent territory swaps; accept on contracted@pos only.
+        trial, c_before, c_after = _realize_order_by_swaps(
+            hub,
+            stubs,
+            owner,
+            pinned,
+            pos,
+            clinks,
+            foreign,
+            links,
+            target_ord=best_ord,
+            allow_global_rise=allow_global_rise,
+        )
+        if c_after >= c_before:
+            continue
+        for h in pinned:
+            if h in pos:
+                trial[h] = pos[h]
+        trial[hub] = pos[hub]
+        # No per-level overlap fix — it fights sector moves and is slow on
+        # large canvases. Zero-overlap is ensured by the layout_tool wrapper.
+        g1 = count_edge_crossings(trial, links)
+        pos = trial
+        any_level = True
+        contracted_gain += c_before - c_after
+        level_notes.append(
+            {
+                "hub_id": hub,
+                "hub_name": state.names.get(hub, hub),
+                "stubs": len(stubs),
+                "contracted_before": c_before,
+                "contracted_after": c_after,
+                "contracted_arc_before": base_cc,
+                "contracted_arc_after": best_cc,
+                "global_after": g1,
+            }
+        )
+
+    if not any_level:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="hierarchy_sectors",
+            params={"accepted_n": 0, "contracted_gain": 0},
+            note="no_level_improved",
+        )
+
+    # One end-of-pass overlap polish (hubs stay pinned).
+    st_end = state.copy()
+    st_end.positions = pos
+    st_end = fix_overlaps_local(st_end, params).state
+    for h in pinned:
+        if h in pos:
+            st_end.positions[h] = pos[h]
+    pos = {k: (float(v[0]), float(v[1])) for k, v in st_end.positions.items()}
+
+    out = state.copy()
+    out.positions = pos
+    moved = {
+        n for n, p in pos.items() if n in state.positions and p != state.positions[n]
+    }
+    g_end = count_edge_crossings(pos, links)
+    out.meta = dict(out.meta or {})
+    out.meta["hierarchy_sectors"] = {
+        "levels": level_notes,
+        "crossings_before": global0,
+        "crossings_after": g_end,
+        "contracted_gain": contracted_gain,
+    }
+    return OpResult(
+        state=out,
+        moved=moved,
+        op="hierarchy_sectors",
+        params={
+            "accepted_n": len(level_notes),
+            "levels": level_notes[:12],
+            "crossings_before": global0,
+            "crossings_after": g_end,
+            "contracted_gain": contracted_gain,
+        },
+        note=(
+            f"hierarchy_sectors:{len(level_notes)} levels "
+            f"contractedΔ={contracted_gain} x:{global0}->{g_end}"
+        ),
+    )
+
+
+def hierarchy_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    out: dict[str, Any] = {}
+    if not overrides:
+        return out
+    if overrides.get("step") is not None:
+        try:
+            out["step"] = float(overrides["step"])
+        except (TypeError, ValueError):
+            pass
+    if overrides.get("max_hubs") is not None:
+        try:
+            out["max_hubs"] = int(overrides["max_hubs"])
+        except (TypeError, ValueError):
+            pass
+    if "allow_global_rise" in overrides:
+        out["allow_global_rise"] = bool(overrides.get("allow_global_rise"))
+    return out

netx_api/mcp_remote/topology/layout_ops/hotspots.py

@@ -0,0 +1,380 @@
+"""Local hotspots: overlaps / dense cells — scope without touching whole canvas."""
+
+from __future__ import annotations
+
+import math
+from collections import defaultdict
+from dataclasses import replace
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import REC_CENTER_DX, REC_CENTER_DY, node_footprint
+from netx_api.mcp_remote.topology.layout_ops.scope import select_scope
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_ops.transforms import (
+    explode_clusters,
+    pack_utilization,
+    resolve_overlaps,
+)
+
+
+def _aabb_overlap(
+    a: tuple[float, float, float, float], b: tuple[float, float, float, float]
+) -> bool:
+    return a[0] < b[2] and a[2] > b[0] and a[1] < b[3] and a[3] > b[1]
+
+
+def overlapping_nodes(state: LayoutState) -> set[str]:
+    """Nodes whose icon+label footprints collide."""
+    ids = list(state.positions)
+    fps: dict[str, tuple[float, float, float, float]] = {}
+    for n in ids:
+        x, y = state.positions[n]
+        fx0, fy0, fx1, fy1 = node_footprint(state.names.get(n, n))
+        fps[n] = (x + fx0, y + fy0, x + fx1, y + fy1)
+    hit: set[str] = set()
+    for i, a in enumerate(ids):
+        for b in ids[i + 1 :]:
+            if _aabb_overlap(fps[a], fps[b]):
+                hit.add(a)
+                hit.add(b)
+    return hit
+
+
+def expand_hops(
+    seeds: set[str], adj: dict[str, set[str]], *, hops: int = 1
+) -> set[str]:
+    out = set(seeds)
+    frontier = set(seeds)
+    for _ in range(max(0, hops)):
+        nxt: set[str] = set()
+        for u in frontier:
+            nxt |= set(adj.get(u, ()))
+        nxt -= out
+        out |= nxt
+        frontier = nxt
+        if not frontier:
+            break
+    return out
+
+
+def dense_blocks(
+    state: LayoutState,
+    *,
+    cell_w: float = REC_CENTER_DX,
+    cell_h: float = REC_CENTER_DY,
+    min_count: int = 5,
+) -> list[set[str]]:
+    """Grid cells with too many centers — local overcrowding."""
+    if len(state.positions) < min_count:
+        return []
+    buckets: dict[tuple[int, int], set[str]] = defaultdict(set)
+    for n, (x, y) in state.positions.items():
+        buckets[(int(math.floor(x / cell_w)), int(math.floor(y / cell_h)))].add(n)
+    return [nodes for nodes in buckets.values() if len(nodes) >= min_count]
+
+
+def close_clusters(
+    state: LayoutState, *, thr: float = 100.0
+) -> list[set[str]]:
+    """Union-find clusters of centers closer than thr (each cluster = one local scope)."""
+    ids = list(state.positions)
+    parent = {n: n for n in ids}
+
+    def find(x: str) -> str:
+        while parent[x] != x:
+            parent[x] = parent[parent[x]]
+            x = parent[x]
+        return x
+
+    def uni(a: str, b: str) -> None:
+        ra, rb = find(a), find(b)
+        if ra != rb:
+            parent[rb] = ra
+
+    cell = max(thr, 40.0)
+    buckets: dict[tuple[int, int], list[str]] = defaultdict(list)
+    for n, (x, y) in state.positions.items():
+        buckets[(int(x // cell), int(y // cell))].append(n)
+    for n, (x, y) in state.positions.items():
+        cx, cy = int(x // cell), int(y // cell)
+        for dx in (-1, 0, 1):
+            for dy in (-1, 0, 1):
+                for m in buckets[(cx + dx, cy + dy)]:
+                    if m <= n:
+                        continue
+                    mx, my = state.positions[m]
+                    if math.hypot(x - mx, y - my) < thr:
+                        uni(n, m)
+    groups: dict[str, set[str]] = defaultdict(set)
+    for n in ids:
+        groups[find(n)].add(n)
+    return [g for g in groups.values() if len(g) >= 2]
+
+
+def sprawled_component_scopes(
+    state: LayoutState,
+    *,
+    target_util: float,
+    min_size: int = 8,
+    max_size: int = 80,
+) -> list[set[str]]:
+    """Graph components with low util — skip huge ones (those need layout, not local pack)."""
+    from netx_api.mcp_remote.topology.layout_ops.graph_util import bbox, connected_components
+
+    comps = connected_components(set(state.positions), state.adj)
+    out: list[set[str]] = []
+    tile = REC_CENTER_DX * REC_CENTER_DY
+    for c in comps:
+        if len(c) < min_size or len(c) > max_size:
+            continue
+        sub = {n: state.positions[n] for n in c}
+        x0, y0, x1, y1 = bbox(sub)
+        area = max((x1 - x0) * (y1 - y0), 1.0)
+        util = len(c) * tile / area
+        if util < target_util:
+            out.append(set(c))
+    return out
+
+
+def _merge_scopes(scopes: list[set[str]], *, max_size: int = 64) -> list[set[str]]:
+    """Merge intersecting scopes but never grow past max_size."""
+    merged: list[set[str]] = []
+    for s in scopes:
+        if not s or len(s) < 2:
+            continue
+        if len(s) > max_size:
+            continue
+        hit = None
+        for m in merged:
+            if s & m and len(m | s) <= max_size:
+                hit = m
+                break
+        if hit is None:
+            merged.append(set(s))
+        else:
+            hit |= s
+    return [m for m in merged if len(m) >= 2]
+
+
+def hotspot_scopes(
+    state: LayoutState,
+    *,
+    hops: int = 1,
+    dense_min: int = 5,
+    close_thr: float = 100.0,
+    target_util: float | None = None,
+    max_scope: int = 64,
+) -> list[set[str]]:
+    """Local scopes only: overlap / close clusters / dense cells / small sprawled comps."""
+    seeds_list: list[set[str]] = []
+    ov = overlapping_nodes(state)
+    if ov and len(ov) <= max_scope:
+        seeds_list.append(ov)
+    seeds_list.extend(close_clusters(state, thr=close_thr))
+    seeds_list.extend(dense_blocks(state, min_count=dense_min))
+    if target_util is not None:
+        seeds_list.extend(
+            sprawled_component_scopes(
+                state, target_util=target_util, max_size=max_scope
+            )
+        )
+
+    if not seeds_list:
+        return []
+
+    expanded: list[set[str]] = []
+    for s in seeds_list:
+        if len(s) < 2 or len(s) > max_scope:
+            continue
+        e = expand_hops(s, state.adj, hops=hops) & set(state.positions)
+        if len(e) > max_scope:
+            e = set(s)  # drop hop expansion if it balloons
+        expanded.append(e)
+    return _merge_scopes(expanded, max_size=max_scope)
+
+
+def fix_overlaps_local(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """Pull apart only overlapping nodes (+1-hop). No global scale/pack."""
+    from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+
+    params = params or LayoutParams()
+    st = state.copy()
+    seeds = overlapping_nodes(st)
+    if not seeds:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="fix_overlaps_local",
+            params={"seeds": 0},
+            note="no overlaps",
+        )
+
+    c0 = count_edge_crossings(st.positions, st.links)
+    baseline = st.copy()
+    scope = expand_hops(seeds, st.adj, hops=1) & set(st.positions)
+    # Prefer moving smaller set; keep rest as obstacles
+    st = select_scope(st, mode="ids", node_ids=scope).state
+    p = replace(
+        params,
+        overlap_iters=max(220, int(params.overlap_iters)),
+        overlap_step=max(4.0, float(params.overlap_step)),
+    )
+    moved: set[str] = set()
+    r1 = resolve_overlaps(st, p, mode="free")
+    st, moved = r1.state, moved | r1.moved
+    r2 = resolve_overlaps(st, p, mode="lateral")
+    st, moved = r2.state, moved | r2.moved
+
+    # Still stuck? explode tiny coincidences inside scope, then resolve again
+    still = overlapping_nodes(st) & scope
+    if still:
+        st = select_scope(st, mode="ids", node_ids=expand_hops(still, st.adj, hops=1)).state
+        r3 = explode_clusters(st, p, thr=12.0, gap=max(40.0, p.cluster_gap), axis="along")
+        st, moved = r3.state, moved | r3.moved
+        r4 = resolve_overlaps(st, p, mode="free")
+        st, moved = r4.state, moved | r4.moved
+        r5 = resolve_overlaps(st, p, mode="lateral")
+        st, moved = r5.state, moved | r5.moved
+
+    st = select_scope(st, mode="all").state
+    c1 = count_edge_crossings(st.positions, st.links)
+    # Free/explode can shred metro geometry — fall back to lateral-only unstick.
+    if c1 > max(c0 * 1.35, c0 + 60):
+        st = baseline
+        st = select_scope(st, mode="ids", node_ids=scope).state
+        moved = set()
+        p2 = replace(p, overlap_iters=max(320, int(p.overlap_iters)))
+        r = resolve_overlaps(st, p2, mode="lateral")
+        st, moved = r.state, r.moved
+        still = overlapping_nodes(st) & scope
+        if still:
+            st = select_scope(
+                st, mode="ids", node_ids=expand_hops(still, st.adj, hops=1)
+            ).state
+            r = resolve_overlaps(st, p2, mode="lateral")
+            st, moved = r.state, moved | r.moved
+        st = select_scope(st, mode="all").state
+        c1 = count_edge_crossings(st.positions, st.links)
+
+    left = overlapping_nodes(st)
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="fix_overlaps_local",
+        params={
+            "seeds": len(seeds),
+            "scope_n": len(scope),
+            "moved_n": len(moved),
+            "overlaps_left": len(left),
+            "crossings_before": c0,
+            "crossings_after": c1,
+        },
+        note=f"local unstick seeds={len(seeds)} left={len(left)} cross={c0}->{c1}",
+    )
+
+
+def _block_util(state: LayoutState, block: set[str]) -> float:
+    from netx_api.mcp_remote.topology.layout_ops.graph_util import bbox
+
+    if len(block) < 2:
+        return 1.0
+    sub = {n: state.positions[n] for n in block if n in state.positions}
+    if len(sub) < 2:
+        return 1.0
+    x0, y0, x1, y1 = bbox(sub)
+    area = max((x1 - x0) * (y1 - y0), 1.0)
+    return len(sub) * REC_CENTER_DX * REC_CENTER_DY / area
+
+
+def _block_has_close(state: LayoutState, block: set[str], thr: float) -> bool:
+    ids = [n for n in block if n in state.positions]
+    for i, a in enumerate(ids):
+        ax, ay = state.positions[a]
+        for b in ids[i + 1 :]:
+            bx, by = state.positions[b]
+            if math.hypot(ax - bx, ay - by) < thr:
+                return True
+    return False
+
+
+def relax_hotspots(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """Per-hotspot local polish — never global compress.
+
+    - Sprawled block (low util): pack + resolve only
+    - Crowded block (close pairs / overlaps): mild explode + resolve (no pack)
+    """
+    params = params or LayoutParams()
+    st = state.copy()
+    scopes = hotspot_scopes(
+        st, hops=1, dense_min=4, close_thr=100.0, target_util=params.target_util
+    )
+    if not scopes:
+        from netx_api.mcp_remote.topology.layout_ops.scope import list_blocks
+
+        scopes = [b for b in list_blocks(st, mode="component") if 4 <= len(b) <= 64]
+
+    scopes = sorted(scopes, key=len, reverse=True)[:24]
+    total_moved: set[str] = set()
+    block_notes: list[dict[str, Any]] = []
+    close_thr = 55.0  # only true local crowding, not soft nn band
+
+    for i, block in enumerate(scopes):
+        if len(block) < 2:
+            continue
+        st = select_scope(st, mode="ids", node_ids=block).state
+        before_ov = len(overlapping_nodes(st) & block)
+        util = _block_util(st, block)
+        crowded = before_ov > 0 or _block_has_close(st, block, close_thr)
+        sprawled = util < params.target_util
+        mode = "pack" if sprawled and not crowded else ("unstick" if crowded else "skip")
+        moved_here: set[str] = set()
+
+        if mode == "unstick":
+            r1 = explode_clusters(
+                st, params, thr=close_thr, gap=max(40.0, params.cluster_gap), axis="along"
+            )
+            st = r1.state
+            moved_here |= r1.moved
+            r2 = resolve_overlaps(st, params, mode="lateral")
+            st = r2.state
+            moved_here |= r2.moved
+            r3 = resolve_overlaps(st, params, mode="free")
+            st = r3.state
+            moved_here |= r3.moved
+        elif mode == "pack":
+            r1 = pack_utilization(st, params)
+            st = r1.state
+            moved_here |= r1.moved
+            r2 = resolve_overlaps(st, params, mode="lateral")
+            st = r2.state
+            moved_here |= r2.moved
+
+        total_moved |= moved_here
+        after_ov = len(overlapping_nodes(st) & block)
+        block_notes.append(
+            {
+                "i": i,
+                "n": len(block),
+                "mode": mode,
+                "util": round(util, 4),
+                "ov_before": before_ov,
+                "ov_after": after_ov,
+                "moved": len(moved_here),
+            }
+        )
+
+    st = select_scope(st, mode="all").state
+    fin = fix_overlaps_local(st, params)
+    st = fin.state
+    total_moved |= fin.moved
+    return OpResult(
+        state=st,
+        moved=total_moved,
+        op="relax_hotspots",
+        params={"blocks": block_notes, "blocks_n": len(scopes), "moved_n": len(total_moved)},
+        note=f"relax blocks={len(scopes)} moved={len(total_moved)}",
+    )

netx_api/mcp_remote/topology/layout_ops/level_util.py

@@ -0,0 +1,209 @@
+"""Map fabric level / role / name → layout layer key; level y-bands."""
+
+from __future__ import annotations
+
+import math
+import re
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+_ROLE_TO_LAYER = {
+    "external": "external",
+    "core": "core",
+    "cn": "core",
+    "aggregation": "agg",
+    "aggregate": "agg",
+    "agg": "agg",
+    "an": "agg",
+    "access": "access",
+    "en": "access",
+    "edge": "access",
+    "cpe": "access",
+}
+
+# Top → bottom on canvas (smaller fabric level sits higher).
+_BAND_ORDER = ("external", "core", "agg", "access", "other")
+
+
+def _parse_level(value: Any) -> float | None:
+    if value is None:
+        return None
+    if isinstance(value, str):
+        s = value.strip()
+        if not s:
+            return None
+        try:
+            value = float(s)
+        except ValueError:
+            return None
+    try:
+        lv = float(value)
+    except (TypeError, ValueError):
+        return None
+    if not math.isfinite(lv):
+        return None
+    return lv
+
+
+def infer_layer(
+    name: str,
+    role: str | None = None,
+    level: float | None = None,
+) -> str:
+    """Map level / role / name token → external|core|agg|access|other."""
+    lv = _parse_level(level)
+    if lv is not None:
+        maj = int(math.floor(lv))
+        if maj <= 0:
+            return "external"
+        if maj == 1:
+            return "core"
+        if maj == 2:
+            return "agg"
+        return "access"
+    r = str(role or "").strip().lower()
+    if r in _ROLE_TO_LAYER:
+        return _ROLE_TO_LAYER[r]
+    m = re.search(r"-(CN|AN|EN)(\d*)-", name or "", re.I)
+    if not m:
+        return "other"
+    return {"CN": "core", "AN": "agg", "EN": "access"}[m.group(1).upper()]
+
+
+def apply_level_bands(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    y0: float = 120.0,
+    band_gap: float = 320.0,
+    pitch: float | None = None,
+    preserve_x: bool = True,
+    layers: tuple[str, ...] | None = None,
+    max_per_row: int = 24,
+    row_gap: float | None = None,
+) -> OpResult:
+    """Snap nodes into horizontal bands by layer (external→…→access).
+
+    Large bands wrap into multiple rows (``max_per_row``) so hundreds of
+    access nodes are not crushed onto one y-line. Within a row, x is either
+    kept (preserve_x) then gently de-overlapped to ``pitch``, or re-spaced
+    by name order.
+    """
+    params = params or LayoutParams()
+    step = float(pitch if pitch is not None else max(params.pitch, 200.0))
+    rgap = float(row_gap if row_gap is not None else max(step * 0.85, 170.0))
+    per_row = max(4, int(max_per_row or 24))
+    order = tuple(layers) if layers else _BAND_ORDER
+    by: dict[str, list[str]] = {ly: [] for ly in order}
+    for nid in state.positions:
+        ly = state.layers.get(nid) or "other"
+        if ly not in by:
+            ly = "other"
+            by.setdefault(ly, [])
+        by[ly].append(nid)
+
+    pos = dict(state.positions)
+    moved: set[str] = set()
+    band_notes: list[dict[str, Any]] = []
+    y_cursor = float(y0)
+
+    def _place_row(ids: list[str], y: float) -> None:
+        nonlocal moved
+        if not ids:
+            return
+        if preserve_x:
+            ids = sorted(ids, key=lambda n: (pos[n][0], state.names.get(n, n)))
+            xs = [float(pos[n][0]) for n in ids]
+            # Enforce min pitch left→right without reversing order.
+            fixed: list[float] = []
+            for i, x in enumerate(xs):
+                if i == 0:
+                    fixed.append(x)
+                else:
+                    fixed.append(max(x, fixed[-1] + step))
+            for n, x in zip(ids, fixed):
+                nxt = (x, y)
+                if nxt != pos[n]:
+                    moved.add(n)
+                pos[n] = nxt
+        else:
+            ids = sorted(ids, key=lambda n: state.names.get(n, n))
+            for i, n in enumerate(ids):
+                nxt = (40.0 + i * step, y)
+                if nxt != pos.get(n):
+                    moved.add(n)
+                pos[n] = nxt
+
+    for ly in order:
+        ids = by.get(ly) or []
+        if not ids:
+            continue
+        if preserve_x:
+            ids = sorted(ids, key=lambda n: (pos[n][0], state.names.get(n, n)))
+        else:
+            ids = sorted(ids, key=lambda n: state.names.get(n, n))
+        rows = [ids[i : i + per_row] for i in range(0, len(ids), per_row)]
+        y_band0 = y_cursor
+        for ri, row in enumerate(rows):
+            _place_row(row, y_cursor + ri * rgap)
+        band_h = max(0, len(rows) - 1) * rgap
+        band_notes.append(
+            {
+                "layer": ly,
+                "count": len(ids),
+                "y": y_band0,
+                "rows": len(rows),
+                "y_max": y_band0 + band_h,
+            }
+        )
+        y_cursor = y_band0 + band_h + float(band_gap)
+
+    out = state.copy()
+    out.positions = pos
+    out.meta = dict(out.meta or {})
+    out.meta["level_bands"] = {
+        "bands": band_notes,
+        "preserve_x": preserve_x,
+        "max_per_row": per_row,
+    }
+    return OpResult(
+        state=out,
+        moved=moved,
+        op="level_bands",
+        params={
+            "bands": band_notes,
+            "preserve_x": preserve_x,
+            "y0": y0,
+            "band_gap": band_gap,
+            "pitch": step,
+            "max_per_row": per_row,
+            "moved_n": len(moved),
+        },
+        note=f"level_bands:{len(band_notes)} bands moved={len(moved)}",
+    )
+
+
+def level_bands_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    out: dict[str, Any] = {}
+    if not overrides:
+        return out
+    for key, cast in (
+        ("y0", float),
+        ("band_gap", float),
+        ("pitch", float),
+        ("row_gap", float),
+        ("max_per_row", int),
+    ):
+        if overrides.get(key) is not None:
+            try:
+                out[key] = cast(overrides[key])
+            except (TypeError, ValueError):
+                pass
+    if "preserve_x" in overrides:
+        out["preserve_x"] = bool(overrides.get("preserve_x"))
+    raw = overrides.get("layers")
+    if isinstance(raw, (list, tuple)) and raw:
+        out["layers"] = tuple(str(x) for x in raw if str(x).strip())
+    return out

netx_api/mcp_remote/topology/layout_ops/mass_field.py

@@ -0,0 +1,591 @@
+"""Per-node / per-edge attract+repulse mass field for soft compose merge.
+
+Replaces uniform exclusive-island rigidity: cores pull hard, rings hold shape,
+chains are tearable and may evolve into rings (raising mass).
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops.dual_units import DualUnit, find_dual_portal_units
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState
+
+# role -> (attract, repulse, mass)
+_ROLE_NODE: dict[str, tuple[float, float, float]] = {
+    "core": (4.0, 1.8, 3.0),
+    "ring": (2.4, 1.2, 1.8),
+    "chain": (0.7, 0.6, 0.5),
+    "free": (1.0, 1.0, 1.0),
+}
+
+_ROLE_EDGE: dict[str, tuple[float, float]] = {
+    "ring": (2.6, 1.0),
+    "chain": (0.5, 0.4),
+    "bridge": (1.2, 0.8),
+    "plain": (1.0, 1.0),
+}
+
+_ROLE_RANK = {"free": 0, "chain": 1, "ring": 2, "core": 3}
+_STAB = {"chain": 0.35, "ring": 1.2, "core": 3.0, "free": 0.7}
+
+
+def _edge_key(a: str, b: str) -> str:
+    return f"{a}|{b}" if a <= b else f"{b}|{a}"
+
+
+def _nest_boost(path_count: int) -> float:
+    extra = max(0, int(path_count) - 2)
+    return 1.0 + 0.15 * float(extra)
+
+
+def annotate_dual_unit(unit: DualUnit) -> dict[str, Any]:
+    """Role tags for one DualUnit (portals→core, paths→ring, tails→chain)."""
+    nest = max(0, len(unit.paths))
+    boost = _nest_boost(nest)
+    nodes: dict[str, dict[str, Any]] = {}
+    edges: dict[str, dict[str, Any]] = {}
+
+    def _set_node(nid: str, role: str) -> None:
+        cur = nodes.get(nid)
+        if cur is None or _ROLE_RANK.get(role, 0) > _ROLE_RANK.get(
+            str(cur.get("role") or "free"), 0
+        ):
+            a, r, m = _ROLE_NODE[role]
+            if role == "ring":
+                a *= boost
+                m *= 1.0 + 0.08 * max(0, nest - 2)
+            nodes[nid] = {
+                "role": role,
+                "attract": round(a, 4),
+                "repulse": round(r, 4),
+                "mass": round(m, 4),
+                "unit_id": unit.unit_id,
+            }
+
+    def _set_edge(u: str, v: str, role: str) -> None:
+        if u == v:
+            return
+        key = _edge_key(u, v)
+        cur = edges.get(key)
+        if cur is None or _ROLE_RANK.get(role, 0) > _ROLE_RANK.get(
+            str(cur.get("role") or "plain"), 0
+        ):
+            ae, re = _ROLE_EDGE[role]
+            if role == "ring":
+                ae *= boost
+            edges[key] = {
+                "role": role,
+                "attract": round(ae, 4),
+                "repulse": round(re, 4),
+                "a": u if u <= v else v,
+                "b": v if u <= v else u,
+            }
+
+    for p in (unit.portal_a, unit.portal_b):
+        _set_node(p, "core")
+    for path in unit.paths:
+        for i, n in enumerate(path):
+            if n in (unit.portal_a, unit.portal_b):
+                _set_node(n, "core")
+            else:
+                _set_node(n, "ring")
+            if i + 1 < len(path):
+                _set_edge(path[i], path[i + 1], "ring")
+    for chain in unit.tails:
+        prev = None
+        # Attach edge from nearest portal/core if chain starts at neighbor — handled
+        # when full graph links are known; here tag chain nodes + consecutive edges.
+        for n in chain:
+            _set_node(n, "chain")
+            if prev is not None:
+                _set_edge(prev, n, "chain")
+            prev = n
+
+    return {
+        "unit_id": unit.unit_id,
+        "nest_depth": nest,
+        "portal_a": unit.portal_a,
+        "portal_b": unit.portal_b,
+        "nodes": nodes,
+        "edges": edges,
+    }
+
+
+def merge_mass_dicts(
+    *parts: dict[str, dict[str, Any]],
+    rank_key: str = "role",
+    rank_map: dict[str, int] | None = None,
+) -> dict[str, dict[str, Any]]:
+    ranks = rank_map or _ROLE_RANK
+    out: dict[str, dict[str, Any]] = {}
+    for part in parts:
+        for k, row in part.items():
+            cur = out.get(k)
+            if cur is None or ranks.get(str(row.get(rank_key) or ""), 0) > ranks.get(
+                str(cur.get(rank_key) or ""), 0
+            ):
+                out[k] = dict(row)
+    return out
+
+
+def _degree_tweak(
+    state: LayoutState,
+    nodes: dict[str, dict[str, Any]],
+) -> None:
+    for n, row in nodes.items():
+        deg = len(state.adj.get(n, ()))
+        layer = str(state.layers.get(n) or "")
+        m = float(row.get("mass") or 1.0)
+        a = float(row.get("attract") or 1.0)
+        if layer in ("core", "agg") or deg >= 8:
+            m *= 1.15
+            a *= 1.08
+        elif deg <= 2 and str(row.get("role")) == "chain":
+            m *= 0.92
+            a *= 0.95
+        row["mass"] = round(m, 4)
+        row["attract"] = round(a, 4)
+
+
+def build_mass_field(
+    state: LayoutState,
+    *,
+    units: list[DualUnit] | None = None,
+    groups: list[dict[str, Any]] | None = None,
+) -> dict[str, Any]:
+    """Build mass_field from dual units (+ optional compose groups).
+
+    Pass ``units=[]`` to skip dual-unit detection (plain FA2 mass tags only).
+    """
+    found = units if units is not None else find_dual_portal_units(state)
+    node_parts: list[dict[str, dict[str, Any]]] = []
+    edge_parts: list[dict[str, dict[str, Any]]] = []
+    unit_rows: list[dict[str, Any]] = []
+    for u in found:
+        ann = annotate_dual_unit(u)
+        unit_rows.append(
+            {
+                "unit_id": ann["unit_id"],
+                "nest_depth": ann["nest_depth"],
+                "portal_a": ann["portal_a"],
+                "portal_b": ann["portal_b"],
+            }
+        )
+        node_parts.append(ann["nodes"])
+        edge_parts.append(ann["edges"])
+
+    nodes = merge_mass_dicts(*node_parts) if node_parts else {}
+    edges = merge_mass_dicts(
+        *edge_parts, rank_key="role", rank_map={"plain": 0, "bridge": 1, "chain": 2, "ring": 3}
+    ) if edge_parts else {}
+
+    # Tag remaining nodes + mark fabric bridges between different group homes.
+    home = home_map_from_groups(groups or [])
+    for n in state.positions:
+        if n not in nodes:
+            nodes[n] = {
+                "role": "free",
+                "attract": 1.0,
+                "repulse": 1.0,
+                "mass": 1.0,
+            }
+    for a, b in state.links:
+        key = _edge_key(a, b)
+        if key in edges:
+            continue
+        ha, hb = home.get(a), home.get(b)
+        role = "bridge" if ha and hb and ha != hb else "plain"
+        ae, re = _ROLE_EDGE[role]
+        edges[key] = {
+            "role": role,
+            "attract": ae,
+            "repulse": re,
+            "a": a if a <= b else b,
+            "b": b if a <= b else a,
+        }
+
+    _degree_tweak(state, nodes)
+
+    soft_groups = []
+    for g in groups or []:
+        key = str(g.get("key") or "")
+        members = [str(x) for x in (g.get("node_ids") or g.get("members") or [])]
+        pivots = [str(x) for x in (g.get("pivots") or [])]
+        cores = [
+            n
+            for n in members
+            if str((nodes.get(n) or {}).get("role")) == "core" or n in pivots
+        ]
+        if not cores:
+            cores = list(pivots)
+        soft_groups.append(
+            {
+                "key": key,
+                "node_ids": members,
+                "pivots": pivots,
+                "cores": cores,
+                "soft": True,
+            }
+        )
+
+    return {
+        "nodes": nodes,
+        "edges": edges,
+        "units": unit_rows,
+        "groups": soft_groups,
+        "tip": (
+            "mass_field: core/ring/chain attract+repulse; use mass_merge "
+            "(not exclusive rigid islands) after compose"
+        ),
+    }
+
+
+def home_map_from_groups(groups: list[dict[str, Any]]) -> dict[str, str]:
+    """First group wins for multi-membership; portals often multi-home."""
+    home: dict[str, str] = {}
+    counts: dict[str, int] = {}
+    for g in groups:
+        key = str(g.get("key") or "")
+        for n in g.get("node_ids") or g.get("members") or []:
+            nid = str(n)
+            counts[nid] = counts.get(nid, 0) + 1
+            if nid not in home:
+                home[nid] = key
+    # Shared portals: keep first home but mark multi via absence of exclusive.
+    return home
+
+
+def group_effective_mass(
+    group: dict[str, Any],
+    nodes: dict[str, dict[str, Any]],
+) -> float:
+    cores = list(group.get("cores") or group.get("pivots") or [])
+    if not cores:
+        # Fall back to top attract members.
+        members = [str(x) for x in (group.get("node_ids") or [])]
+        members.sort(
+            key=lambda n: -float((nodes.get(n) or {}).get("attract") or 0.0)
+        )
+        cores = members[:2]
+    total = 0.0
+    for c in cores:
+        row = nodes.get(c) or {}
+        total += float(row.get("mass") or 1.0) * float(row.get("attract") or 1.0)
+    return max(total, 0.1)
+
+
+def core_centroid(
+    group: dict[str, Any],
+    pos: dict[str, tuple[float, float]],
+    nodes: dict[str, dict[str, Any]],
+) -> tuple[float, float] | None:
+    cores = [c for c in (group.get("cores") or group.get("pivots") or []) if c in pos]
+    if not cores:
+        members = [n for n in (group.get("node_ids") or []) if n in pos]
+        if not members:
+            return None
+        members.sort(
+            key=lambda n: -float((nodes.get(n) or {}).get("attract") or 0.0)
+        )
+        cores = members[:3]
+    if not cores:
+        return None
+    cx = sum(pos[c][0] for c in cores) / len(cores)
+    cy = sum(pos[c][1] for c in cores) / len(cores)
+    return (cx, cy)
+
+
+def evolve_chains_to_rings(
+    state: LayoutState,
+    mass: dict[str, Any],
+) -> dict[str, Any]:
+    """Promote chain nodes that sit on newly detected dual-portal corridors."""
+    units = find_dual_portal_units(state)
+    nodes = dict(mass.get("nodes") or {})
+    edges = dict(mass.get("edges") or {})
+    promoted_n = 0
+    promoted_e = 0
+    for u in units:
+        ann = annotate_dual_unit(u)
+        for nid, row in ann["nodes"].items():
+            if str(row.get("role")) != "ring":
+                continue
+            cur = nodes.get(nid) or {}
+            if str(cur.get("role")) == "chain":
+                nodes[nid] = row
+                promoted_n += 1
+            elif nid not in nodes or _ROLE_RANK.get(
+                str(row.get("role")), 0
+            ) > _ROLE_RANK.get(str(cur.get("role") or "free"), 0):
+                nodes[nid] = row
+        for key, row in ann["edges"].items():
+            if str(row.get("role")) != "ring":
+                continue
+            cur = edges.get(key) or {}
+            if str(cur.get("role")) == "chain":
+                edges[key] = row
+                promoted_e += 1
+            elif key not in edges:
+                edges[key] = row
+    _degree_tweak(state, nodes)
+    out = dict(mass)
+    out["nodes"] = nodes
+    out["edges"] = edges
+    out["evolve"] = {
+        "promoted_nodes": promoted_n,
+        "promoted_edges": promoted_e,
+        "units_n": len(units),
+    }
+    return out
+
+
+def capture_pass(
+    state: LayoutState,
+    mass: dict[str, Any],
+    *,
+    kappa_node: float = 1.25,
+    kappa_block: float = 2.0,
+    rho_ideal: float = 6.0,
+    ideal_len: float = 540.0,
+) -> tuple[dict[str, Any], dict[str, Any]]:
+    """Rewrite soft group membership: steal nodes / whole weak blocks."""
+    groups = [dict(g) for g in (mass.get("groups") or [])]
+    if len(groups) < 2:
+        return mass, {"stolen_nodes": 0, "stolen_blocks": 0}
+    nodes = mass.get("nodes") or {}
+    pos = state.positions
+    # Rebuild member lists as sets
+    by_key: dict[str, dict[str, Any]] = {}
+    for g in groups:
+        key = str(g.get("key") or "")
+        g["node_ids"] = [str(x) for x in (g.get("node_ids") or [])]
+        g["_set"] = set(g["node_ids"])
+        by_key[key] = g
+
+    masses = {k: group_effective_mass(g, nodes) for k, g in by_key.items()}
+    cents = {k: core_centroid(g, pos, nodes) for k, g in by_key.items()}
+
+    # Home: exclusive preferred (membership count==1)
+    count: dict[str, int] = {}
+    for g in groups:
+        for n in g["node_ids"]:
+            count[n] = count.get(n, 0) + 1
+    home: dict[str, str] = {}
+    for g in groups:
+        k = str(g.get("key") or "")
+        for n in g["node_ids"]:
+            if count.get(n, 0) == 1:
+                home[n] = k
+            elif n not in home:
+                home[n] = k
+
+    stolen_nodes = 0
+    for n, gkey in list(home.items()):
+        if count.get(n, 0) > 1:
+            continue  # shared portals stay
+        role = str((nodes.get(n) or {}).get("role") or "free")
+        if role == "core":
+            continue
+        if n not in pos:
+            continue
+        nx, ny = pos[n]
+        hold = masses.get(gkey, 0.1) * _STAB.get(role, 0.7)
+        best_h = None
+        best_pull = 0.0
+        for hk, hc in cents.items():
+            if hk == gkey or hc is None:
+                continue
+            d = math.hypot(nx - hc[0], ny - hc[1])
+            pull = masses.get(hk, 0.1) / (d + 1.0)
+            if pull > best_pull:
+                best_pull = pull
+                best_h = hk
+        if best_h is None:
+            continue
+        if best_pull > kappa_node * max(hold, 1e-6):
+            # Move membership
+            by_key[gkey]["_set"].discard(n)
+            by_key[best_h]["_set"].add(n)
+            home[n] = best_h
+            stolen_nodes += 1
+
+    stolen_blocks = 0
+    # Block capture: weak whole group re-parented (merge members into strong).
+    keys = list(by_key.keys())
+    for i, wk in enumerate(keys):
+        for sk in keys[i + 1 :]:
+            for weak, strong in ((wk, sk), (sk, wk)):
+                mw, ms = masses.get(weak, 0.1), masses.get(strong, 0.1)
+                if ms < kappa_block * mw:
+                    continue
+                cw, cs = cents.get(weak), cents.get(strong)
+                if cw is None or cs is None:
+                    continue
+                dist = math.hypot(cw[0] - cs[0], cw[1] - cs[1])
+                if dist > rho_ideal * ideal_len:
+                    continue
+                # Contact: any bridge edge or shared portal
+                contact = False
+                wset = by_key[weak]["_set"]
+                sset = by_key[strong]["_set"]
+                if wset & sset:
+                    contact = True
+                else:
+                    for a, b in state.links:
+                        if (a in wset and b in sset) or (b in wset and a in sset):
+                            contact = True
+                            break
+                if not contact:
+                    continue
+                # Absorb exclusive weak members into strong (keep soft parent key
+                # on strong; clear weak exclusive into strong set).
+                moved = [n for n in list(wset) if count.get(n, 0) == 1]
+                if len(moved) < 2:
+                    continue
+                for n in moved:
+                    by_key[weak]["_set"].discard(n)
+                    by_key[strong]["_set"].add(n)
+                    home[n] = strong
+                stolen_blocks += 1
+                masses[strong] = group_effective_mass(by_key[strong], nodes)
+                masses[weak] = group_effective_mass(by_key[weak], nodes)
+                cents[strong] = core_centroid(by_key[strong], pos, nodes)
+                cents[weak] = core_centroid(by_key[weak], pos, nodes)
+                break
+
+    new_groups = []
+    for g in groups:
+        key = str(g.get("key") or "")
+        members = sorted(by_key[key]["_set"])
+        pivots = [p for p in (g.get("pivots") or []) if p in by_key[key]["_set"]]
+        cores = [
+            n
+            for n in members
+            if str((nodes.get(n) or {}).get("role")) == "core" or n in pivots
+        ]
+        new_groups.append(
+            {
+                "key": key,
+                "node_ids": members,
+                "pivots": pivots,
+                "cores": cores or pivots,
+                "soft": True,
+            }
+        )
+
+    out = dict(mass)
+    out["groups"] = new_groups
+    report = {
+        "stolen_nodes": stolen_nodes,
+        "stolen_blocks": stolen_blocks,
+        "kappa_node": kappa_node,
+        "kappa_block": kappa_block,
+    }
+    return out, report
+
+
+def geo_score(
+    state: LayoutState,
+    mass: dict[str, Any],
+) -> float:
+    """Higher is better: ring edge length consistency + core compactness."""
+    nodes = mass.get("nodes") or {}
+    edges = mass.get("edges") or {}
+    pos = state.positions
+    ring_lens: list[float] = []
+    chain_lens: list[float] = []
+    for key, row in edges.items():
+        a, b = str(row.get("a") or ""), str(row.get("b") or "")
+        if a not in pos or b not in pos:
+            # key form
+            if "|" in key:
+                a, b = key.split("|", 1)
+            else:
+                continue
+        if a not in pos or b not in pos:
+            continue
+        L = math.hypot(pos[a][0] - pos[b][0], pos[a][1] - pos[b][1])
+        role = str(row.get("role") or "plain")
+        if role == "ring":
+            ring_lens.append(L)
+        elif role == "chain":
+            chain_lens.append(L)
+
+    score = 0.0
+    if len(ring_lens) >= 2:
+        mean = sum(ring_lens) / len(ring_lens)
+        var = sum((x - mean) ** 2 for x in ring_lens) / len(ring_lens)
+        score += 100.0 / (1.0 + math.sqrt(var) / max(mean, 1.0))
+    elif ring_lens:
+        score += 40.0
+
+    # Penalize very long chains (should be tearable / reattached).
+    if chain_lens:
+        mean_c = sum(chain_lens) / len(chain_lens)
+        score -= min(40.0, mean_c / 80.0)
+
+    # Core neighborhood compactness
+    cores = [n for n, r in nodes.items() if str(r.get("role")) == "core" and n in pos]
+    if len(cores) >= 2:
+        cx = sum(pos[n][0] for n in cores) / len(cores)
+        cy = sum(pos[n][1] for n in cores) / len(cores)
+        spread = sum(math.hypot(pos[n][0] - cx, pos[n][1] - cy) for n in cores) / len(
+            cores
+        )
+        score += 80.0 / (1.0 + spread / 400.0)
+
+    return float(score)
+
+
+def attach_mass_to_compose_meta(
+    meta: dict[str, Any],
+    mass: dict[str, Any],
+) -> dict[str, Any]:
+    """Write mass_groups + mass_field onto compose meta (keep rigid_groups)."""
+    out = dict(meta)
+    groups = mass.get("groups") or []
+    if not groups and out.get("rigid_groups"):
+        groups = [
+            {**g, "soft": True, "cores": list(g.get("pivots") or [])}
+            for g in out["rigid_groups"]
+            if isinstance(g, dict)
+        ]
+        mass = dict(mass)
+        mass["groups"] = groups
+    out["mass_groups"] = groups
+    out["mass_field"] = {
+        "nodes_n": len(mass.get("nodes") or {}),
+        "edges_n": len(mass.get("edges") or {}),
+        "units": mass.get("units") or [],
+        "tip": mass.get("tip"),
+    }
+    # Full field stored at state.meta["mass_field"] by caller.
+    out["soft"] = True
+    return out
+
+
+def groups_from_mass_or_rigid(meta: dict[str, Any] | None) -> list[dict[str, Any]]:
+    """Prefer soft mass_groups, fall back to rigid_groups."""
+    if not meta:
+        return []
+    raw = meta.get("mass_groups") or meta.get("rigid_groups") or []
+    out: list[dict[str, Any]] = []
+    for g in raw:
+        if not isinstance(g, dict):
+            continue
+        nodes = [str(x) for x in (g.get("node_ids") or []) if str(x)]
+        if len(nodes) < 2:
+            continue
+        pivots = [str(x) for x in (g.get("pivots") or []) if str(x)]
+        out.append(
+            {
+                "key": str(g.get("key") or ""),
+                "node_ids": nodes,
+                "pivots": pivots,
+                "cores": [str(x) for x in (g.get("cores") or pivots) if str(x)],
+                "soft": bool(g.get("soft", True)),
+            }
+        )
+    return out

netx_api/mcp_remote/topology/layout_ops/min_rings.py

@@ -0,0 +1,372 @@
+"""Dual-hub minimal-ring layering (PLAU↔ATP style).
+
+When two aggregation hubs share ≥2 nearly interior-disjoint corridors,
+nest those paths on alternating ellipse bands and park leftover side
+chains in free left/right sectors. Preferable to per-AN petal pack for
+agg_bar metro rings (0-crossing when the graph is path-planar).
+"""
+
+from __future__ import annotations
+
+import math
+from collections import defaultdict, deque
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _all_simple_paths(
+    src: str,
+    dst: str,
+    adj: dict[str, set[str]],
+    *,
+    cutoff: int = 16,
+    forbid: set[str] | None = None,
+) -> list[list[str]]:
+    forbid = set(forbid or ())
+    paths: list[list[str]] = []
+    stack: list[tuple[str, list[str]]] = [(src, [src])]
+    while stack:
+        u, path = stack.pop()
+        if len(path) > cutoff:
+            continue
+        for v in adj.get(u, ()):
+            if v in path or v in forbid:
+                continue
+            np = path + [v]
+            if v == dst:
+                paths.append(np)
+            else:
+                stack.append((v, np))
+    return paths
+
+
+def _path_core(path: list[str]) -> set[str]:
+    """Exclusive midpoints (drop portal-adjacent hops that rings often share)."""
+    mid = path[1:-1]
+    if not mid:
+        return set()
+    if len(mid) <= 2:
+        return set(mid)
+    return set(mid[1:-1])
+
+
+def cover_hub_paths(
+    hub_a: str,
+    hub_b: str,
+    adj: dict[str, set[str]],
+    names: dict[str, str],
+    *,
+    cutoff: int = 16,
+    forbid: set[str] | None = None,
+) -> list[list[str]]:
+    """Greedy path cover between hubs; cores disjoint (portal-adjacent may share)."""
+    paths = _all_simple_paths(hub_a, hub_b, adj, cutoff=cutoff, forbid=forbid)
+    paths = [p for p in paths if p[1:-1]]  # skip direct hub-hub
+    paths.sort(key=lambda p: (len(p), "".join(names.get(x, x) for x in p)))
+    picked: list[list[str]] = []
+    used_core: set[str] = set()
+    for p in paths:
+        core = _path_core(p)
+        if not core or core & used_core:
+            continue
+        picked.append(p)
+        used_core |= core
+    picked.sort(key=len)
+    return picked
+
+
+def pick_dual_hubs(state: LayoutState) -> tuple[str, str] | None:
+    """Top two agg (else core) hubs by degree; need both present in graph."""
+    cand = [
+        n
+        for n, ly in state.layers.items()
+        if ly == "agg" and n in state.adj
+    ]
+    if len(cand) < 2:
+        cand = [
+            n
+            for n, ly in state.layers.items()
+            if ly in ("agg", "core") and n in state.adj
+        ]
+    if len(cand) < 2:
+        return None
+    cand.sort(
+        key=lambda n: (-len(state.adj.get(n, ())), state.names.get(n, n))
+    )
+    a, b = cand[0], cand[1]
+    if a == b:
+        return None
+    # Prefer name-stable left/right
+    if state.names.get(a, a) > state.names.get(b, b):
+        a, b = b, a
+    return a, b
+
+
+def min_rings_eligible(
+    state: LayoutState,
+    *,
+    min_paths: int = 2,
+    min_cover_frac: float = 0.35,
+) -> dict[str, Any] | None:
+    """Return plan dict if dual-hub path cover is worth running."""
+    hubs = pick_dual_hubs(state)
+    if not hubs:
+        return None
+    hub_a, hub_b = hubs
+    # Ignore other high-layer nodes as path interiors (orphan ANs park later).
+    other_hubs = {
+        n
+        for n, ly in state.layers.items()
+        if ly in ("agg", "core") and n not in (hub_a, hub_b)
+    }
+    paths = cover_hub_paths(
+        hub_a, hub_b, state.adj, state.names, forbid=other_hubs
+    )
+    if len(paths) < min_paths:
+        return None
+    used = {n for p in paths for n in p[1:-1]}
+    access = {n for n, ly in state.layers.items() if ly == "access"}
+    if not access:
+        return None
+    cover = len(used & access) / max(1, len(access))
+    # Low cover with many paths is still a star/hub petal job (e.g. BTM),
+    # not dual-hub corridor nesting — fall through to ume_petals.
+    if cover < min_cover_frac:
+        return None
+    return {
+        "hub_a": hub_a,
+        "hub_b": hub_b,
+        "paths": paths,
+        "cover_frac": cover,
+        "path_count": len(paths),
+    }
+
+
+def _comps(nodes: set[str], adj: dict[str, set[str]], names: dict[str, str]) -> list[list[str]]:
+    seen: set[str] = set()
+    out: list[list[str]] = []
+    for n in sorted(nodes, key=lambda x: names.get(x, x)):
+        if n in seen:
+            continue
+        q = deque([n])
+        seen.add(n)
+        c: list[str] = []
+        while q:
+            u = q.popleft()
+            c.append(u)
+            for v in adj.get(u, ()):
+                if v in nodes and v not in seen:
+                    seen.add(v)
+                    q.append(v)
+        out.append(c)
+    return out
+
+
+def _attach_of(
+    comp: list[str], on: set[str], adj: dict[str, set[str]], fallback: str
+) -> tuple[str, str]:
+    for n in comp:
+        for v in adj.get(n, ()):
+            if v in on:
+                return v, n
+    return fallback, comp[0]
+
+
+def _order_chain(
+    comp: list[str], start: str, adj: dict[str, set[str]], names: dict[str, str]
+) -> list[str]:
+    s = set(comp)
+    if start not in s:
+        start = sorted(comp, key=lambda n: names.get(n, n))[0]
+    ordered = [start]
+    seen = {start}
+    prev = None
+    cur = start
+    while len(ordered) < len(comp):
+        nbs = [v for v in adj.get(cur, ()) if v in s and v not in seen]
+        if not nbs:
+            rest = [n for n in comp if n not in seen]
+            if not rest:
+                break
+            cur = sorted(rest, key=lambda n: names.get(n, n))[0]
+            ordered.append(cur)
+            seen.add(cur)
+            prev = None
+            continue
+        nbs.sort(
+            key=lambda v: (
+                len([x for x in adj.get(v, ()) if x in s]),
+                names.get(v, v),
+            )
+        )
+        if prev in nbs and len(nbs) > 1:
+            nbs = [v for v in nbs if v != prev] + [prev]
+        prev, cur = cur, nbs[0]
+        ordered.append(cur)
+        seen.add(cur)
+    return ordered
+
+
+def layout_min_rings_positions(
+    state: LayoutState,
+    params: LayoutParams,
+    plan: dict[str, Any] | None = None,
+) -> dict[str, tuple[float, float]] | None:
+    """Compute positions; None if plan ineligible."""
+    plan = plan or min_rings_eligible(state)
+    if not plan:
+        return None
+    hub_a = str(plan["hub_a"])
+    hub_b = str(plan["hub_b"])
+    paths: list[list[str]] = list(plan["paths"])  # type: ignore[arg-type]
+    adj, names, layers = state.adj, state.names, state.layers
+
+    rx = max(params.an_gap * 2.5, params.pitch * 7.0, 1100.0)
+    ry_step = max(params.side * 1.6, params.lane * 0.9, 280.0)
+    pitch = max(params.pitch * 0.8, 160.0)
+    base_r = max(params.side * 1.3, 220.0)
+
+    # Prefer denser bands when graph is small.
+    n_access = sum(1 for ly in layers.values() if ly == "access")
+    if n_access <= 55:
+        ry_step = min(ry_step, 320.0)
+        pitch = min(pitch, 170.0)
+
+    pos: dict[str, tuple[float, float]] = {
+        hub_a: (-rx, 0.0),
+        hub_b: (rx, 0.0),
+    }
+    # Nodes that appear as portal-adjacent on ≥2 paths sit on the hub chord.
+    hop_count: dict[str, int] = defaultdict(int)
+    for p in paths:
+        mid = p[1:-1]
+        if not mid:
+            continue
+        hop_count[mid[0]] += 1
+        if len(mid) > 1:
+            hop_count[mid[-1]] += 1
+    shared_hops = {n for n, c in hop_count.items() if c >= 2}
+
+    used: set[str] = set()
+    for i, p in enumerate(paths):
+        side = 1 if i % 2 == 0 else -1
+        ry = ry_step * ((i // 2) + 1)
+        m = len(p)
+        for j, n in enumerate(p):
+            if n in (hub_a, hub_b):
+                continue
+            t = j / (m - 1) if m > 1 else 0.5
+            if n in shared_hops:
+                # Keep on axis so corridor arcs do not cross the hub chord.
+                if n not in pos:
+                    pos[n] = (-rx + 2 * rx * t, 0.0)
+                used.add(n)
+                continue
+            ang = math.pi * (1 - t)
+            if side < 0:
+                ang = -ang
+            pos[n] = (rx * math.cos(ang), ry * math.sin(ang))
+            used.add(n)
+    on = used | {hub_a, hub_b}
+
+    # Orphan hubs (other agg/core) + leftover access comps → free sectors
+    leftovers = {n for n in names if n not in on}
+    orphan_hubs = [
+        n
+        for n in leftovers
+        if layers.get(n) in ("agg", "core")
+    ]
+    left_access = leftovers - set(orphan_hubs)
+    lcomps = _comps(left_access, adj, names)
+
+    by_hub: dict[str, list[tuple[list[str], str]]] = defaultdict(list)
+    for c in lcomps:
+        att, seed = _attach_of(c, on, adj, hub_a)
+        by_hub[att].append((c, seed))
+
+    left_angs = [math.pi, 2.55, 3.55, -2.55]
+    right_angs = [0.0, 0.5, -0.5, 0.95]
+    for hub, groups in by_hub.items():
+        groups = sorted(groups, key=lambda g: -len(g[0]))
+        if hub == hub_a:
+            angs = left_angs
+        elif hub == hub_b:
+            angs = right_angs
+        else:
+            angs = None
+        hx, hy = pos.get(hub, (0.0, 0.0))
+        for gi, (comp, seed) in enumerate(groups):
+            if angs is not None:
+                ang = angs[gi % len(angs)]
+            else:
+                ang = math.atan2(hy, hx or 1e-9) + (gi - (len(groups) - 1) / 2) * 0.25
+            for i, n in enumerate(_order_chain(comp, seed, adj, names)):
+                r = base_r + i * pitch
+                pos[n] = (hx + r * math.cos(ang), hy + r * math.sin(ang))
+
+    # Park orphan hubs above/below clear of bands
+    ymin = min((y for _, y in pos.values()), default=0.0)
+    ymax = max((y for _, y in pos.values()), default=0.0)
+    for i, n in enumerate(sorted(orphan_hubs, key=lambda x: names.get(x, x))):
+        pos[n] = (80.0 * (i - (len(orphan_hubs) - 1) / 2), ymin - 280.0 - i * 40)
+
+    for n in names:
+        if n not in pos:
+            pos[n] = (0.0, ymax + 200.0)
+
+    # Normalize to positive margin
+    xs = [p[0] for p in pos.values()]
+    ys = [p[1] for p in pos.values()]
+    x0, y0 = min(xs), min(ys)
+    pad = params.margin
+    return {n: (x - x0 + pad, y - y0 + pad) for n, (x, y) in pos.items()}
+
+
+def build_min_ring_skeleton(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult | None:
+    """Return OpResult if dual-hub min-rings applies; else None (caller falls back)."""
+    params = params or LayoutParams()
+    plan = min_rings_eligible(state)
+    if not plan:
+        return None
+    pos = layout_min_rings_positions(state, params, plan)
+    if not pos:
+        return None
+
+    st = state.copy()
+    st.positions = pos
+    st.pinned = {plan["hub_a"], plan["hub_b"]}
+    # Also pin path portals' immediate corridor order lightly via spine
+    spine: set[str] = {plan["hub_a"], plan["hub_b"]}
+    for p in plan["paths"]:
+        spine.update(p)
+    st.spine = spine
+    st.last_moved = set(pos.keys())
+    cross = count_edge_crossings(pos, st.links)
+    st.meta["rings_mode"] = "min_rings"
+    st.meta["min_rings"] = {
+        "hub_a": plan["hub_a"],
+        "hub_b": plan["hub_b"],
+        "path_count": plan["path_count"],
+        "cover_frac": round(float(plan["cover_frac"]), 3),
+        "edge_crossings": cross,
+    }
+    st.meta["components"] = 1
+    return OpResult(
+        state=st,
+        moved=set(pos.keys()),
+        op="build_min_ring_skeleton",
+        params={
+            "mode": "min_rings",
+            "path_count": plan["path_count"],
+            "cover_frac": plan["cover_frac"],
+            "hubs": [plan["hub_a"], plan["hub_b"]],
+        },
+        note=(
+            f"min-rings dual-hub paths={plan['path_count']} "
+            f"cover={plan['cover_frac']:.2f} cross={cross}"
+        ),
+    )

netx_api/mcp_remote/topology/layout_ops/partition.py

@@ -0,0 +1,523 @@
+"""Soft partitioning for layout init — own algorithms first, igraph optional.
+
+Design (partial igraph use):
+  1. Hard blocks = connected components (existing ``list_blocks(component)``).
+  2. Soft blocks inside a giant CC = **hub-seeded territory** (our BFS; default).
+  3. Optional igraph:
+     - ``leiden``: only refine leftover / oversized mesh pockets (hubs pinned).
+     - ``fr_pack``: place soft-block *centers* on a supergraph (not final metro coords).
+  4. Per-block geometry stays ours: beam / stub petals / chain spine / ume_petals.
+
+Never use force-directed as the final access layout.
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict, deque
+from dataclasses import dataclass
+from typing import Any, Iterable
+
+from netx_api.mcp_remote.topology.layout_ops.graph_util import connected_components
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+# Preferred soft-block step when packing centers (px).
+_BLOCK_GAP = 420.0
+
+
+def resolve_block_mode(state: LayoutState, mode: str | None = None) -> str:
+    """Resolve ``auto`` → hub_territory when one giant CC has multiple hubs."""
+    m = (mode or "component").strip().lower()
+    if m != "auto":
+        return m
+    active = {
+        n
+        for n in state.positions
+        if state.layers.get(n) in ("core", "agg", "access")
+    }
+    comps = connected_components(active, state.adj) if active else []
+    hubs = pick_hub_seeds(state)
+    big = max((len(c) for c in comps), default=0)
+    if len(comps) <= 2 and big >= 40 and len(hubs) >= 2:
+        return "hub_territory"
+    return "component"
+
+
+def igraph_available() -> bool:
+    try:
+        import igraph  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
+@dataclass(frozen=True)
+class SoftBlock:
+    block_id: int
+    hub_id: str | None
+    method: str
+    node_ids: tuple[str, ...]
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "block_id": self.block_id,
+            "hub_id": self.hub_id,
+            "method": self.method,
+            "size": len(self.node_ids),
+            "node_ids": list(self.node_ids)[:80],
+            "node_count": len(self.node_ids),
+        }
+
+
+def pick_hub_seeds(
+    state: LayoutState,
+    *,
+    min_degree: int = 2,
+    max_hubs: int = 24,
+) -> list[str]:
+    """core/agg hubs by degree; fall back to high-degree nodes."""
+    ids = list(state.positions.keys())
+    layered = [
+        n
+        for n in ids
+        if state.layers.get(n) in ("core", "agg")
+        and len(state.adj.get(n, ())) >= min_degree
+    ]
+    if layered:
+        layered.sort(
+            key=lambda n: (-len(state.adj.get(n, ())), state.names.get(n, n))
+        )
+        return layered[:max_hubs]
+    ranked = sorted(
+        ids,
+        key=lambda n: (-len(state.adj.get(n, ())), state.names.get(n, n)),
+    )
+    return [n for n in ranked if len(state.adj.get(n, ())) >= max(3, min_degree)][
+        :max_hubs
+    ]
+
+
+def hub_territory_partition(
+    state: LayoutState,
+    *,
+    hubs: Iterable[str] | None = None,
+) -> list[SoftBlock]:
+    """Multi-source BFS from each hub's 1-hop stubs — one soft block per hub.
+
+    Hubs themselves are included in their block. Nodes claimed by the nearest
+    stub (first visit). Leftovers become CC soft-blocks (``method=leftover``).
+    """
+    hub_list = [h for h in (hubs or pick_hub_seeds(state)) if h in state.positions]
+    if not hub_list:
+        comps = connected_components(set(state.positions), state.adj)
+        return [
+            SoftBlock(i, None, "component", tuple(sorted(c)))
+            for i, c in enumerate(comps)
+            if c
+        ]
+
+    pinned = set(hub_list)
+    owner: dict[str, str] = {}
+    q: deque[str] = deque()
+    for h in hub_list:
+        for stub in sorted(state.adj.get(h, ()), key=lambda i: state.names.get(i, i)):
+            if stub in pinned or stub in owner:
+                continue
+            owner[stub] = h
+            q.append(stub)
+    while q:
+        u = q.popleft()
+        h = owner[u]
+        for v in state.adj.get(u, ()):
+            if v in pinned or v in owner:
+                continue
+            owner[v] = h
+            q.append(v)
+
+    by_hub: dict[str, list[str]] = {h: [h] for h in hub_list}
+    for nid, h in owner.items():
+        by_hub.setdefault(h, [h]).append(nid)
+
+    blocks: list[SoftBlock] = []
+    claimed = set(hub_list) | set(owner)
+    for h in hub_list:
+        nodes = tuple(sorted(set(by_hub.get(h, [h])), key=lambda i: state.names.get(i, i)))
+        blocks.append(SoftBlock(len(blocks), h, "hub_territory", nodes))
+
+    rest = set(state.positions) - claimed
+    if rest:
+        for comp in connected_components(rest, state.adj):
+            if not comp:
+                continue
+            blocks.append(
+                SoftBlock(
+                    len(blocks),
+                    None,
+                    "leftover",
+                    tuple(sorted(comp, key=lambda i: state.names.get(i, i))),
+                )
+            )
+    return blocks
+
+
+def _leiden_membership(
+    node_ids: list[str],
+    adj: dict[str, set[str]],
+    *,
+    resolution: float = 1.0,
+) -> list[int] | None:
+    if not igraph_available() or len(node_ids) < 6:
+        return None
+    import igraph as ig
+
+    idx = {n: i for i, n in enumerate(node_ids)}
+    edges: list[tuple[int, int]] = []
+    seen: set[tuple[int, int]] = set()
+    for a in node_ids:
+        for b in adj.get(a, ()):
+            if b not in idx or a >= b:
+                continue
+            ea, eb = idx[a], idx[b]
+            key = (ea, eb) if ea < eb else (eb, ea)
+            if key in seen:
+                continue
+            seen.add(key)
+            edges.append(key)
+    if not edges:
+        return None
+    g = ig.Graph(n=len(node_ids), edges=edges, directed=False)
+    try:
+        part = g.community_leiden(
+            objective_function="modularity",
+            resolution=resolution,
+            n_iterations=2,
+        )
+    except Exception:
+        try:
+            part = g.community_multilevel()
+        except Exception:
+            return None
+    return list(part.membership)
+
+
+def leiden_refine_leftovers(
+    state: LayoutState,
+    blocks: list[SoftBlock],
+    *,
+    resolution: float = 0.8,
+    min_split_size: int = 12,
+) -> list[SoftBlock]:
+    """Optionally split leftover/mesh pockets with Leiden; hubs stay untouched.
+
+    If igraph is missing, returns ``blocks`` unchanged.
+    """
+    if not igraph_available():
+        return blocks
+    out: list[SoftBlock] = []
+    for b in blocks:
+        if b.method != "leftover" or len(b.node_ids) < min_split_size:
+            out.append(SoftBlock(len(out), b.hub_id, b.method, b.node_ids))
+            continue
+        nodes = list(b.node_ids)
+        memb = _leiden_membership(nodes, state.adj, resolution=resolution)
+        if memb is None:
+            out.append(SoftBlock(len(out), b.hub_id, b.method, b.node_ids))
+            continue
+        groups: dict[int, list[str]] = defaultdict(list)
+        for n, m in zip(nodes, memb):
+            groups[int(m)].append(n)
+        if len(groups) <= 1:
+            out.append(SoftBlock(len(out), b.hub_id, b.method, b.node_ids))
+            continue
+        for gid in sorted(groups.keys()):
+            ids = tuple(sorted(groups[gid], key=lambda i: state.names.get(i, i)))
+            out.append(SoftBlock(len(out), None, "leiden", ids))
+    return out
+
+
+def partition_soft_blocks(
+    state: LayoutState,
+    *,
+    mode: str = "hub_territory",
+    hubs: Iterable[str] | None = None,
+    leiden_resolution: float = 0.8,
+) -> list[SoftBlock]:
+    """Public entry: ``hub_territory`` (default) | ``leiden`` | ``soft``.
+
+    - hub_territory: our seeded BFS only
+    - leiden: hub territories + Leiden on leftovers (needs igraph)
+    - soft: alias of leiden if igraph else hub_territory
+    """
+    m = (mode or "hub_territory").strip().lower()
+    base = hub_territory_partition(state, hubs=hubs)
+    if m in {"leiden", "soft"} and igraph_available():
+        return leiden_refine_leftovers(
+            state, base, resolution=leiden_resolution
+        )
+    return base
+
+
+def soft_blocks_as_sets(blocks: list[SoftBlock]) -> list[set[str]]:
+    return [set(b.node_ids) for b in blocks if b.node_ids]
+
+
+def _block_centroids(
+    state: LayoutState, blocks: list[SoftBlock]
+) -> dict[int, tuple[float, float]]:
+    out: dict[int, tuple[float, float]] = {}
+    for b in blocks:
+        pts = [state.positions[n] for n in b.node_ids if n in state.positions]
+        if not pts:
+            continue
+        out[b.block_id] = (
+            sum(p[0] for p in pts) / len(pts),
+            sum(p[1] for p in pts) / len(pts),
+        )
+    return out
+
+
+def _centroid_spread(centroids: dict[int, tuple[float, float]]) -> float:
+    """Max pairwise distance among centroids (0 if <2)."""
+    ids = list(centroids)
+    if len(ids) < 2:
+        return 0.0
+    best = 0.0
+    for i, a in enumerate(ids):
+        ax, ay = centroids[a]
+        for b in ids[i + 1 :]:
+            bx, by = centroids[b]
+            d = ((ax - bx) ** 2 + (ay - by) ** 2) ** 0.5
+            if d > best:
+                best = d
+    return best
+
+
+def _median_nn(centroids: dict[int, tuple[float, float]]) -> float:
+    ids = list(centroids)
+    if len(ids) < 2:
+        return 0.0
+    nns: list[float] = []
+    for a in ids:
+        ax, ay = centroids[a]
+        best = None
+        for b in ids:
+            if a == b:
+                continue
+            bx, by = centroids[b]
+            d = ((ax - bx) ** 2 + (ay - by) ** 2) ** 0.5
+            if best is None or d < best:
+                best = d
+        if best is not None:
+            nns.append(best)
+    if not nns:
+        return 0.0
+    nns.sort()
+    return nns[len(nns) // 2]
+
+
+def _pack_centers_preserve(
+    centroids: dict[int, tuple[float, float]],
+    *,
+    gap: float,
+) -> dict[int, tuple[float, float]]:
+    """Keep relative constellation; scale about COM so median NN ≈ gap."""
+    if len(centroids) < 2:
+        return dict(centroids)
+    cx = sum(p[0] for p in centroids.values()) / len(centroids)
+    cy = sum(p[1] for p in centroids.values()) / len(centroids)
+    med = _median_nn(centroids)
+    if med < 1e-6:
+        return dict(centroids)
+    # Only densify when already too sparse; never explode a tight pack here.
+    scale = min(1.0, gap / med)
+    if abs(scale - 1.0) < 1e-3:
+        return dict(centroids)
+    return {
+        bid: (cx + (x - cx) * scale, cy + (y - cy) * scale)
+        for bid, (x, y) in centroids.items()
+    }
+
+
+def _pack_centers_fr_or_grid(
+    state: LayoutState,
+    blocks: list[SoftBlock],
+    *,
+    gap: float,
+) -> dict[int, tuple[float, float]]:
+    """Fresh placement when centroids are collapsed (init / stacked)."""
+    id_of = {}
+    for b in blocks:
+        for n in b.node_ids:
+            id_of[n] = b.block_id
+    cut: dict[tuple[int, int], int] = defaultdict(int)
+    for a, b in state.links:
+        ia, ib = id_of.get(a), id_of.get(b)
+        if ia is None or ib is None or ia == ib:
+            continue
+        key = (ia, ib) if ia < ib else (ib, ia)
+        cut[key] += 1
+
+    n = len(blocks)
+    if igraph_available() and n >= 2 and cut:
+        import igraph as ig
+
+        edges = list(cut.keys())
+        weights = [float(cut[e]) for e in edges]
+        g = ig.Graph(n=n, edges=edges, directed=False)
+        try:
+            layout = g.layout_fruchterman_reingold(weights=weights, niter=200)
+        except Exception:
+            layout = g.layout_fruchterman_reingold(niter=200)
+        xs = [float(p[0]) for p in layout]
+        ys = [float(p[1]) for p in layout]
+        minx, maxx = min(xs), max(xs)
+        miny, maxy = min(ys), max(ys)
+        span = max(maxx - minx, maxy - miny, 1e-6)
+        target = gap * max(2.0, n**0.5)
+        scale = target / span
+        return {
+            blocks[i].block_id: ((xs[i] - minx) * scale, (ys[i] - miny) * scale)
+            for i in range(n)
+        }
+
+    cols = max(1, int(n**0.5 + 0.999))
+    out: dict[int, tuple[float, float]] = {}
+    for i, b in enumerate(blocks):
+        r, c = divmod(i, cols)
+        out[b.block_id] = (c * gap, r * gap)
+    return out
+
+
+def pack_block_centers(
+    state: LayoutState,
+    blocks: list[SoftBlock],
+    *,
+    gap: float = _BLOCK_GAP,
+    strategy: str = "auto",
+) -> dict[int, tuple[float, float]]:
+    """Suggest soft-block centroids (init only; does not move nodes).
+
+    ``strategy``:
+      - ``preserve``: keep current relative centroids; densify so median NN≈gap
+      - ``grid`` / ``fr``: ignore current world placement (FR if igraph else grid)
+      - ``auto``: if centroids already spread (≥0.75·gap), preserve; else FR/grid
+    """
+    if not blocks:
+        return {}
+    centroids = _block_centroids(state, blocks)
+    if len(centroids) < 2:
+        return centroids
+
+    strat = (strategy or "auto").strip().lower()
+    spread = _centroid_spread(centroids)
+    if strat == "preserve" or (strat == "auto" and spread >= gap * 0.75):
+        return _pack_centers_preserve(centroids, gap=gap)
+    return _pack_centers_fr_or_grid(state, blocks, gap=gap)
+
+
+def partition_report(state: LayoutState, *, mode: str = "soft") -> dict[str, Any]:
+    """Structure-facing summary for analyzeTopologyViewLayout."""
+    blocks = partition_soft_blocks(state, mode=mode)
+    centers = pack_block_centers(state, blocks)
+    return {
+        "mode": mode if mode != "soft" else ("leiden" if igraph_available() else "hub_territory"),
+        "igraph": igraph_available(),
+        "block_count": len(blocks),
+        "blocks": [
+            {
+                **b.as_dict(),
+                "center_hint": (
+                    [round(centers[b.block_id][0], 1), round(centers[b.block_id][1], 1)]
+                    if b.block_id in centers
+                    else None
+                ),
+            }
+            for b in blocks[:40]
+        ],
+        "tip": (
+            "Soft blocks for init only: lay each hub territory / leftover with our "
+            "petal/spine algorithms; use center_hint to pack blocks. "
+            "igraph Leiden/FR are optional extras when available."
+        ),
+    }
+
+
+def pack_soft_blocks(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    mode: str = "hub_territory",
+    gap: float | None = None,
+    strategy: str = "auto",
+) -> OpResult:
+    """Rigid-translate each soft block so centroids match ``pack_block_centers``.
+
+    Init packing only — does not reshape petals inside a block. No-op if <2 blocks.
+    ``strategy=auto`` preserves an already-spread constellation (densify only);
+    collapsed stacks still get FR/grid separation.
+    """
+    del params  # reserved for future spacing knobs
+    st = state.copy()
+    raw = (mode or "hub_territory").strip().lower()
+    resolved = resolve_block_mode(st, raw) if raw == "auto" else raw
+    if resolved == "component":
+        resolved = "hub_territory"
+    blocks = partition_soft_blocks(st, mode=resolved)
+    if len(blocks) < 2:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="pack_soft_blocks",
+            params={"mode": resolved, "blocks_n": len(blocks)},
+            note="skip soft pack (<2 blocks)",
+        )
+    g = float(gap or _BLOCK_GAP)
+    strat = (strategy or "auto").strip().lower()
+    before = _block_centroids(st, blocks)
+    targets = pack_block_centers(st, blocks, gap=g, strategy=strat)
+    used = (
+        "preserve"
+        if strat == "preserve"
+        or (strat == "auto" and _centroid_spread(before) >= g * 0.75)
+        else ("fr" if igraph_available() else "grid")
+    )
+    moved: set[str] = set()
+    for b in blocks:
+        tid = b.block_id
+        if tid not in targets or not b.node_ids:
+            continue
+        pts = [st.positions[n] for n in b.node_ids if n in st.positions]
+        if not pts:
+            continue
+        cx = sum(p[0] for p in pts) / len(pts)
+        cy = sum(p[1] for p in pts) / len(pts)
+        tx, ty = targets[tid]
+        dx, dy = tx - cx, ty - cy
+        if abs(dx) < 1e-6 and abs(dy) < 1e-6:
+            continue
+        for n in b.node_ids:
+            if n not in st.positions:
+                continue
+            x, y = st.positions[n]
+            st.positions[n] = (x + dx, y + dy)
+            moved.add(n)
+    st.meta["soft_pack"] = {
+        "mode": resolved,
+        "strategy": used,
+        "blocks_n": len(blocks),
+        "moved_n": len(moved),
+        "igraph_centers": igraph_available(),
+    }
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="pack_soft_blocks",
+        params={
+            "mode": resolved,
+            "strategy": used,
+            "blocks_n": len(blocks),
+            "moved_n": len(moved),
+        },
+        note=f"packed soft blocks={len(blocks)} strategy={used} moved={len(moved)}",
+    )

netx_api/mcp_remote/topology/layout_ops/pin_beam.py

@@ -0,0 +1,252 @@
+"""Stage-2: pin core horizontal beam + agg row via rigid soft-block translates."""
+
+from __future__ import annotations
+
+from collections import deque
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local
+from netx_api.mcp_remote.topology.layout_ops.partition import partition_soft_blocks
+from netx_api.mcp_remote.topology.layout_ops.score import score_state
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_ops.transforms import normalize_origin
+
+
+def _hops_to_cores(
+    src: str, cores: list[str], adj: dict[str, set[str]]
+) -> tuple[int, str]:
+    if not cores:
+        return 99, ""
+    core_set = set(cores)
+    if src in core_set:
+        return 0, src
+    seen = {src}
+    q: deque[tuple[str, int]] = deque([(src, 0)])
+    while q:
+        u, d = q.popleft()
+        for v in adj.get(u, ()):
+            if v in seen:
+                continue
+            if v in core_set:
+                return d + 1, v
+            seen.add(v)
+            q.append((v, d + 1))
+    return 99, cores[0]
+
+
+def _core_order(state: LayoutState) -> list[str]:
+    cores = [
+        n
+        for n in state.positions
+        if state.layers.get(n) == "core"
+    ]
+    if len(cores) < 2:
+        # fall back: high-degree hubs that look like CN
+        for n, nm in state.names.items():
+            if "-CN" in (nm or "") and n not in cores:
+                cores.append(n)
+    cores = sorted(
+        set(cores),
+        key=lambda n: (-len(state.adj.get(n, ())), state.names.get(n, n)),
+    )
+    return cores
+
+
+def _agg_hubs(state: LayoutState, *, min_degree: int = 4) -> list[str]:
+    aggs = [
+        n
+        for n in state.positions
+        if state.layers.get(n) == "agg"
+        and len(state.adj.get(n, ())) >= min_degree
+    ]
+    return sorted(
+        aggs,
+        key=lambda n: (-len(state.adj.get(n, ())), state.names.get(n, n)),
+    )
+
+
+def _apply_targets(
+    state: LayoutState,
+    targets: dict[str, tuple[float, float]],
+    by_hub: dict[str, Any],
+) -> LayoutState:
+    pos = dict(state.positions)
+    for hid, (tx, ty) in targets.items():
+        block = by_hub.get(hid)
+        if not block or hid not in pos:
+            if hid in pos:
+                pos[hid] = (tx, ty)
+            continue
+        hx, hy = pos[hid]
+        dx, dy = tx - hx, ty - hy
+        for n in block.node_ids:
+            if n in pos:
+                x, y = pos[n]
+                pos[n] = (x + dx, y + dy)
+    st = state.copy()
+    st.positions = pos
+    st = fix_overlaps_local(st, LayoutParams()).state
+    st = normalize_origin(st, LayoutParams()).state
+    return st
+
+
+def pin_beam_rigid(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    step: float | None = None,
+    gap_scales: tuple[float, ...] = (1.0, 1.3, 1.6, 0.85),
+) -> OpResult:
+    """Rigid-translate soft blocks so cores form a beam and aggs sit on a row.
+
+    Picks the trial with lowest (overlaps, crossings). Does not worsen crossings
+    vs the input when a better trial exists; otherwise returns input unchanged.
+    """
+    params = params or LayoutParams()
+    step_px = float(step if step is not None else max(params.pitch, 180.0))
+    cores = _core_order(state)
+    if len(cores) < 2:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="pin_beam",
+            params={"cores": cores},
+            note="need_ge_2_cores",
+        )
+
+    aggs = _agg_hubs(state)
+    aggs = sorted(
+        aggs,
+        key=lambda a: (
+            _hops_to_cores(a, cores, state.adj)[0],
+            state.names.get(a, a),
+        ),
+    )
+    blocks = partition_soft_blocks(state, mode="hub_territory")
+    by_hub = {b.hub_id: b for b in blocks if b.hub_id}
+
+    links = list(state.links)
+    base_cross = count_edge_crossings(state.positions, links)
+    base_score = score_state(state)
+    base_ov = int(base_score.get("footprint_overlap_pairs") or 0) + int(
+        base_score.get("label_overlap_pairs") or 0
+    )
+
+    gap0 = step_px * 2.4
+    beam_y = 0.0
+    trials: list[tuple[str, LayoutState, int, int]] = []
+
+    for gs in gap_scales:
+        gap = gap0 * gs
+        for petal_mul in (4.0, 4.5, 5.5):
+            petal_dy = step_px * petal_mul
+            targets: dict[str, tuple[float, float]] = {}
+            for i, c in enumerate(cores):
+                targets[c] = (i * gap, beam_y)
+            if aggs:
+                lo = targets[cores[0]][0] - gap * 0.5
+                hi = targets[cores[-1]][0] + gap * 0.5
+                for i, a in enumerate(aggs):
+                    _, pref = _hops_to_cores(a, cores, state.adj)
+                    prefer_x = targets.get(pref, targets[cores[0]])[0]
+                    if len(aggs) == 1:
+                        x = prefer_x
+                    else:
+                        x = lo + (hi - lo) * i / (len(aggs) - 1)
+                        x = 0.55 * x + 0.45 * prefer_x
+                    targets[a] = (x, beam_y + petal_dy)
+            st2 = _apply_targets(state, targets, by_hub)
+            sc = score_state(st2)
+            ov = int(sc.get("footprint_overlap_pairs") or 0) + int(
+                sc.get("label_overlap_pairs") or 0
+            )
+            cross = int(sc.get("edge_crossings") or 0)
+            trials.append((f"g{gs}_dy{petal_dy:.0f}", st2, ov, cross))
+
+    # Y-align only (preserve x): often safer on already-readable canvases
+    cy = sum(state.positions[c][1] for c in cores if c in state.positions) / len(
+        cores
+    )
+    targets_y: dict[str, tuple[float, float]] = {}
+    for c in cores:
+        if c in state.positions:
+            targets_y[c] = (state.positions[c][0], cy)
+    ay = cy + step_px * 4.0
+    for a in aggs:
+        if a in state.positions:
+            targets_y[a] = (state.positions[a][0], ay)
+    if targets_y:
+        st_y = _apply_targets(state, targets_y, by_hub)
+        sc = score_state(st_y)
+        ov = int(sc.get("footprint_overlap_pairs") or 0) + int(
+            sc.get("label_overlap_pairs") or 0
+        )
+        cross = int(sc.get("edge_crossings") or 0)
+        trials.append(("y_align", st_y, ov, cross))
+
+    if not trials:
+        return OpResult(
+            state=state, moved=set(), op="pin_beam", note="no_trials"
+        )
+
+    best_name, best_st, best_ov, best_cross = min(
+        trials, key=lambda t: (t[2], t[3])
+    )
+    # Refuse internal accept if worse than input on both axes we care about
+    if best_ov > base_ov or best_cross > base_cross + max(20, int(base_cross * 0.05)):
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="pin_beam",
+            params={
+                "cores": cores,
+                "aggs": aggs,
+                "best_trial": best_name,
+                "best_crossings": best_cross,
+                "base_crossings": base_cross,
+                "accepted": False,
+            },
+            note="no_improvement",
+        )
+
+    moved = {
+        n
+        for n, p in best_st.positions.items()
+        if n in state.positions and p != state.positions[n]
+    }
+    best_st.meta = dict(best_st.meta or {})
+    best_st.meta["pin_beam"] = {
+        "trial": best_name,
+        "cores": cores,
+        "aggs": aggs,
+        "crossings_before": base_cross,
+        "crossings_after": best_cross,
+        "overlaps_after": best_ov,
+    }
+    return OpResult(
+        state=best_st,
+        moved=moved,
+        op="pin_beam",
+        params={
+            "cores": cores,
+            "aggs": aggs,
+            "best_trial": best_name,
+            "accepted": True,
+            "crossings_before": base_cross,
+            "crossings_after": best_cross,
+        },
+        note=f"pin_beam:{best_name} {base_cross}->{best_cross}",
+    )
+
+
+def pin_beam_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    out: dict[str, Any] = {}
+    if not overrides:
+        return out
+    if overrides.get("step") is not None:
+        try:
+            out["step"] = float(overrides["step"])
+        except (TypeError, ValueError):
+            pass
+    return out

netx_api/mcp_remote/topology/layout_ops/press_crossings.py

@@ -0,0 +1,826 @@
+"""Stage-2 crossing pressure without temp scripts.
+
+Actions used by agents via layoutTopologyView:
+- press_hot_edges: rotate non-portal ends of top crossing edges about the other end
+- press_crossers: move nodes that participate in crossings against those hot edges
+- polish_crossings: straighten → hot_edges → crossers → untangle(portals)
+
+Hot path uses incremental crossing deltas (O(deg·E) per trial) so KND-scale
+graphs (~500 links) stay interactive under MCP stdio timeouts.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    count_edge_crossings,
+    crossings_after_node_move,
+    crossings_involving_node,
+    crossing_participation_full,
+    node_footprint,
+    segments_properly_intersect,
+    top_crossing_edges,
+)
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_ops.untangle import untangle_crossings
+
+
+def park_phantom_nodes(state: LayoutState) -> set[str]:
+    """Park region: markers / absurd coords so they do not blow bbox/util."""
+    moved: set[str] = set()
+    for nid, (x, y) in list(state.positions.items()):
+        if str(nid).startswith("region:") or abs(x) > 1e5 or abs(y) > 1e5:
+            if not math.isfinite(x) or not math.isfinite(y) or abs(x) > 1e5 or abs(y) > 1e5:
+                state.positions[nid] = (160.0, 160.0)
+                moved.add(nid)
+            elif str(nid).startswith("region:"):
+                # keep finite but park markers to origin corner
+                if abs(x) > 1e4 or abs(y) > 1e4:
+                    state.positions[nid] = (160.0, 160.0)
+                    moved.add(nid)
+    return moved
+
+
+def _overlaps_any(
+    node: str,
+    pos: dict[str, tuple[float, float]],
+    names: dict[str, str],
+) -> bool:
+    x, y = pos[node]
+    fa = node_footprint(names.get(node, ""))
+    ax0, ay0, ax1, ay1 = x + fa[0], y + fa[1], x + fa[2], y + fa[3]
+    for b, (bx, by) in pos.items():
+        if b == node:
+            continue
+        if abs(bx - x) > 90 and abs(by - y) > 70:
+            continue
+        fb = node_footprint(names.get(b, ""))
+        bx0, by0, bx1, by1 = bx + fb[0], by + fb[1], bx + fb[2], by + fb[3]
+        if ax0 < bx1 and ax1 > bx0 and ay0 < by1 and ay1 > by0:
+            return True
+    return False
+
+
+def _rotate_about(
+    hub: tuple[float, float], leaf: tuple[float, float], ang: float
+) -> tuple[float, float]:
+    hx, hy = hub
+    lx, ly = leaf
+    dx, dy = lx - hx, ly - hy
+    c, s = math.cos(ang), math.sin(ang)
+    return (hx + dx * c - dy * s, hy + dx * s + dy * c)
+
+
+def frozen_portals_from_state(
+    state: LayoutState, portal_ids: list[str] | None = None
+) -> set[str]:
+    from netx_api.mcp_remote.topology.layout_ops.rigid_units import (
+        _hub_portals,
+        frozen_ids_for_protect,
+    )
+
+    if portal_ids:
+        raw = {str(x) for x in portal_ids if str(x)}
+        # Explicit dual-unit portals (~20) must stay frozen; only shrink if
+        # caller passed the inflated multi-membership set.
+        return _hub_portals(state, raw, cap=32)
+    return frozen_ids_for_protect(state, "portals")
+
+
+def _large_graph_budget(n_links: int) -> dict[str, Any]:
+    """Shrink search when E is large so MCP stdio does not time out.
+
+    Target: full polish_crossings on ~500-link graphs finishes in ~15–25s.
+    """
+    e = max(0, int(n_links))
+    if e >= 1200:
+        return {
+            "hot_top_n": 6,
+            "hot_max_moves": 10,
+            "hot_max_sweeps": 1,
+            "hot_angle_step": math.pi / 6,
+            "hot_radii": [0.95, 1.2],
+            "cross_top_n": 4,
+            "cross_max_moves": 12,
+            "cross_max_sweeps": 1,
+            "cross_cand_cap": 48,
+            "straighten": False,
+            "skip_dual_full_x": True,
+            "untangle_rounds": 18,
+            "untangle_moves": 2,
+            "untangle_rank_cap": 36,
+            "untangle_angle_step": 30,
+        }
+    if e >= 400:
+        return {
+            "hot_top_n": 8,
+            "hot_max_moves": 14,
+            "hot_max_sweeps": 2,
+            "hot_angle_step": math.pi / 8,
+            "hot_radii": [0.9, 1.15, 1.35],
+            "cross_top_n": 5,
+            "cross_max_moves": 16,
+            "cross_max_sweeps": 2,
+            "cross_cand_cap": 72,
+            # straighten_channels is O(channels×modes×crossings); on 1k+ link
+            # metros it can stall for minutes with no cancel checkpoints.
+            "straighten": False,
+            "skip_dual_full_x": e >= 800,
+            "untangle_rounds": 28,
+            "untangle_moves": 3,
+            "untangle_rank_cap": 48,
+            "untangle_angle_step": 24,
+        }
+    if e >= 200:
+        return {
+            "hot_top_n": 8,
+            "hot_max_moves": 14,
+            "hot_max_sweeps": 2,
+            "hot_angle_step": math.pi / 9,
+            "hot_radii": [0.9, 1.1, 1.25],
+            "cross_top_n": 4,
+            "cross_max_moves": 14,
+            "cross_max_sweeps": 2,
+            "cross_cand_cap": 80,
+            "straighten": False,
+            "skip_dual_full_x": False,
+            "untangle_rounds": 24,
+            "untangle_moves": 3,
+            "untangle_rank_cap": 60,
+            "untangle_angle_step": 20,
+        }
+    return {
+        "hot_top_n": 10,
+        "hot_max_moves": 24,
+        "hot_max_sweeps": 4,
+        "hot_angle_step": math.pi / 12,
+        "hot_radii": [0.85, 1.0, 1.15, 1.35],
+        "cross_top_n": 6,
+        "cross_max_moves": 40,
+        "cross_max_sweeps": 6,
+        "cross_cand_cap": 220,
+        # Off by default: straighten flattens dual-unit petal eyes into H-chains.
+        "straighten": False,
+        "skip_dual_full_x": False,
+        "untangle_rounds": 120,
+        "untangle_moves": 5,
+        "untangle_rank_cap": 140,
+        "untangle_angle_step": 15,
+    }
+
+
+def _has_petal_dual_eye(state: LayoutState) -> bool:
+    """True when a multi-corridor dual-unit covers a large share of the canvas."""
+    try:
+        from netx_api.mcp_remote.topology.layout_ops.dual_units import (
+            classify_dual_unit,
+            find_dual_portal_units,
+        )
+    except Exception:
+        return False
+    units = find_dual_portal_units(state, max_units=24)
+    if not units:
+        return False
+    n = max(1, len(state.positions) or len(state.names) or len(state.adj))
+    for u in units:
+        if classify_dual_unit(u) != "petal":
+            continue
+        if len(u.member_ids()) >= max(8, int(0.25 * n)):
+            return True
+    return False
+
+
+def press_hot_edges(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    portal_ids: list[str] | None = None,
+    top_n: int | None = None,
+    max_moves: int | None = None,
+    max_sweeps: int | None = None,
+) -> OpResult:
+    """Rotate non-portal ends of top crossing edges about the other endpoint."""
+    from netx_api.mcp_remote.topology.layout_jobs import raise_if_cancelled, touch_heartbeat
+
+    del params
+    st = state.copy()
+    park_phantom_nodes(st)
+    frozen = frozen_portals_from_state(st, portal_ids)
+    pos = dict(st.positions)
+    names = st.names
+    links = st.links
+    adj = st.adj
+    budget = _large_graph_budget(len(links))
+    # Cap overrides — giant metros must not explode search via params.top_n.
+    top_n = min(
+        int(top_n if top_n is not None else budget["hot_top_n"]),
+        int(budget["hot_top_n"]) + (0 if len(links) >= 800 else 4),
+    )
+    max_moves = min(
+        int(max_moves if max_moves is not None else budget["hot_max_moves"]),
+        int(budget["hot_max_moves"]) + (0 if len(links) >= 800 else 6),
+    )
+    max_sweeps = min(
+        int(max_sweeps if max_sweeps is not None else budget["hot_max_sweeps"]),
+        int(budget["hot_max_sweeps"]),
+    )
+    angle_step = float(budget["hot_angle_step"])
+    radii_scale = list(budget["hot_radii"])
+    angles = [i * angle_step for i in range(1, max(2, int(round(2 * math.pi / angle_step))))]
+    skip_dual = bool(budget.get("skip_dual_full_x"))
+
+    touch_heartbeat()
+    start = count_edge_crossings(pos, links)
+    cur = start
+    moved: set[str] = set()
+    accepted = 0
+
+    for _sweep in range(max(1, int(max_sweeps))):
+        raise_if_cancelled()
+        touch_heartbeat()
+        _n, _nh, edge_hit = crossing_participation_full(pos, links)
+        touch_heartbeat()
+        tops = top_crossing_edges(
+            pos, links, names=names, top_n=top_n, edge_participation=edge_hit
+        )
+        sweep_moves = 0
+        for ei, edge in enumerate(tops):
+            if ei % 2 == 0:
+                raise_if_cancelled()
+                touch_heartbeat()
+            a, b = str(edge["a_node_id"]), str(edge["b_node_id"])
+            if a not in pos or b not in pos:
+                continue
+            cand_pairs: list[tuple[str, str]] = []
+            if a not in frozen:
+                cand_pairs.append((a, b))
+            if b not in frozen:
+                cand_pairs.append((b, a))
+            if not cand_pairs:
+                continue
+            cand_pairs.sort(
+                key=lambda pair: (
+                    0 if pair[1] in frozen else 1,
+                    len(adj.get(pair[0], ())),
+                )
+            )
+            leaf, hub = cand_pairs[0]
+            hub_xy, leaf_xy = pos[hub], pos[leaf]
+            r0 = math.hypot(leaf_xy[0] - hub_xy[0], leaf_xy[1] - hub_xy[1]) or 200.0
+            # Cap orbit radius so long spokes cannot fling leaves across a giant bbox.
+            # Giant metros still need a higher reel target than 900 (bridges ≫5k).
+            r_cap = 2200.0 if len(links) >= 400 else 1400.0
+            r_use = min(r0, r_cap)
+            max_disp = 1100.0 if len(links) >= 400 else 1100.0
+            # Inward reel on long metro bridges needs a larger displacement budget
+            # (otherwise max_disp rejects the only moves that cut crossings).
+            if r0 > max_disp * 1.5:
+                max_disp = max(max_disp, min(r0 * 0.55, r0 - 0.35 * r_use))
+            local_before = crossings_involving_node(leaf, pos, links, adj)
+            best: tuple[int, tuple[float, float]] | None = None
+            best_dual: (
+                tuple[int, tuple[float, float], tuple[float, float]] | None
+            ) = None
+
+            def _try_cand(cand: tuple[float, float], disp_cap: float | None = None) -> None:
+                nonlocal best
+                cap = max_disp if disp_cap is None else disp_cap
+                if math.hypot(cand[0] - leaf_xy[0], cand[1] - leaf_xy[1]) > cap:
+                    return
+                trial_pos = {**pos, leaf: cand}
+                if _overlaps_any(leaf, trial_pos, names):
+                    return
+                c1 = crossings_after_node_move(
+                    pos,
+                    links,
+                    adj,
+                    leaf,
+                    cand,
+                    current_total=cur,
+                    local_before=local_before,
+                )
+                if c1 < cur and (best is None or c1 < best[0]):
+                    best = (c1, cand)
+
+            # Reel-in along the spoke first (shorten without hunting angles).
+            # Pulling straight toward the hub cannot fling the leaf across the
+            # bbox, so allow it to travel all the way to the r_use target;
+            # max_disp alone stops short (e.g. r0=8000 → cap 4400 < 5800 needed).
+            if r0 > r_use + 50.0:
+                reel_cap = max(max_disp, r0 - r_use)
+                ux = (leaf_xy[0] - hub_xy[0]) / r0
+                uy = (leaf_xy[1] - hub_xy[1]) / r0
+                for rt in (
+                    r_use,
+                    r_use * 1.15,
+                    max(220.0, r0 * 0.25),
+                    max(220.0, r0 * 0.4),
+                    max(220.0, r0 * 0.55),
+                    max(220.0, r0 * 0.7),
+                ):
+                    rt = min(rt, r0 - 1.0)
+                    if rt < 120.0:
+                        continue
+                    _try_cand((hub_xy[0] + ux * rt, hub_xy[1] + uy * rt), reel_cap)
+
+            # Both ends walk toward the midpoint (metro bridges often need this).
+            # Skip on giant E: each trial is a full O(E²) count and stalls MCP.
+            if (not skip_dual) and r0 > 1500.0 and hub not in frozen:
+                mx = 0.5 * (hub_xy[0] + leaf_xy[0])
+                my = 0.5 * (hub_xy[1] + leaf_xy[1])
+                ux = (leaf_xy[0] - hub_xy[0]) / r0
+                uy = (leaf_xy[1] - hub_xy[1]) / r0
+                px, py = -uy, ux
+                dual_plans: list[tuple[tuple[float, float], tuple[float, float]]] = []
+                for t in (0.15, 0.25, 0.35, 0.45, 0.55):
+                    dual_plans.append(
+                        (
+                            (
+                                leaf_xy[0] + t * (mx - leaf_xy[0]),
+                                leaf_xy[1] + t * (my - leaf_xy[1]),
+                            ),
+                            (
+                                hub_xy[0] + t * (mx - hub_xy[0]),
+                                hub_xy[1] + t * (my - hub_xy[1]),
+                            ),
+                        )
+                    )
+                # Asymmetric: reel the free leaf harder than the hub.
+                for tl, th in ((0.45, 0.15), (0.6, 0.2), (0.7, 0.25)):
+                    dual_plans.append(
+                        (
+                            (
+                                leaf_xy[0] + tl * (mx - leaf_xy[0]),
+                                leaf_xy[1] + tl * (my - leaf_xy[1]),
+                            ),
+                            (
+                                hub_xy[0] + th * (mx - hub_xy[0]),
+                                hub_xy[1] + th * (my - hub_xy[1]),
+                            ),
+                        )
+                    )
+                for s in (-900.0, -520.0, -280.0, 280.0, 520.0, 900.0):
+                    dual_plans.append(
+                        (
+                            (leaf_xy[0] + px * s, leaf_xy[1] + py * s),
+                            (hub_xy[0] + px * s, hub_xy[1] + py * s),
+                        )
+                    )
+                for leaf_c, hub_c in dual_plans:
+                    trial = {**pos, leaf: leaf_c, hub: hub_c}
+                    if _overlaps_any(leaf, trial, names) or _overlaps_any(
+                        hub, trial, names
+                    ):
+                        continue
+                    c1 = count_edge_crossings(trial, links)
+                    if c1 < cur and (best_dual is None or c1 < best_dual[0]):
+                        best_dual = (c1, leaf_c, hub_c)
+
+            for ang in angles:
+                for rs in radii_scale:
+                    rx, ry = _rotate_about(hub_xy, leaf_xy, ang)
+                    dx, dy = rx - hub_xy[0], ry - hub_xy[1]
+                    L = math.hypot(dx, dy) or 1.0
+                    cand = (
+                        hub_xy[0] + dx / L * r_use * min(rs, 1.25),
+                        hub_xy[1] + dy / L * r_use * min(rs, 1.25),
+                    )
+                    _try_cand(cand)
+            if best is None:
+                hx, hy = hub_xy
+                lx, ly = leaf_xy
+                dx, dy = lx - hx, ly - hy
+                L = math.hypot(dx, dy) or 1.0
+                px, py = -dy / L, dx / L
+                for s in (-720.0, -480.0, -320.0, -200.0, -120.0, 120.0, 200.0, 320.0, 480.0, 720.0):
+                    _try_cand((lx + px * s, ly + py * s))
+            # Prefer dual mid-walk when it beats single-end moves.
+            if best_dual is not None and (
+                best is None or best_dual[0] < best[0]
+            ):
+                pos[leaf] = best_dual[1]
+                pos[hub] = best_dual[2]
+                cur = best_dual[0]
+                moved.add(leaf)
+                moved.add(hub)
+                accepted += 1
+                sweep_moves += 1
+                if accepted >= max_moves:
+                    break
+                continue
+            if best is None:
+                continue
+            pos[leaf] = best[1]
+            cur = best[0]
+            moved.add(leaf)
+            accepted += 1
+            sweep_moves += 1
+            if accepted >= max_moves:
+                break
+        if sweep_moves == 0 or accepted >= max_moves:
+            break
+
+    st.positions = pos
+    st.meta = dict(st.meta or {})
+    st.meta["press_hot_edges"] = {
+        "start_crossings": start,
+        "end_crossings": cur,
+        "accepted_moves": accepted,
+        "frozen_n": len(frozen),
+        "budget": {"top_n": top_n, "max_moves": max_moves, "max_sweeps": max_sweeps},
+    }
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="press_hot_edges",
+        params=st.meta["press_hot_edges"],
+        note=f"press_hot_edges {start}->{cur} moves={accepted}",
+    )
+
+
+def _edges_crossing(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    a: str,
+    b: str,
+) -> list[tuple[str, str]]:
+    if a not in pos or b not in pos:
+        return []
+    p1, p2 = pos[a], pos[b]
+    out: list[tuple[str, str]] = []
+    for c, d in links:
+        if len({a, b, c, d}) < 4 or c not in pos or d not in pos:
+            continue
+        if segments_properly_intersect(p1, p2, pos[c], pos[d]):
+            out.append((c, d))
+    return out
+
+
+def _try_move_node(
+    pos: dict[str, tuple[float, float]],
+    names: dict[str, str],
+    links: list[tuple[str, str]],
+    adj: dict[str, set[str]],
+    node: str,
+    *,
+    jump: float,
+    cur: int,
+    cand_cap: int = 220,
+) -> tuple[int, tuple[float, float]] | None:
+    x0, y0 = pos[node]
+    before_local = crossings_involving_node(node, pos, links, adj)
+    if before_local <= 0:
+        return None
+    ang_step = 30 if cand_cap <= 60 else (20 if cand_cap <= 100 else 10)
+    # Keep jumps local — long radii explode util on metro canvases.
+    # Large graphs still need mid-range radii to clear hot-edge crossers.
+    radii = (
+        (120, 220, 360, 520)
+        if cand_cap <= 80
+        else (80, 140, 220, 320, 450, 600, 800, 1100)
+    )
+    cands: list[tuple[float, float]] = []
+    for ang in range(0, 360, ang_step):
+        for r in radii:
+            if r > jump:
+                continue
+            rad = math.radians(ang)
+            cands.append((x0 + r * math.cos(rad), y0 + r * math.sin(rad)))
+    nbs = [pos[v] for v in adj.get(node, ()) if v in pos]
+    if nbs:
+        cx = sum(p[0] for p in nbs) / len(nbs)
+        cy = sum(p[1] for p in nbs) / len(nbs)
+        dx, dy = x0 - cx, y0 - cy
+        L = math.hypot(dx, dy) or 1.0
+        ux, uy = dx / L, dy / L
+        px, py = -uy, ux
+        for s in (-800.0, -500.0, -350.0, -200.0, 200.0, 350.0, 500.0, 800.0):
+            cands.append((x0 + ux * s, y0 + uy * s))
+            cands.append((x0 + px * s, y0 + py * s))
+    if len(cands) > cand_cap:
+        cands = cands[:: max(1, len(cands) // cand_cap)]
+    best: tuple[int, tuple[float, float]] | None = None
+    for cand in cands:
+        if math.hypot(cand[0] - x0, cand[1] - y0) > jump + 1:
+            continue
+        trial_pos = {**pos, node: cand}
+        if _overlaps_any(node, trial_pos, names):
+            continue
+        after_local = crossings_involving_node(node, trial_pos, links, adj)
+        if after_local >= before_local:
+            continue
+        c1 = cur - before_local + after_local
+        if c1 < cur and (best is None or c1 < best[0]):
+            best = (c1, cand)
+    return best
+
+
+def press_crossers(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    portal_ids: list[str] | None = None,
+    top_n: int | None = None,
+    max_moves: int | None = None,
+    max_sweeps: int | None = None,
+) -> OpResult:
+    """Move non-portal nodes involved in crossings against top hot edges."""
+    del params
+    st = state.copy()
+    park_phantom_nodes(st)
+    frozen = frozen_portals_from_state(st, portal_ids)
+    pos = dict(st.positions)
+    names = st.names
+    links = st.links
+    adj = st.adj
+    budget = _large_graph_budget(len(links))
+    top_n = int(top_n if top_n is not None else budget["cross_top_n"])
+    max_moves = int(max_moves if max_moves is not None else budget["cross_max_moves"])
+    max_sweeps = int(max_sweeps if max_sweeps is not None else budget["cross_max_sweeps"])
+    cand_cap = int(budget["cross_cand_cap"])
+
+    start = count_edge_crossings(pos, links)
+    cur = start
+    moved: set[str] = set()
+    accepted = 0
+
+    for _sweep in range(max(1, int(max_sweeps))):
+        _n, _nh, edge_hit = crossing_participation_full(pos, links)
+        tops = top_crossing_edges(
+            pos, links, names=names, top_n=top_n, edge_participation=edge_hit
+        )
+        scores: dict[str, int] = {}
+        for he in tops:
+            a, b = str(he["a_node_id"]), str(he["b_node_id"])
+            for c, d in _edges_crossing(pos, links, a, b):
+                for nid in (c, d, a, b):
+                    if nid in frozen or nid not in pos:
+                        continue
+                    deg = len(adj.get(nid, ()))
+                    if deg >= 16:
+                        continue
+                    scores[nid] = scores.get(nid, 0) + int(he["crossing_hits"]) + max(
+                        0, 10 - deg
+                    )
+        ranked = sorted(scores, key=lambda n: (-scores[n], len(adj.get(n, ())), n))
+        node_cap = 16 if cand_cap <= 60 else (24 if cand_cap <= 100 else 40)
+        improved = False
+        for node in ranked[:node_cap]:
+            jump = 1400.0 if len(adj.get(node, ())) <= 4 else 1000.0
+            best = _try_move_node(
+                pos, names, links, adj, node, jump=jump, cur=cur, cand_cap=cand_cap
+            )
+            if best is None:
+                continue
+            pos[node] = best[1]
+            cur = best[0]
+            moved.add(node)
+            accepted += 1
+            improved = True
+            if accepted >= max_moves:
+                break
+        if not improved or accepted >= max_moves:
+            break
+
+    st.positions = pos
+    st.meta = dict(st.meta or {})
+    st.meta["press_crossers"] = {
+        "start_crossings": start,
+        "end_crossings": cur,
+        "accepted_moves": accepted,
+        "frozen_n": len(frozen),
+        "budget": {"top_n": top_n, "max_moves": max_moves, "max_sweeps": max_sweeps},
+    }
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="press_crossers",
+        params=st.meta["press_crossers"],
+        note=f"press_crossers {start}->{cur} moves={accepted}",
+    )
+
+
+def polish_crossings(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    portal_ids: list[str] | None = None,
+    straighten: bool | None = None,
+    max_degree: int = 9,
+    untangle_rounds: int | None = None,
+    top_n: int | None = None,
+    max_moves: int | None = None,
+    max_sweeps: int | None = None,
+    preserve_dual_eye: bool | None = None,
+) -> OpResult:
+    """Pipeline: park phantoms → (optional straighten) → hot → crossers → untangle.
+
+    Petal dual-unit eyes: straighten stays off unless ``straighten=true`` —
+    channel flattening destroys parallel-lane eye geometry.
+    """
+    from netx_api.mcp_remote.topology.layout_jobs import raise_if_cancelled, report_progress
+
+    params = params or LayoutParams()
+    st = state.copy()
+    park_phantom_nodes(st)
+    budget = _large_graph_budget(len(st.links))
+    preserve = True if preserve_dual_eye is None else bool(preserve_dual_eye)
+    petal_eye = False
+    eye_portals: list[str] = []
+    if preserve:
+        try:
+            from netx_api.mcp_remote.topology.layout_ops.dual_units import (
+                classify_dual_unit,
+                find_dual_portal_units,
+            )
+
+            units = find_dual_portal_units(st, max_units=24)
+            n = max(1, len(st.positions) or len(st.names) or len(st.adj))
+            for u in units:
+                if classify_dual_unit(u) != "petal":
+                    continue
+                if len(u.member_ids()) >= max(8, int(0.25 * n)):
+                    petal_eye = True
+                    eye_portals = [u.portal_a, u.portal_b]
+                    break
+        except Exception:
+            petal_eye = _has_petal_dual_eye(st)
+    if portal_ids is None and eye_portals:
+        portal_ids = eye_portals
+    # Giant graphs: never allow straighten even if caller asks (stalls for minutes).
+    if len(st.links) >= 800:
+        do_straighten = False
+    elif petal_eye and straighten is not True:
+        do_straighten = False
+    else:
+        do_straighten = (
+            bool(budget["straighten"]) if straighten is None else bool(straighten)
+        )
+    untangle_rounds = int(
+        untangle_rounds if untangle_rounds is not None else budget["untangle_rounds"]
+    )
+    if len(st.links) >= 800:
+        untangle_rounds = min(untangle_rounds, int(budget["untangle_rounds"]))
+        top_n = (
+            min(int(top_n), int(budget["hot_top_n"]))
+            if top_n is not None
+            else top_n
+        )
+        max_moves = (
+            min(int(max_moves), int(budget["hot_max_moves"]))
+            if max_moves is not None
+            else max_moves
+        )
+    report_progress(
+        "polish_start",
+        pct=42.0,
+        message=f"links={len(st.links)} rounds={untangle_rounds}",
+        links=len(st.links),
+        untangle_rounds=untangle_rounds,
+    )
+    start = count_edge_crossings(st.positions, st.links)
+    trace: list[dict[str, Any]] = []
+    moved: set[str] = set()
+
+    if do_straighten:
+        from netx_api.mcp_remote.topology.layout_ops.channels import straighten_channels_greedy
+
+        raise_if_cancelled()
+        report_progress("polish_straighten", pct=48.0, message=f"x0={start}")
+        op = straighten_channels_greedy(st, params)
+        st = op.state
+        moved |= op.moved
+        trace.append({"op": "straighten_channels", "note": op.note, **(op.params or {})})
+
+    raise_if_cancelled()
+    report_progress("polish_hot_edges", pct=55.0, message="press_hot_edges")
+    op_h = press_hot_edges(
+        st,
+        params,
+        portal_ids=portal_ids,
+        top_n=top_n,
+        max_moves=max_moves,
+        max_sweeps=max_sweeps,
+    )
+    st = op_h.state
+    moved |= op_h.moved
+    trace.append({"op": "press_hot_edges", "note": op_h.note, **(op_h.params or {})})
+
+    raise_if_cancelled()
+    report_progress("polish_crossers", pct=62.0, message="press_crossers")
+    op_c = press_crossers(
+        st,
+        params,
+        portal_ids=portal_ids,
+        top_n=top_n,
+        max_moves=max_moves,
+        max_sweeps=max_sweeps,
+    )
+    st = op_c.state
+    moved |= op_c.moved
+    trace.append({"op": "press_crossers", "note": op_c.note, **(op_c.params or {})})
+
+    focus: list[str] = []
+    _n, node_hit, edge_hit = crossing_participation_full(st.positions, st.links)
+    tops_e = top_crossing_edges(
+        st.positions, st.links, names=st.names, top_n=5, edge_participation=edge_hit
+    )
+    for row in tops_e:
+        focus.append(str(row["a_node_id"]))
+        focus.append(str(row["b_node_id"]))
+    ranked_nodes = sorted(node_hit.items(), key=lambda kv: -kv[1])[:5]
+    focus.extend(nid for nid, _ in ranked_nodes)
+    focus = list(dict.fromkeys(focus))
+
+    raise_if_cancelled()
+    report_progress(
+        "polish_untangle",
+        pct=70.0,
+        message=f"untangle rounds={untangle_rounds} focus={len(focus)}",
+        untangle_rounds=untangle_rounds,
+        focus_n=len(focus),
+    )
+    op_u = untangle_crossings(
+        st,
+        params,
+        protect_rigid="portals",
+        focus_ids=focus,
+        max_rounds=untangle_rounds,
+        max_degree=max_degree,
+        target_crossings=40,
+        max_jump=1000.0,
+        moves_per_round=int(budget["untangle_moves"]),
+        frozen_ids=frozen_portals_from_state(st, portal_ids) if portal_ids else None,
+        rank_cap=int(budget["untangle_rank_cap"]),
+        angle_step=int(budget["untangle_angle_step"]),
+        refresh_every=3 if len(st.links) >= 200 else 1,
+    )
+    st = op_u.state
+    moved |= op_u.moved
+    trace.append({"op": "untangle", "note": op_u.note, **(op_u.params or {})})
+
+    end = count_edge_crossings(st.positions, st.links)
+    report_progress(
+        "polish_done",
+        pct=74.0,
+        message=f"crossings {start}->{end}",
+        crossings_before=start,
+        crossings_after=end,
+    )
+    st.meta = dict(st.meta or {})
+    st.meta["polish_crossings"] = {
+        "start_crossings": start,
+        "end_crossings": end,
+        "trace": trace,
+        "focus_n": len(focus),
+        "budget": budget,
+        "straighten": do_straighten,
+        "preserve_dual_eye": preserve,
+        "petal_eye_detected": petal_eye,
+        "untangle_rounds": untangle_rounds,
+    }
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="polish_crossings",
+        params=st.meta["polish_crossings"],
+        note=f"polish_crossings {start}->{end}",
+    )
+
+
+def press_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    o = overrides or {}
+    out: dict[str, Any] = {}
+    for key, cast in (
+        ("top_n", int),
+        ("max_moves", int),
+        ("max_sweeps", int),
+        ("max_degree", int),
+        ("untangle_rounds", int),
+    ):
+        if key in o and o[key] is not None:
+            try:
+                out[key] = cast(o[key])
+            except (TypeError, ValueError):
+                pass
+    if "straighten" in o:
+        v = o["straighten"]
+        out["straighten"] = (
+            v
+            if isinstance(v, bool)
+            else str(v).strip().lower() in {"1", "true", "yes", "on"}
+        )
+    if "preserve_dual_eye" in o:
+        v = o["preserve_dual_eye"]
+        out["preserve_dual_eye"] = (
+            v
+            if isinstance(v, bool)
+            else str(v).strip().lower() in {"1", "true", "yes", "on"}
+        )
+    if "portal_ids" in o and isinstance(o["portal_ids"], list):
+        out["portal_ids"] = [str(x) for x in o["portal_ids"] if str(x)]
+    if "source_view_ids" in o and isinstance(o["source_view_ids"], list):
+        out["source_view_ids"] = [str(x) for x in o["source_view_ids"] if str(x)]
+    return out

netx_api/mcp_remote/topology/layout_ops/pull_far_chains.py

@@ -0,0 +1,354 @@
+"""Pull far deg≤2 chains / isolates toward the eye portal anchor.
+
+Unlike uniform ``compact_bbox``, only moves far corridors (and deg=0 orphans).
+Hard gates: crossings not rise, overlaps stay 0, portals frozen.
+Scale is toward the portal mid-point (not the chain hub) so outer hubs
+still shrink the canvas bbox.
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings, compute_edge_clearance
+from netx_api.mcp_remote.topology.layout_ops.graph_util import bbox
+from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import _has_any_footprint_overlap
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_topology_quality import extract_chain_paths
+
+
+def _anchor(pos: dict[str, tuple[float, float]], frozen: set[str]) -> tuple[float, float]:
+    pts = [pos[n] for n in frozen if n in pos]
+    if pts:
+        return (
+            sum(p[0] for p in pts) / len(pts),
+            sum(p[1] for p in pts) / len(pts),
+        )
+    xs = [p[0] for p in pos.values()] or [0.0]
+    ys = [p[1] for p in pos.values()] or [0.0]
+    return ((min(xs) + max(xs)) / 2.0, (min(ys) + max(ys)) / 2.0)
+
+
+def _area(pos: dict[str, tuple[float, float]]) -> float:
+    if len(pos) < 2:
+        return 1.0
+    x0, y0, x1, y1 = bbox(pos)
+    return max((x1 - x0) * (y1 - y0), 1.0)
+
+
+def _clr_hits(pos, links, names) -> int:
+    ec = compute_edge_clearance(pos, links, names=names, top_n=1)
+    if ec.get("edge_clearance_skipped"):
+        return 10**9
+    return int(ec.get("edge_clearance_hits") or 0)
+
+
+def _try_scale(
+    pos: dict[str, tuple[float, float]],
+    mobile: list[str],
+    *,
+    cx: float,
+    cy: float,
+    scale: float,
+    links,
+    names,
+    g0: int,
+    clr0: int,
+    max_clearance_slack: int,
+) -> tuple[dict[str, tuple[float, float]], int, int, float] | None:
+    trial = dict(pos)
+    for n in mobile:
+        x, y = pos[n]
+        trial[n] = (cx + (x - cx) * scale, cy + (y - cy) * scale)
+    if _has_any_footprint_overlap(trial, names):
+        return None
+    g1 = count_edge_crossings(trial, links)
+    if g1 > g0:
+        return None
+    clr1 = _clr_hits(trial, links, names)
+    if clr1 > clr0 + max(0, int(max_clearance_slack)):
+        return None
+    return trial, g1, clr1, _area(trial)
+
+
+def pull_far_chains(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    frozen_ids: set[str] | None = None,
+    portal_ids: list[str] | None = None,
+    max_chains: int = 16,
+    min_tip_radius: float = 1800.0,
+    scales: tuple[float, ...] = (0.92, 0.88, 0.84, 0.80, 0.75),
+    max_clearance_slack: int = 40,
+    pull_isolates: bool = True,
+) -> OpResult:
+    """Shorten farthest corridors / orphans toward the portal mid-point."""
+    del params
+    st = state.copy()
+    pos = dict(st.positions)
+    names = st.names
+    links = list(st.links)
+    adj = st.adj
+    frozen: set[str] = set(frozen_ids or ())
+    for p in portal_ids or []:
+        if p:
+            frozen.add(str(p))
+
+    g0 = count_edge_crossings(pos, links)
+    if _has_any_footprint_overlap(pos, names):
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="pull_far_chains",
+            note="pull_far_chains:overlaps_before",
+            params={"error": "overlaps_before"},
+        )
+    clr0 = _clr_hits(pos, links, names)
+    area0 = _area(pos)
+    cx, cy = _anchor(pos, frozen)
+
+    chains = extract_chain_paths(adj)
+    scored: list[tuple[float, list[str], str]] = []
+    for path in chains:
+        path = [n for n in path if n in pos]
+        if len(path) < 2:
+            continue
+        d0 = math.hypot(pos[path[0]][0] - cx, pos[path[0]][1] - cy)
+        d1 = math.hypot(pos[path[-1]][0] - cx, pos[path[-1]][1] - cy)
+        tip = path[0] if d0 >= d1 else path[-1]
+        hub = path[-1] if tip == path[0] else path[0]
+        if tip in frozen:
+            continue
+        tip_r = max(d0, d1)
+        if tip_r < float(min_tip_radius):
+            continue
+        scored.append((tip_r, path, hub))
+    scored.sort(key=lambda t: t[0], reverse=True)
+
+    moved: set[str] = set()
+    accepted: list[dict[str, Any]] = []
+    used: set[str] = set(frozen)
+
+    for tip_r, path, hub in scored[: max(1, int(max_chains))]:
+        # Scale corridor toward portal mid-point (shrinks bbox even if hub is outer).
+        mobile = [n for n in path if n not in used and n in pos and n not in frozen]
+        if len(mobile) < 1:
+            continue
+        best_local = None
+        best_key = None
+        for scale in scales:
+            got = _try_scale(
+                pos,
+                mobile,
+                cx=cx,
+                cy=cy,
+                scale=scale,
+                links=links,
+                names=names,
+                g0=g0,
+                clr0=clr0,
+                max_clearance_slack=max_clearance_slack,
+            )
+            if got is None:
+                continue
+            trial, g1, clr1, area1 = got
+            key = (g1, area1, clr1)
+            if best_key is None or key < best_key:
+                best_key = key
+                best_local = (trial, scale, g1, clr1, area1)
+        if best_local is None:
+            continue
+        trial, scale, g1, clr1, area1 = best_local
+        tip = (
+            path[0]
+            if math.hypot(pos[path[0]][0] - cx, pos[path[0]][1] - cy)
+            >= math.hypot(pos[path[-1]][0] - cx, pos[path[-1]][1] - cy)
+            else path[-1]
+        )
+        tip_r1 = math.hypot(trial[tip][0] - cx, trial[tip][1] - cy)
+        if tip_r1 >= tip_r - 1.0 and area1 >= area0 * 0.999:
+            continue
+        pos = trial
+        g0 = g1
+        clr0 = clr1
+        for n in mobile:
+            moved.add(n)
+            used.add(n)
+        accepted.append(
+            {
+                "hub": hub,
+                "tip": tip,
+                "member_n": len(mobile),
+                "scale": scale,
+                "tip_r0": round(tip_r, 1),
+                "tip_r1": round(tip_r1, 1),
+            }
+        )
+
+    isolates_n = 0
+    if pull_isolates:
+        orphans = [
+            n
+            for n, nbs in adj.items()
+            if n in pos
+            and n not in used
+            and n not in frozen
+            and len(nbs) == 0
+            and math.hypot(pos[n][0] - cx, pos[n][1] - cy) >= float(min_tip_radius) * 0.6
+        ]
+        orphans.sort(
+            key=lambda n: math.hypot(pos[n][0] - cx, pos[n][1] - cy),
+            reverse=True,
+        )
+        for n in orphans[: max(4, int(max_chains) // 2)]:
+            best_local = None
+            best_key = None
+            for scale in scales:
+                got = _try_scale(
+                    pos,
+                    [n],
+                    cx=cx,
+                    cy=cy,
+                    scale=scale,
+                    links=links,
+                    names=names,
+                    g0=g0,
+                    clr0=clr0,
+                    max_clearance_slack=max_clearance_slack,
+                )
+                if got is None:
+                    continue
+                trial, g1, clr1, area1 = got
+                key = (g1, area1, clr1)
+                if best_key is None or key < best_key:
+                    best_key = key
+                    best_local = (trial, scale, g1, clr1)
+            if best_local is None:
+                continue
+            trial, scale, g1, clr1 = best_local
+            pos = trial
+            g0 = g1
+            clr0 = clr1
+            moved.add(n)
+            used.add(n)
+            isolates_n += 1
+            accepted.append(
+                {
+                    "hub": None,
+                    "tip": n,
+                    "member_n": 1,
+                    "scale": scale,
+                    "isolate": True,
+                }
+            )
+
+    leaves = [
+        n
+        for n, nbs in adj.items()
+        if n in pos
+        and n not in used
+        and n not in frozen
+        and len(nbs) == 1
+        and math.hypot(pos[n][0] - cx, pos[n][1] - cy) >= float(min_tip_radius)
+    ]
+    leaves.sort(
+        key=lambda n: math.hypot(pos[n][0] - cx, pos[n][1] - cy),
+        reverse=True,
+    )
+    for n in leaves[: max(4, int(max_chains) // 2)]:
+        best_local = None
+        best_key = None
+        for scale in scales:
+            got = _try_scale(
+                pos,
+                [n],
+                cx=cx,
+                cy=cy,
+                scale=scale,
+                links=links,
+                names=names,
+                g0=g0,
+                clr0=clr0,
+                max_clearance_slack=max_clearance_slack,
+            )
+            if got is None:
+                continue
+            trial, g1, clr1, area1 = got
+            key = (g1, area1, clr1)
+            if best_key is None or key < best_key:
+                best_key = key
+                best_local = (trial, scale, g1, clr1)
+        if best_local is None:
+            continue
+        trial, scale, g1, clr1 = best_local
+        pos = trial
+        g0 = g1
+        clr0 = clr1
+        moved.add(n)
+        used.add(n)
+        accepted.append(
+            {
+                "hub": next(iter(adj.get(n) or ()), None),
+                "tip": n,
+                "member_n": 1,
+                "scale": scale,
+                "leaf": True,
+            }
+        )
+
+    st.positions = pos
+    st.last_moved = moved
+    g_end = count_edge_crossings(pos, links)
+    clr_end = _clr_hits(pos, links, names)
+    meta = {
+        "mode": "pull_far_chains",
+        "chains_tried": min(len(scored), max(1, int(max_chains))),
+        "chains_accepted": len(accepted),
+        "accepted_chains": accepted[:20],
+        "moved_n": len(moved),
+        "isolates_pulled": isolates_n,
+        "start_area": round(area0, 1),
+        "end_area": round(_area(pos), 1),
+        "start_crossings": count_edge_crossings(dict(state.positions), list(state.links)),
+        "end_crossings": g_end,
+        "start_clearance_hits": _clr_hits(dict(state.positions), list(state.links), names),
+        "end_clearance_hits": clr_end,
+    }
+    st.meta["pull_far_chains"] = meta
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="pull_far_chains",
+        params=meta,
+        note=(
+            f"pull_far_chains n={len(accepted)} moved={len(moved)} "
+            f"area={meta['start_area']}→{meta['end_area']} "
+            f"x={meta['start_crossings']}→{meta['end_crossings']}"
+        ),
+    )
+
+
+def pull_far_chains_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    o = overrides or {}
+    portals = o.get("portal_ids") or o.get("portals") or []
+    if isinstance(portals, str):
+        portals = [portals]
+    frozen = o.get("frozen_ids") or []
+    if isinstance(frozen, str):
+        frozen = [frozen]
+    scales = o.get("scales")
+    if isinstance(scales, (list, tuple)) and scales:
+        sc = tuple(float(x) for x in scales)
+    else:
+        sc = (0.92, 0.88, 0.84, 0.80, 0.75)
+    return {
+        "portal_ids": [str(x) for x in portals if str(x).strip()],
+        "frozen_ids": {str(x) for x in frozen if str(x).strip()},
+        "max_chains": int(o.get("max_chains") or 16),
+        "min_tip_radius": float(o.get("min_tip_radius") or 1800.0),
+        "scales": sc,
+        "max_clearance_slack": int(o.get("max_clearance_slack") or 40),
+        "pull_isolates": bool(o.get("pull_isolates", True)),
+    }

netx_api/mcp_remote/topology/layout_ops/recipe.py

@@ -0,0 +1,352 @@
+"""Compose atomic ops into multipass recipes (supports per-block scope)."""
+
+from __future__ import annotations
+
+from dataclasses import asdict
+from typing import Any, Callable
+
+from netx_api.mcp_remote.topology.layout_ops.score import score_op, score_state
+from netx_api.mcp_remote.topology.layout_ops.scope import list_blocks, map_blocks, select_scope
+from netx_api.mcp_remote.topology.layout_ops.sides import place_side_branches
+from netx_api.mcp_remote.topology.layout_ops.skeleton import build_skeleton
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local, relax_hotspots
+from netx_api.mcp_remote.topology.layout_ops.rings import build_ring_skeleton
+from netx_api.mcp_remote.topology.layout_ops.partition import pack_soft_blocks
+from netx_api.mcp_remote.topology.layout_ops.transforms import (
+    enforce_min_gap,
+    explode_clusters,
+    normalize_origin,
+    pack_utilization,
+    resolve_overlaps,
+    scale_edge_axes,
+    scale_region,
+    select_pins,
+    soft_nn_scale,
+)
+
+OpFn = Callable[..., OpResult]
+
+REGISTRY: dict[str, OpFn] = {
+    "build_skeleton": build_skeleton,
+    "build_ring_skeleton": build_ring_skeleton,
+    "place_side_branches": place_side_branches,
+    "select_pins": select_pins,
+    "select_scope": select_scope,
+    "map_blocks": map_blocks,
+    "scale_region": scale_region,
+    "scale_edge_axes": scale_edge_axes,
+    "pack_utilization": pack_utilization,
+    "pack_soft_blocks": pack_soft_blocks,
+    "resolve_overlaps": resolve_overlaps,
+    "explode_clusters": explode_clusters,
+    "enforce_min_gap": enforce_min_gap,
+    "soft_nn_scale": soft_nn_scale,
+    "normalize_origin": normalize_origin,
+    "fix_overlaps_local": fix_overlaps_local,
+    "relax_hotspots": relax_hotspots,
+    "score": score_op,
+}
+
+
+def smd_corridor_v1_passes() -> list[dict[str, Any]]:
+    """Global structure, then per-component explode+resolve (scoped)."""
+    return [
+        {"op": "build_skeleton"},
+        {"op": "place_side_branches"},
+        {"op": "select_scope", "kwargs": {"mode": "all"}},
+        {"op": "scale_edge_axes"},
+        {"op": "soft_nn_scale"},
+        {"op": "map_blocks", "kwargs": {"mode": "component"}},
+        # Expanded at runtime by foreach_blocks
+        {
+            "op": "foreach_blocks",
+            "kwargs": {
+                "mode": "component",
+                "passes": [
+                    {
+                        "op": "explode_clusters",
+                        "kwargs": {"thr": 20.0, "gap": 80.0, "axis": "perp"},
+                    },
+                    {"op": "resolve_overlaps", "kwargs": {"mode": "lateral"}},
+                ],
+            },
+        },
+        {"op": "select_scope", "kwargs": {"mode": "all"}},
+        {"op": "normalize_origin"},
+        {"op": "score"},
+    ]
+
+
+def smd_corridor_unstick_v1_passes() -> list[dict[str, Any]]:
+    """v1 + second per-block free resolve for stubborn overlaps."""
+    return [
+        *smd_corridor_v1_passes()[:-2],
+        {
+            "op": "foreach_blocks",
+            "kwargs": {
+                "mode": "component",
+                "passes": [
+                    {"op": "resolve_overlaps", "kwargs": {"mode": "free"}},
+                ],
+            },
+        },
+        {"op": "select_scope", "kwargs": {"mode": "all"}},
+        {"op": "normalize_origin"},
+        {"op": "score"},
+    ]
+
+
+def agg_rings_v1_passes() -> list[dict[str, Any]]:
+    """Rings recipe: dual-hub min-rings when eligible, else UME petals.
+
+    Light overlap fix only — avoid explode that destroys ring geometry.
+    """
+    return [
+        {"op": "build_ring_skeleton"},
+        {"op": "select_pins", "kwargs": {"mode": "agg"}},
+        {"op": "select_scope", "kwargs": {"mode": "all"}},
+        {"op": "fix_overlaps_local"},
+        {"op": "normalize_origin"},
+        {"op": "score"},
+    ]
+
+
+def smd_corridor_compact_v1_passes() -> list[dict[str, Any]]:
+    """Corridor skeleton, then pack/unstick **per component** — no global crush.
+
+    Global isotropic pack destroys crossings; islands are compacted in-place.
+    Ends with surgical local overlap fix.
+    """
+    return [
+        {"op": "build_skeleton"},
+        {"op": "place_side_branches"},
+        {"op": "select_scope", "kwargs": {"mode": "all"}},
+        {"op": "scale_edge_axes"},
+        {"op": "soft_nn_scale"},
+        {"op": "map_blocks", "kwargs": {"mode": "component"}},
+        {
+            "op": "foreach_blocks",
+            "kwargs": {
+                "mode": "component",
+                "min_size": 3,
+                "passes": [
+                    {"op": "pack_utilization"},
+                    {
+                        "op": "explode_clusters",
+                        "kwargs": {"thr": 8.0, "gap": 35.0, "axis": "along"},
+                    },
+                    {"op": "resolve_overlaps", "kwargs": {"mode": "lateral"}},
+                ],
+            },
+        },
+        {"op": "select_scope", "kwargs": {"mode": "all"}},
+        {"op": "fix_overlaps_local"},
+        {"op": "normalize_origin"},
+        {"op": "score"},
+    ]
+
+
+RECIPES: dict[str, Callable[[], list[dict[str, Any]]]] = {
+    "smd_corridor_v1": smd_corridor_v1_passes,
+    "smd_corridor_unstick_v1": smd_corridor_unstick_v1_passes,
+    "smd_corridor_compact_v1": smd_corridor_compact_v1_passes,
+    "agg_rings_v1": agg_rings_v1_passes,
+}
+
+
+def _apply_op(
+    st: LayoutState,
+    name: str,
+    params: LayoutParams,
+    kwargs: dict[str, Any],
+    *,
+    ume_reference: bool,
+) -> OpResult:
+    if name == "score":
+        return score_op(st, ume_reference=ume_reference)
+    if name == "select_pins":
+        return select_pins(st, **kwargs)
+    if name == "select_scope":
+        return select_scope(st, **kwargs)
+    if name == "map_blocks":
+        return map_blocks(st, **kwargs)
+    if name == "pack_soft_blocks":
+        return pack_soft_blocks(st, params, **kwargs)
+    if name == "scale_region":
+        return scale_region(st, params, **kwargs)
+    if name == "resolve_overlaps":
+        return resolve_overlaps(st, params, **kwargs)
+    if name == "explode_clusters":
+        return explode_clusters(st, params, **kwargs)
+    if name == "enforce_min_gap":
+        return enforce_min_gap(st, params, **kwargs)
+    if name == "fix_overlaps_local":
+        return fix_overlaps_local(st, params)
+    if name == "relax_hotspots":
+        return relax_hotspots(st, params)
+    if name == "build_ring_skeleton":
+        return build_ring_skeleton(st, params)
+    fn = REGISTRY.get(name)
+    if fn is None:
+        raise KeyError(f"unknown op: {name}")
+    return fn(st, params)
+
+
+def _trace_entry(
+    i: int,
+    op: str,
+    note: str,
+    params: dict[str, Any],
+    moved_n: int,
+    pinned_n: int,
+    metrics: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    m = metrics or {}
+    return {
+        "i": i,
+        "op": op,
+        "note": note,
+        "params": params,
+        "moved_n": moved_n,
+        "pinned_n": pinned_n,
+        "edge_crossings": m.get("edge_crossings"),
+        "nn_p50": m.get("nn_p50"),
+        "footprint_overlap_pairs": m.get("footprint_overlap_pairs"),
+        "label_overlap_pairs": m.get("label_overlap_pairs"),
+        "space_utilization": m.get("space_utilization"),
+        "bbox": m.get("bbox"),
+        "grade": (m.get("grade") or {}).get("overall") if m else None,
+    }
+
+
+def _expand_foreach(
+    st: LayoutState,
+    kwargs: dict[str, Any],
+    params: LayoutParams,
+    *,
+    ume_reference: bool,
+    base_i: int,
+) -> tuple[LayoutState, list[dict[str, Any]]]:
+    """Run sub-passes once per block. Score only once after all blocks (fast)."""
+    mode = str(kwargs.get("mode") or "component")
+    sub_passes: list[dict[str, Any]] = list(kwargs.get("passes") or [])
+    min_size = int(kwargs.get("min_size") or 2)
+    blocks = list_blocks(st, mode=mode)
+    # Record resolved mode (auto → hub_territory|component) for agents/trace.
+    from netx_api.mcp_remote.topology.layout_ops.partition import resolve_block_mode
+
+    resolved_mode = resolve_block_mode(st, mode) if mode == "auto" else mode
+    st = st.copy()
+    st.meta["block_mode"] = resolved_mode
+    st.meta["blocks"] = [sorted(b) for b in blocks]
+    trace: list[dict[str, Any]] = []
+    step_i = base_i
+    total_moved = 0
+
+    for bi, block in enumerate(blocks):
+        if len(block) < min_size:
+            continue
+        st = select_scope(st, mode="ids", node_ids=block).state
+        block_moved = 0
+        for step in sub_passes:
+            name = str(step.get("op") or "")
+            kw = dict(step.get("kwargs") or {})
+            result = _apply_op(st, name, params, kw, ume_reference=ume_reference)
+            st = result.state
+            block_moved += len(result.moved)
+            total_moved += len(result.moved)
+        trace.append(
+            _trace_entry(
+                step_i,
+                "foreach_block",
+                f"block[{bi}] n={len(block)} moved≈{block_moved}",
+                {"block_index": bi, "scope_n": len(block), "mode": mode},
+                block_moved,
+                len(st.pinned),
+            )
+        )
+        step_i += 1
+
+    st = select_scope(st, mode="all").state
+    metrics = score_state(st, ume_reference=ume_reference)
+    trace.append(
+        _trace_entry(
+            step_i,
+            "foreach_blocks",
+            f"done blocks={len(blocks)} moved≈{total_moved}",
+            {"mode": mode, "blocks_n": len(blocks), "moved_n": total_moved},
+            total_moved,
+            len(st.pinned),
+            metrics,
+        )
+    )
+    return st, trace
+
+
+def run_recipe(
+    state: LayoutState,
+    recipe: str | list[dict[str, Any]] = "smd_corridor_v1",
+    params: LayoutParams | None = None,
+    *,
+    ume_reference: bool = False,
+) -> tuple[LayoutState, list[dict[str, Any]], dict[str, Any]]:
+    """Run passes; return final state, per-pass trace, final score."""
+    params = params or LayoutParams()
+    passes = RECIPES[recipe]() if isinstance(recipe, str) else recipe
+    st = state.copy()
+    trace: list[dict[str, Any]] = []
+    i = 0
+
+    for step in passes:
+        name = str(step.get("op") or "")
+        kwargs = dict(step.get("kwargs") or {})
+        if name == "foreach_blocks":
+            st, sub = _expand_foreach(
+                st, kwargs, params, ume_reference=ume_reference, base_i=i
+            )
+            trace.extend(sub)
+            i = (trace[-1]["i"] + 1) if trace else i + 1
+            continue
+
+        result = _apply_op(st, name, params, kwargs, ume_reference=ume_reference)
+        st = result.state
+        # Lightweight steps skip full score; score at structural checkpoints + end.
+        want_score = name in {
+            "build_skeleton",
+            "place_side_branches",
+            "soft_nn_scale",
+            "normalize_origin",
+            "score",
+            "pack_utilization",
+        }
+        metrics = score_state(st, ume_reference=ume_reference) if want_score else None
+        trace.append(
+            _trace_entry(
+                i,
+                result.op,
+                result.note,
+                result.params,
+                len(result.moved),
+                len(st.pinned),
+                metrics,
+            )
+        )
+        i += 1
+
+    final = score_state(st, ume_reference=ume_reference)
+    final["recipe"] = recipe if isinstance(recipe, str) else "custom"
+    final["params"] = asdict(params)
+    final["passes"] = len(trace)
+    return st, trace, final
+
+
+def positions_for_api(state: LayoutState) -> list[dict[str, float | str]]:
+    return [
+        {
+            "fabric_node_id": n,
+            "x": round(state.positions[n][0], 1),
+            "y": round(state.positions[n][1], 1),
+        }
+        for n in state.positions
+    ]

netx_api/mcp_remote/topology/layout_ops/rigid_orbit.py

@@ -0,0 +1,566 @@
+"""Rigid-body densify: drag a dual-unit by an external corridor (bridge orbit).
+
+For each staging rigid group, pick an external bridge tip as pivot and sweep the
+exclusive members on concentric circles (angle × radius_scale). Prefer inward
+radii to raise util / shrink bbox while keeping overlaps=0 and crossings within
+``x_slack``.
+
+This reuses the polar spirit of ``compose_orbit`` attach, but runs *after*
+compose as a densify/util pass (portals stay frozen when shared).
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.compose_orbit import crossings_touching
+from netx_api.mcp_remote.topology.layout_ops.hotspots import overlapping_nodes
+from netx_api.mcp_remote.topology.layout_ops.rigid_units import groups_from_compose_meta
+from netx_api.mcp_remote.topology.layout_ops.score import score_state
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState, OpResult
+
+_COORD_ABS_MAX = 1.0e6
+_DEFAULT_ANGLE_STEP = 30
+# Bias inward: pull units toward external tips to densify.
+_DEFAULT_RADII = (0.55, 0.7, 0.85, 1.0, 1.15, 1.35)
+# Large canvases: coarser polar grid (full E² count is too expensive).
+_LARGE_N = 600
+_LARGE_ANGLE_STEP = 45
+_LARGE_RADII = (0.55, 0.75, 0.9, 1.1)
+_MIN_BRIDGE = 180.0
+_FOOTPRINT_MIN = 90.0
+
+
+def _apply_polar(
+    base: dict[str, tuple[float, float]],
+    members: list[str],
+    pivot: tuple[float, float],
+    *,
+    angle: float,
+    radius_scale: float,
+) -> dict[str, tuple[float, float]]:
+    cos_a, sin_a = math.cos(angle), math.sin(angle)
+    cx, cy = pivot
+    out = dict(base)
+    for n in members:
+        if n not in out:
+            continue
+        x, y = out[n]
+        dx, dy = x - cx, y - cy
+        rx = (dx * cos_a - dy * sin_a) * radius_scale
+        ry = (dx * sin_a + dy * cos_a) * radius_scale
+        out[n] = (cx + rx, cy + ry)
+    return out
+
+
+def _bbox_area(pos: dict[str, tuple[float, float]], ids: set[str] | None = None) -> float:
+    pts = [
+        pos[n]
+        for n in (ids or pos.keys())
+        if n in pos
+        and abs(pos[n][0]) <= _COORD_ABS_MAX
+        and abs(pos[n][1]) <= _COORD_ABS_MAX
+    ]
+    if len(pts) < 2:
+        return 1.0
+    xs = [p[0] for p in pts]
+    ys = [p[1] for p in pts]
+    return max(max(xs) - min(xs), 1e-6) * max(max(ys) - min(ys), 1e-6)
+
+
+def _footprint_hits(
+    pos: dict[str, tuple[float, float]],
+    moved: set[str],
+    frozen: set[str],
+    *,
+    min_dist: float = _FOOTPRINT_MIN,
+) -> int:
+    """Cheap center collisions between moved nodes and the rest."""
+    hits = 0
+    md2 = min_dist * min_dist
+    others = [
+        (pos[n][0], pos[n][1])
+        for n in pos
+        if n not in moved and n not in frozen
+        and abs(pos[n][0]) <= _COORD_ABS_MAX
+    ]
+    for n in moved:
+        if n not in pos:
+            continue
+        x, y = pos[n]
+        for ox, oy in others:
+            dx, dy = x - ox, y - oy
+            if dx * dx + dy * dy < md2:
+                hits += 1
+                break
+    return hits
+
+
+def _parse_groups(
+    groups: list[dict[str, Any]],
+    valid: set[str],
+) -> list[dict[str, Any]]:
+    counts: dict[str, int] = {}
+    parsed_raw: list[tuple[str, list[str], list[str]]] = []
+    for g in groups:
+        key = str(g.get("key") or "")
+        members = [str(n) for n in (g.get("node_ids") or []) if str(n) in valid]
+        if len(members) < 2:
+            continue
+        pivots = [str(p) for p in (g.get("pivots") or []) if str(p) in valid]
+        for n in members:
+            counts[n] = counts.get(n, 0) + 1
+        parsed_raw.append((key, members, pivots))
+    shared = {n for n, c in counts.items() if c > 1}
+    out: list[dict[str, Any]] = []
+    for key, members, pivots in parsed_raw:
+        piv = pivots or [n for n in members if n in shared]
+        exclusive = [n for n in members if n not in shared]
+        out.append(
+            {
+                "key": key,
+                "members": members,
+                "pivots": piv,
+                "exclusive": exclusive,
+                "shared": [n for n in members if n in shared],
+            }
+        )
+    return out
+
+
+def _external_bridges(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    members: set[str],
+    exclusive: set[str],
+) -> list[tuple[float, str, str]]:
+    """Return (length, inner_id, outer_id) sorted longest-first."""
+    bridges: list[tuple[float, str, str]] = []
+    for a, b in links:
+        if a not in pos or b not in pos:
+            continue
+        a_in, b_in = a in members, b in members
+        if a_in == b_in:
+            continue
+        inner, outer = (a, b) if a_in else (b, a)
+        # Prefer tips that are exclusive (true corridor leaf), not shared portals.
+        if exclusive and inner not in exclusive and inner not in members:
+            continue
+        L = math.hypot(pos[inner][0] - pos[outer][0], pos[inner][1] - pos[outer][1])
+        if L < _MIN_BRIDGE:
+            continue
+        bridges.append((L, inner, outer))
+    bridges.sort(reverse=True)
+    return bridges
+
+
+def _movers_for_group(g: dict[str, Any]) -> list[str]:
+    """Nodes that move under bridge orbit (exclusive; whole group if free)."""
+    if g["exclusive"]:
+        return list(g["exclusive"])
+    # No shared membership → whole body can orbit.
+    return list(g["members"])
+
+
+def rigid_orbit_candidates_for_group(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    group: dict[str, Any],
+    *,
+    angle_step: int = _DEFAULT_ANGLE_STEP,
+    radii: tuple[float, ...] = _DEFAULT_RADII,
+    bridges_per_group: int = 4,
+    cand_cap: int = 120,
+    x0: int | None = None,
+    area0: float | None = None,
+    x_slack: int = 40,
+) -> list[dict[str, Any]]:
+    """Ranked candidates for one rigid group (best first).
+
+    Ranking uses *partial* crossings (edges touching movers) — O(focus×E) —
+    not full global E². Callers verify top picks with ``count_edge_crossings``.
+    """
+    del x0  # reserved; global verify happens in round
+    members = set(group["members"])
+    exclusive = set(group["exclusive"])
+    movers = _movers_for_group(group)
+    if len(movers) < 2:
+        return []
+    frozen_portals = set(group["shared"] or group["pivots"])
+    bridges = _external_bridges(pos, links, members, exclusive)[
+        : max(1, bridges_per_group)
+    ]
+    if not bridges:
+        return []
+
+    area_base = _bbox_area(pos) if area0 is None else float(area0)
+    step = max(15, min(60, int(angle_step)))
+    rads = tuple(float(r) for r in radii) or _DEFAULT_RADII
+    touch_slack = max(4, int(x_slack))
+
+    cands: list[dict[str, Any]] = []
+    for L, inner, outer in bridges:
+        if outer not in pos or inner not in pos:
+            continue
+        pivot = pos[outer]
+        focus0 = set(movers) | {inner, outer}
+        x_touch0 = crossings_touching(pos, links, focus0)
+        for deg in range(0, 360, step):
+            ang = math.radians(deg)
+            for rs in rads:
+                if deg == 0 and abs(rs - 1.0) < 1e-9:
+                    continue
+                trial = _apply_polar(pos, movers, pivot, angle=ang, radius_scale=rs)
+                for p in frozen_portals:
+                    if p in pos:
+                        trial[p] = pos[p]
+                trial[outer] = pos[outer]
+                ov_cheap = _footprint_hits(trial, set(movers), frozen_portals)
+                if ov_cheap > 0:
+                    continue
+                x_touch = crossings_touching(trial, links, focus0)
+                if x_touch > x_touch0 + touch_slack:
+                    continue
+                area1 = _bbox_area(trial)
+                area_ratio = area_base / max(area1, 1e-6)
+                # Prefer denser bbox, then fewer partial crossings.
+                rank = (area_ratio, -(x_touch - x_touch0), -x_touch, -L)
+                cands.append(
+                    {
+                        "group_key": group["key"],
+                        "inner": inner,
+                        "outer": outer,
+                        "bridge_len": round(L, 1),
+                        "angle_deg": float(deg),
+                        "radius_scale": float(rs),
+                        "crossings_touch": x_touch,
+                        "crossings_touch0": x_touch0,
+                        "area_ratio": round(area_ratio, 4),
+                        "delta_touch": x_touch - x_touch0,
+                        "movers_n": len(movers),
+                        "rank": rank,
+                        "positions": {n: trial[n] for n in movers if n in trial},
+                    }
+                )
+                if len(cands) >= cand_cap * 2:
+                    break
+            if len(cands) >= cand_cap * 2:
+                break
+        if len(cands) >= cand_cap * 2:
+            break
+
+    cands.sort(key=lambda r: r["rank"], reverse=True)
+    useful = [
+        c
+        for c in cands
+        if c["area_ratio"] >= 1.004 or c["delta_touch"] < 0
+    ]
+    out = useful[:cand_cap] if useful else cands[: min(3, len(cands))]
+    for i, c in enumerate(out, start=1):
+        c["rank_i"] = i
+        c.pop("rank", None)
+    return out
+
+
+def rigid_orbit_round(
+    state: LayoutState,
+    *,
+    groups: list[dict[str, Any]] | None = None,
+    top_n: int = 24,
+    bridges_per_group: int = 3,
+    angle_step: int = _DEFAULT_ANGLE_STEP,
+    radii: tuple[float, ...] | list[float] | None = None,
+    cand_cap: int = 80,
+    x_slack: int | None = None,
+    max_accepts: int = 16,
+) -> OpResult:
+    """Greedy bridge-orbit densify over top rigid groups."""
+    from netx_api.mcp_remote.topology.layout_jobs import (
+        raise_if_cancelled,
+        report_progress,
+        touch_heartbeat,
+    )
+
+    st = state.copy()
+    groups = groups if groups is not None else groups_from_compose_meta(
+        (st.meta or {}).get("compose_views")
+    )
+    valid = {
+        n
+        for n, (x, y) in st.positions.items()
+        if abs(x) <= _COORD_ABS_MAX
+        and abs(y) <= _COORD_ABS_MAX
+        and math.isfinite(x)
+        and math.isfinite(y)
+    }
+    parsed = _parse_groups(groups or [], valid)
+    if not parsed:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="rigid_orbit",
+            params={"groups": 0},
+            note="rigid_orbit:no_groups",
+        )
+
+    large = len(st.positions) >= _LARGE_N or len(st.links) >= _LARGE_N
+    before = score_state(st, fast=True)
+    before_util = float((before.get("summary") or {}).get("util") or 0.0)
+    before_x = int((before.get("summary") or {}).get("crossings") or 0)
+    before_ov = len(overlapping_nodes(st))
+    area0 = _bbox_area(st.positions)
+    slack = (
+        max(8, int(before_x * 0.08))
+        if x_slack is None
+        else max(0, int(x_slack))
+    )
+    # Auto-coarsen polar grid on large canvases unless caller overrode radii.
+    use_angle = int(angle_step)
+    if radii is None and large:
+        rads = _LARGE_RADII
+        use_angle = max(use_angle, _LARGE_ANGLE_STEP)
+        bridges_per_group = min(int(bridges_per_group), 2)
+        cand_cap = min(int(cand_cap), 48)
+        top_n = min(int(top_n), 20)
+        max_accepts = min(int(max_accepts), 10)
+    else:
+        rads = tuple(float(r) for r in (radii or _DEFAULT_RADII))
+
+    # Order groups by longest external bridge (sparsity / stretch drivers).
+    scored_g: list[tuple[float, dict[str, Any]]] = []
+    for g in parsed:
+        br = _external_bridges(
+            st.positions, st.links, set(g["members"]), set(g["exclusive"])
+        )
+        if not br:
+            continue
+        scored_g.append((br[0][0], g))
+    scored_g.sort(key=lambda t: t[0], reverse=True)
+    ordered = [g for _L, g in scored_g[: max(1, int(top_n))]]
+
+    pos = {n: (float(p[0]), float(p[1])) for n, p in st.positions.items()}
+    links = list(st.links)
+    cur_x = before_x
+    cur_area = area0
+    moved: set[str] = set()
+    trace: list[dict[str, Any]] = []
+    accepts = 0
+    verify_k = 3 if large else 5
+
+    report_progress(
+        "rigid_orbit",
+        pct=48.0,
+        message=f"scan {len(ordered)} groups large={large}",
+        groups=len(ordered),
+    )
+
+    for gi, g in enumerate(ordered):
+        if accepts >= max(1, int(max_accepts)):
+            break
+        raise_if_cancelled()
+        touch_heartbeat()
+        if gi == 0 or gi % 2 == 0 or accepts > 0:
+            pct = 48.0 + 25.0 * (gi / max(len(ordered), 1))
+            report_progress(
+                "rigid_orbit",
+                pct=min(72.0, pct),
+                message=f"group {gi + 1}/{len(ordered)} accepts={accepts}",
+                group_key=g["key"],
+                accepts=accepts,
+            )
+        cands = rigid_orbit_candidates_for_group(
+            pos,
+            links,
+            g,
+            angle_step=use_angle,
+            radii=rads,
+            bridges_per_group=bridges_per_group,
+            cand_cap=cand_cap,
+            x0=cur_x,
+            area0=cur_area,
+            x_slack=slack,
+        )
+        if not cands:
+            trace.append({"group_key": g["key"], "skipped": "no_candidates"})
+            continue
+        applied = False
+        for cand in cands[:verify_k]:
+            trial = dict(pos)
+            movers_ids = set(cand.get("positions") or {})
+            for n, xy in (cand.get("positions") or {}).items():
+                trial[n] = xy
+            frozen = set(g.get("shared") or g.get("pivots") or [])
+            if _footprint_hits(trial, movers_ids, frozen) > 0:
+                continue
+            raise_if_cancelled()
+            touch_heartbeat()
+            x1 = count_edge_crossings(trial, links)
+            if x1 > cur_x + slack:
+                continue
+            area1 = _bbox_area(trial)
+            area_ratio = cur_area / max(area1, 1e-6)
+            util_proxy_up = area_ratio >= 1.004
+            x_down = x1 < cur_x
+            if not util_proxy_up and not x_down:
+                continue
+            # Accept (footprint-ok). Residual AABB → ensure_zero_overlap in layout_tool.
+            pos = trial
+            cur_x = x1
+            cur_area = area1
+            moved.update(movers_ids)
+            accepts += 1
+            applied = True
+            trace.append(
+                {
+                    "group_key": g["key"],
+                    "applied": True,
+                    "inner": cand.get("inner"),
+                    "outer": cand.get("outer"),
+                    "angle_deg": cand.get("angle_deg"),
+                    "radius_scale": cand.get("radius_scale"),
+                    "bridge_len": cand.get("bridge_len"),
+                    "crossings": x1,
+                    "area_ratio": round(area_ratio, 4),
+                    "movers_n": len(movers_ids),
+                }
+            )
+            break
+        if not applied:
+            best = cands[0]
+            trace.append(
+                {
+                    "group_key": g["key"],
+                    "skipped": "no_accept",
+                    "best_area_ratio": best.get("area_ratio"),
+                    "best_delta_touch": best.get("delta_touch"),
+                }
+            )
+
+    st.positions = pos
+    fin = score_state(st, fast=True)
+    end_util = float((fin.get("summary") or {}).get("util") or 0.0)
+    end_x = int((fin.get("summary") or {}).get("crossings") or 0)
+    end_ov = len(overlapping_nodes(st))
+    # Residual overlaps: keep accepted moves; layout_tool ensure_zero_overlap repairs.
+    # Only hard-revert when we never accepted and somehow got worse.
+    if end_ov > before_ov and accepts == 0:
+        return OpResult(
+            state=state.copy(),
+            moved=set(),
+            op="rigid_orbit",
+            params={
+                "reverted": True,
+                "reason": "overlaps",
+                "start_util": before_util,
+                "start_crossings": before_x,
+                "trace": trace,
+            },
+            note="rigid_orbit:reverted overlaps",
+        )
+    util_up = end_util > before_util + 1e-6
+    area_up = (area0 / max(_bbox_area(st.positions), 1e-6)) >= 1.004
+    x_ok = end_x <= before_x + slack
+    # Crossing-only gains count (util may stay flat when bbox is hull-dominated).
+    if not ((util_up or area_up or (accepts > 0 and end_x < before_x)) and x_ok) and accepts == 0:
+        meta = {
+            "reverted": True,
+            "reason": "no_gain",
+            "start_util": before_util,
+            "end_util": before_util,
+            "start_crossings": before_x,
+            "end_crossings": before_x,
+            "x_slack": slack,
+            "scanned_groups": len(ordered),
+            "trace": trace[:40],
+        }
+        st0 = state.copy()
+        st0.meta = dict(st0.meta or {})
+        st0.meta["rigid_orbit"] = meta
+        return OpResult(
+            state=st0,
+            moved=set(),
+            op="rigid_orbit",
+            params=meta,
+            note="rigid_orbit:reverted no_gain",
+        )
+
+    meta = {
+        "reverted": False,
+        "start_util": before_util,
+        "end_util": end_util,
+        "start_crossings": before_x,
+        "end_crossings": end_x,
+        "accepted": accepts,
+        "moved_n": len(moved),
+        "x_slack": slack,
+        "scanned_groups": len(ordered),
+        "bbox_area_ratio": round(area0 / max(_bbox_area(st.positions), 1e-6), 4),
+        "trace": trace[:60],
+    }
+    st.meta = dict(st.meta or {})
+    st.meta["rigid_orbit"] = meta
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="rigid_orbit",
+        params=meta,
+        note=(
+            f"rigid_orbit util {before_util:.4f}->{end_util:.4f} "
+            f"x {before_x}->{end_x} accepted={accepts}/{len(ordered)}"
+        ),
+    )
+
+
+def rigid_orbit_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    o = overrides or {}
+    out: dict[str, Any] = {}
+    if o.get("top_n") is not None:
+        try:
+            out["top_n"] = max(1, min(80, int(o["top_n"])))
+        except (TypeError, ValueError):
+            pass
+    if o.get("bridges_per_group") is not None:
+        try:
+            out["bridges_per_group"] = max(1, min(12, int(o["bridges_per_group"])))
+        except (TypeError, ValueError):
+            pass
+    if o.get("angle_step") is not None:
+        try:
+            out["angle_step"] = max(10, min(90, int(o["angle_step"])))
+        except (TypeError, ValueError):
+            pass
+    if o.get("cand_cap") is not None:
+        try:
+            out["cand_cap"] = max(20, min(400, int(o["cand_cap"])))
+        except (TypeError, ValueError):
+            pass
+    if o.get("max_accepts") is not None:
+        try:
+            out["max_accepts"] = max(1, min(64, int(o["max_accepts"])))
+        except (TypeError, ValueError):
+            pass
+    if o.get("x_slack") is not None:
+        try:
+            out["x_slack"] = max(0, int(o["x_slack"]))
+        except (TypeError, ValueError):
+            pass
+    if isinstance(o.get("radii"), (list, tuple)) and o["radii"]:
+        try:
+            out["radii"] = tuple(float(r) for r in o["radii"])
+        except (TypeError, ValueError):
+            pass
+    groups = o.get("rigid_groups") or o.get("_rigid_groups")
+    if isinstance(groups, list):
+        out["groups"] = groups
+    # round defaults true; allow explicit false for preview-only catalog
+    if "round" in o:
+        v = o.get("round")
+        out["round"] = v in (True, 1, "1", "true", "yes", "on") or v is True
+    else:
+        out["round"] = True
+    if o.get("group_key") is not None:
+        out["group_key"] = str(o.get("group_key") or "").strip()
+    return out

netx_api/mcp_remote/topology/layout_ops/ring_faces.py

@@ -0,0 +1,181 @@
+"""Minimal ring faces: detect chordless cycles and piercing by foreign edges.
+
+Stage-1/2 use this to keep ring interiors hollow (metro face integrity).
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import segments_properly_intersect
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState
+from netx_api.mcp_remote.topology.layout_topology_quality import (
+    RING_MAX_LEN,
+    find_short_chordless_cycles,
+)
+
+
+@dataclass(frozen=True)
+class RingFace:
+    node_ids: tuple[str, ...]
+
+    def as_dict(self) -> dict[str, Any]:
+        return {"n": len(self.node_ids), "node_ids": list(self.node_ids)}
+
+
+def extract_ring_faces(
+    state: LayoutState,
+    *,
+    max_len: int = min(6, RING_MAX_LEN),
+    max_cycles: int = 80,
+) -> list[RingFace]:
+    cycles = find_short_chordless_cycles(
+        state.adj, max_len=max_len, max_cycles=max_cycles
+    )
+    faces = [RingFace(tuple(c)) for c in cycles]
+    faces.sort(key=lambda f: (len(f.node_ids), f.node_ids[0] if f.node_ids else ""))
+    return faces
+
+
+def _point_in_poly(
+    x: float, y: float, poly: list[tuple[float, float]]
+) -> bool:
+    """Ray casting; boundary counts as outside (conservative)."""
+    n = len(poly)
+    if n < 3:
+        return False
+    inside = False
+    j = n - 1
+    for i in range(n):
+        xi, yi = poly[i]
+        xj, yj = poly[j]
+        if ((yi > y) != (yj > y)) and (
+            x < (xj - xi) * (y - yi) / (yj - yi + 1e-15) + xi
+        ):
+            inside = not inside
+        j = i
+    return inside
+
+
+def ring_polygon(
+    face: RingFace, pos: dict[str, tuple[float, float]]
+) -> list[tuple[float, float]] | None:
+    pts = [pos[n] for n in face.node_ids if n in pos]
+    if len(pts) < 3:
+        return None
+    return pts
+
+
+def count_ring_pierces(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+    faces: list[RingFace],
+) -> dict[str, Any]:
+    """Foreign edges that properly cross a ring boundary edge."""
+    pierced = 0
+    pierce_x = 0
+    for face in faces:
+        rset = set(face.node_ids)
+        poly = ring_polygon(face, pos)
+        if not poly:
+            continue
+        n = len(face.node_ids)
+        ring_edges = []
+        for i in range(n):
+            a, b = face.node_ids[i], face.node_ids[(i + 1) % n]
+            if a in pos and b in pos:
+                ring_edges.append((a, b))
+        hit = False
+        for a, b in links:
+            if a not in pos or b not in pos:
+                continue
+            # foreign if at least one endpoint outside ring
+            if a in rset and b in rset:
+                continue
+            p1, p2 = pos[a], pos[b]
+            for u, v in ring_edges:
+                if len({a, b, u, v}) < 4:
+                    continue
+                if segments_properly_intersect(p1, p2, pos[u], pos[v]):
+                    pierce_x += 1
+                    hit = True
+        if hit:
+            pierced += 1
+    return {
+        "ring_count": len(faces),
+        "rings_pierced": pierced,
+        "pierce_crossings": pierce_x,
+    }
+
+
+def place_ring_rectangle(
+    face: RingFace,
+    *,
+    center: tuple[float, float],
+    width: float,
+    height: float,
+    start_angle: float = -math.pi / 2,
+) -> dict[str, tuple[float, float]]:
+    """Place ring nodes on an axis-aligned rectangle (hollow face)."""
+    nodes = list(face.node_ids)
+    n = len(nodes)
+    if n < 3:
+        return {}
+    cx, cy = center
+    hw, hh = width / 2, height / 2
+    # Perimeter parametrization 0..1 around rectangle
+    perim = 2 * (width + height)
+    out: dict[str, tuple[float, float]] = {}
+    for i, nid in enumerate(nodes):
+        t = (i / n + (start_angle + math.pi / 2) / (2 * math.pi)) % 1.0
+        d = t * perim
+        if d <= width:
+            x, y = cx - hw + d, cy - hh
+        elif d <= width + height:
+            x, y = cx + hw, cy - hh + (d - width)
+        elif d <= 2 * width + height:
+            x, y = cx + hw - (d - width - height), cy + hh
+        else:
+            x, y = cx - hw, cy + hh - (d - 2 * width - height)
+        out[nid] = (x, y)
+    return out
+
+
+def eject_intruders(
+    pos: dict[str, tuple[float, float]],
+    faces: list[RingFace],
+    *,
+    push: float = 40.0,
+    protected: set[str] | None = None,
+) -> dict[str, tuple[float, float]]:
+    """Push non-ring nodes out of ring polygons."""
+    protected = protected or set()
+    out = dict(pos)
+    for face in faces:
+        rset = set(face.node_ids)
+        poly = ring_polygon(face, out)
+        if not poly:
+            continue
+        cx = sum(p[0] for p in poly) / len(poly)
+        cy = sum(p[1] for p in poly) / len(poly)
+        for nid, (x, y) in list(out.items()):
+            if nid in rset or nid in protected:
+                continue
+            if not _point_in_poly(x, y, poly):
+                continue
+            dx, dy = x - cx, y - cy
+            dist = math.hypot(dx, dy) or 1.0
+            out[nid] = (cx + dx / dist * (dist + push), cy + dy / dist * (dist + push))
+    return out
+
+
+def ring_faces_report(state: LayoutState) -> dict[str, Any]:
+    faces = extract_ring_faces(state)
+    pierces = count_ring_pierces(state.positions, state.links, faces)
+    return {
+        **pierces,
+        "sample": [f.as_dict() for f in faces[:10]],
+        "tip": "最小环应中空；channel_metro / protect_rings 优先降 pierced。",
+    }

netx_api/mcp_remote/topology/layout_ops/rings.py

@@ -0,0 +1,35 @@
+"""Ring/chain layout entry: dual-hub min-rings first, else UME petals.
+
+- ``min_rings``: two agg hubs + ≥2 parallel corridors → nested ellipse bands
+  (A1-ULU / PLAU↔ATP style). Prefer when structure says ``agg_bar``.
+- ``ume_petals``: rect-perimeter petals around AN anchors (legacy Sugiyama path).
+"""
+
+from __future__ import annotations
+
+from netx_api.mcp_remote.topology.layout_ops.min_rings import build_min_ring_skeleton
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_ops.sugiyama import build_sugiyama_layout
+
+
+def build_ring_skeleton(state: LayoutState, params: LayoutParams | None = None) -> OpResult:
+    params = params or LayoutParams()
+    dual = build_min_ring_skeleton(state, params)
+    if dual is not None:
+        return OpResult(
+            state=dual.state,
+            moved=dual.moved,
+            op="build_ring_skeleton",
+            params={**(dual.params or {}), "mode": "min_rings"},
+            note=dual.note,
+        )
+    out = build_sugiyama_layout(state, params)
+    st = out.state
+    st.meta["rings_mode"] = st.meta.get("rings_mode") or "ume_petals"
+    return OpResult(
+        state=st,
+        moved=out.moved,
+        op="build_ring_skeleton",
+        params={**(out.params or {}), "mode": st.meta.get("rings_mode", "ume_petals")},
+        note=out.note or "UME petal rect-perimeter skeleton",
+    )

netx_api/mcp_remote/topology/layout_ops/scope.py

@@ -0,0 +1,153 @@
+"""Block / scope selection — ops act only on active nodes."""
+
+from __future__ import annotations
+
+from typing import Any, Iterable
+
+from netx_api.mcp_remote.topology.layout_ops.graph_util import bbox, connected_components
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState, OpResult
+
+
+def active_nodes(state: LayoutState) -> set[str]:
+    """Nodes currently in scope (all if scope is None/empty meaning unrestricted)."""
+    if state.scope is None:
+        return set(state.positions.keys())
+    return {n for n in state.scope if n in state.positions}
+
+
+def movable_nodes(state: LayoutState, *, respect_pins: bool = True) -> set[str]:
+    """Nodes an op may move: in scope, not hard agg/core, optionally not soft-pinned."""
+    act = active_nodes(state)
+    hard = {n for n in act if state.layers.get(n) in ("agg", "core")}
+    out = act - hard
+    if respect_pins:
+        out -= set(state.pinned)
+    return out
+
+
+def list_blocks(
+    state: LayoutState,
+    *,
+    mode: str = "component",
+) -> list[set[str]]:
+    """Partition graph into blocks for per-block recipes.
+
+    Modes:
+      - component: hard CC (default; existing recipes)
+      - layer / bbox_quad: coarse buckets
+      - hub_territory: soft blocks by hub-seeded BFS (our algo)
+      - leiden / soft: hub territories + optional igraph Leiden on leftovers
+    """
+    ids = set(state.positions.keys())
+    m = (mode or "component").strip().lower()
+    if m == "auto":
+        from netx_api.mcp_remote.topology.layout_ops.partition import resolve_block_mode
+
+        m = resolve_block_mode(state, "auto")
+    if m == "component":
+        active = {
+            n
+            for n in ids
+            if state.layers.get(n) in ("core", "agg", "access")
+        }
+        comps = connected_components(active, state.adj)
+        blocks = [set(c) for c in comps if c]
+        # orphans / other
+        rest = ids - {n for b in blocks for n in b}
+        if rest:
+            blocks.append(rest)
+        return blocks
+    if m in {"hub_territory", "leiden", "soft"}:
+        from netx_api.mcp_remote.topology.layout_ops.partition import (
+            partition_soft_blocks,
+            soft_blocks_as_sets,
+        )
+
+        return soft_blocks_as_sets(partition_soft_blocks(state, mode=m))
+    if m == "layer":
+        by: dict[str, set[str]] = {}
+        for n in ids:
+            by.setdefault(state.layers.get(n, "other"), set()).add(n)
+        return [by[k] for k in sorted(by.keys()) if by[k]]
+    if m == "bbox_quad":
+        if not ids:
+            return []
+        x0, y0, x1, y1 = bbox(state.positions)
+        mx, my = (x0 + x1) / 2, (y0 + y1) / 2
+        quads: list[set[str]] = [set() for _ in range(4)]
+        for n, (x, y) in state.positions.items():
+            qi = (0 if x < mx else 1) + (0 if y < my else 2)
+            quads[qi].add(n)
+        return [q for q in quads if q]
+    # single full block
+    return [ids] if ids else []
+
+
+def select_scope(
+    state: LayoutState,
+    *,
+    mode: str = "all",
+    node_ids: Iterable[str] | None = None,
+    component_index: int | None = None,
+    layer: str | None = None,
+    bbox_region: tuple[float, float, float, float] | None = None,
+) -> OpResult:
+    """Set state.scope. mode=all clears scope (full graph)."""
+    st = state.copy()
+    pos = st.positions
+    if mode == "all" or (mode == "ids" and not node_ids):
+        st.scope = None
+        return OpResult(
+            state=st, moved=set(), op="select_scope", params={"mode": "all"}, note="scope=all"
+        )
+
+    if mode == "ids" and node_ids is not None:
+        scope = {n for n in node_ids if n in pos}
+    elif mode == "component":
+        blocks = list_blocks(st, mode="component")
+        idx = 0 if component_index is None else int(component_index)
+        scope = blocks[idx] if 0 <= idx < len(blocks) else set()
+    elif mode == "layer":
+        lyr = layer or "access"
+        scope = {n for n in pos if st.layers.get(n) == lyr}
+    elif mode == "bbox" and bbox_region is not None:
+        x0, y0, x1, y1 = bbox_region
+        scope = {
+            n
+            for n, (x, y) in pos.items()
+            if x0 <= x <= x1 and y0 <= y <= y1
+        }
+    elif mode == "bbox_quad":
+        blocks = list_blocks(st, mode="bbox_quad")
+        idx = 0 if component_index is None else int(component_index)
+        scope = blocks[idx] if 0 <= idx < len(blocks) else set()
+    else:
+        scope = set(pos.keys())
+
+    st.scope = scope
+    return OpResult(
+        state=st,
+        moved=set(),
+        op="select_scope",
+        params={"mode": mode, "scope_n": len(scope), "component_index": component_index},
+        note=f"scope={mode} n={len(scope)}",
+    )
+
+
+def map_blocks(
+    state: LayoutState,
+    *,
+    mode: str = "component",
+) -> OpResult:
+    """Record block partition in meta (for agents / foreach). Does not change scope."""
+    st = state.copy()
+    blocks = list_blocks(st, mode=mode)
+    st.meta["blocks"] = [sorted(b) for b in blocks]
+    st.meta["block_mode"] = mode
+    return OpResult(
+        state=st,
+        moved=set(),
+        op="map_blocks",
+        params={"mode": mode, "blocks_n": len(blocks), "sizes": [len(b) for b in blocks]},
+        note=f"blocks={len(blocks)} mode={mode}",
+    )

netx_api/mcp_remote/topology/layout_ops/score.py

@@ -0,0 +1,57 @@
+"""Score a LayoutState with layout_metrics + layout_stats composite."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState, OpResult
+from netx_api.mcp_remote.topology.layout_stats import analyze_layout_stats
+
+
+def score_state(
+    state: LayoutState,
+    *,
+    ume_reference: bool = False,
+    fast: bool | None = None,
+) -> dict[str, Any]:
+    nodes = [
+        {
+            "fabric_node_id": n,
+            "name": state.names.get(n, n),
+            "x": state.positions[n][0],
+            "y": state.positions[n][1],
+        }
+        for n in state.positions
+    ]
+    edges = [{"a_node_id": a, "b_node_id": b} for a, b in state.links]
+    # Giant canvases: skip ring pierce (O(rings·E²)-ish) unless forced full.
+    if fast is None:
+        fast = len(nodes) >= 600 or len(state.links) >= 700
+    return analyze_layout_stats(
+        nodes, edges, ume_reference=ume_reference, fast=bool(fast)
+    )
+
+
+def score_op(state: LayoutState, *, ume_reference: bool = False) -> OpResult:
+    st = state.copy()
+    m = score_state(st, ume_reference=ume_reference)
+    st.meta["last_score"] = m
+    report = m.get("report") or {}
+    verdict = report.get("verdict") or m.get("summary") or {}
+    return OpResult(
+        state=st,
+        moved=set(),
+        op="score",
+        params={
+            "total": verdict.get("total"),
+            "overall": verdict.get("overall"),
+            "headline": verdict.get("headline"),
+            "overlap": (report.get("overlap") or {}).get("status"),
+            "crossing": (report.get("crossing") or {}).get("status"),
+            "sparsity": (report.get("sparsity") or {}).get("status"),
+            "edge_crossings": m.get("edge_crossings"),
+            "nn_p50": m.get("nn_p50"),
+            "space_utilization": m.get("space_utilization"),
+        },
+        note=f"score={verdict.get('total')} {verdict.get('headline')}",
+    )

netx_api/mcp_remote/topology/layout_ops/sides.py

@@ -0,0 +1,87 @@
+"""Atomic op: place side-branch NEs on both sides of spine."""
+
+from __future__ import annotations
+
+import math
+
+from netx_api.mcp_remote.topology.layout_ops.graph_util import spine_backbone
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def place_side_branches(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """X-gain + lane push for non-pinned / off-spine access nodes."""
+    params = params or LayoutParams()
+    st = state.copy()
+    pos = dict(st.positions)
+    if not pos:
+        return OpResult(state=st, moved=set(), op="place_side_branches", note="empty")
+
+    # Amplify X about global center (borrow left/right).
+    xs = [pos[n][0] for n in pos]
+    cx = sum(xs) / len(xs)
+    pos = {n: (cx + (x - cx) * params.x_gain, y) for n, (x, y) in pos.items()}
+    moved = set(pos.keys())
+
+    ens = [n for n in pos if st.layers.get(n) == "access"]
+    if ens:
+        if st.spine:
+            spine = spine_backbone([n for n in ens if n in st.spine], st.adj, st.names)
+            if len(spine) < 2:
+                spine = spine_backbone(ens, st.adj, st.names)
+        else:
+            spine = spine_backbone(ens, st.adj, st.names)
+        spine_set = set(spine)
+        st.spine = spine_set
+        tang: dict[str, tuple[float, float]] = {}
+        for i, n in enumerate(spine):
+            if i + 1 < len(spine):
+                x0, y0 = pos[n]
+                x1, y1 = pos[spine[i + 1]]
+            elif i > 0:
+                x1, y1 = pos[n]
+                x0, y0 = pos[spine[i - 1]]
+            else:
+                tang[n] = (1.0, 0.0)
+                continue
+            dx, dy = x1 - x0, y1 - y0
+            L = math.hypot(dx, dy) or 1.0
+            tang[n] = (dx / L, dy / L)
+
+        for n in ens:
+            if n in spine_set or n in st.pinned:
+                continue
+            sx, sy = pos[n]
+            nearest = min(
+                spine, key=lambda s: math.hypot(pos[s][0] - sx, pos[s][1] - sy)
+            )
+            tx, ty = tang.get(nearest, (1.0, 0.0))
+            nx, ny = -ty, tx
+            px, py = pos[nearest]
+            cross = tx * (sy - py) - ty * (sx - px)
+            sign = 1.0 if cross >= 0 else -1.0
+            cur = math.hypot(sx - px, sy - py)
+            target = max(params.lane, cur)
+            if cur < 1e-6:
+                pos[n] = (px + nx * sign * target, py + ny * sign * target)
+            elif cur < target:
+                sc = target / cur
+                pos[n] = (px + (sx - px) * sc, py + (sy - py) * sc)
+
+    # Pin skeleton after sides placed
+    pin = {
+        n
+        for n in pos
+        if st.layers.get(n) in ("agg", "core") or n in st.spine
+    }
+    st.positions = pos
+    st.pinned = pin
+    st.last_moved = moved - pin
+    return OpResult(
+        state=st,
+        moved=st.last_moved,
+        op="place_side_branches",
+        params={"x_gain": params.x_gain, "lane": params.lane, "pinned_n": len(pin)},
+        note="side lanes from spine; pinned=agg+core+spine",
+    )

netx_api/mcp_remote/topology/layout_ops/sink_dual_units.py

@@ -0,0 +1,497 @@
+"""Select dual-unit batches to sink from a root canvas onto a child region.
+
+Pure selection / park / per-unit layout helpers — HTTP moves live in http_tools.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops.dual_units import DualUnit, layout_dual_unit
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState
+
+
+def _portal_share_counts(units: list[DualUnit]) -> dict[str, int]:
+    counts: dict[str, int] = {}
+    for u in units:
+        for p in (u.portal_a, u.portal_b):
+            counts[p] = counts.get(p, 0) + 1
+    return counts
+
+
+def unit_detach_score(u: DualUnit, share: dict[str, int]) -> tuple[int, int, int]:
+    """Tie-break only: less shared portals, then lower unit_id (stable)."""
+    shared = sum(1 for p in (u.portal_a, u.portal_b) if share.get(p, 0) > 1)
+    share_sum = share.get(u.portal_a, 0) + share.get(u.portal_b, 0)
+    return (shared, share_sum, int(u.unit_id))
+
+
+def select_dual_unit_batch(
+    units: list[DualUnit],
+    *,
+    max_units: int = 3,
+    min_nodes: int = 8,
+    max_nodes: int = 300,
+    max_batch_nodes: int = 400,
+    exclude_ids: set[str] | None = None,
+    keep_ids: set[str] | None = None,
+    sink_ids: set[str] | None = None,
+    prefer_pure: bool = False,
+    prefer_core_eye: bool = True,
+    prefer_top_eye: bool | None = None,
+    layers: dict[str, str] | None = None,
+) -> list[DualUnit]:
+    """Greedy pick detachable dual-units within size / batch caps.
+
+    Eyes walk **top→down** (core → agg → access): prefer core/agg portal
+    pairs and maximize movable membership. ``prefer_core_eye`` is an alias
+    for ``prefer_top_eye`` (default true).
+    """
+    exclude_ids = exclude_ids or set()
+    keep_ids = keep_ids or set()
+    layers = layers or {}
+    if prefer_top_eye is None:
+        prefer_top_eye = prefer_core_eye
+    share = _portal_share_counts(units)
+    candidates: list[DualUnit] = []
+    for u in units:
+        members = u.member_ids()
+        if not members:
+            continue
+        movable = members - keep_ids
+        if sink_ids is not None:
+            movable = movable & sink_ids
+        if not movable or movable.issubset(exclude_ids):
+            continue
+        n = len(members)
+        if n < int(min_nodes) or n > int(max_nodes):
+            continue
+        if members and members.issubset(exclude_ids | keep_ids):
+            continue
+        # Need enough *movable* mass for this level phase.
+        if len(movable) < max(2, min(4, int(min_nodes) // 2)):
+            continue
+        candidates.append(u)
+
+    def _movable(u: DualUnit) -> set[str]:
+        m = u.member_ids() - keep_ids
+        if sink_ids is not None:
+            m = m & sink_ids
+        return m
+
+    def _portal_tier(nid: str) -> int:
+        ly = (layers.get(nid) or "").strip().lower()
+        if ly == "core":
+            return 0
+        if ly == "agg":
+            return 1
+        if ly == "access":
+            return 2
+        return 3
+
+    def _eye_key(u: DualUnit) -> tuple[int, int]:
+        t = sorted((_portal_tier(u.portal_a), _portal_tier(u.portal_b)))
+        return (t[0], t[1])
+
+    def _score(u: DualUnit) -> tuple:
+        mov = _movable(u)
+        keep_portals = sum(1 for p in (u.portal_a, u.portal_b) if p in keep_ids)
+        pure_penalty = keep_portals if prefer_pure else 0
+        # Top-down eye tier, then max movable / membership.
+        eye = _eye_key(u) if prefer_top_eye else (0, 0)
+        return (
+            pure_penalty,
+            eye,
+            -len(mov),
+            -len(u.member_ids()),
+            *unit_detach_score(u, share),
+        )
+
+    candidates.sort(key=_score)
+
+    picked: list[DualUnit] = []
+    claimed: set[str] = set()
+    for u in candidates:
+        if len(picked) >= int(max_units):
+            break
+        movable = _movable(u)
+        interior = movable - {u.portal_a, u.portal_b}
+        if interior & claimed:
+            continue
+        next_ids = claimed | movable
+        if len(next_ids) > int(max_batch_nodes):
+            continue
+        picked.append(u)
+        claimed |= movable
+    return picked
+
+
+def batch_node_ids(
+    units: list[DualUnit],
+    *,
+    keep_ids: set[str] | None = None,
+    sink_ids: set[str] | None = None,
+) -> list[str]:
+    keep_ids = keep_ids or set()
+    out: list[str] = []
+    seen: set[str] = set()
+    for u in units:
+        for nid in sorted(u.member_ids()):
+            if not nid or nid in seen or nid in keep_ids:
+                continue
+            if sink_ids is not None and nid not in sink_ids:
+                continue
+            seen.add(nid)
+            out.append(nid)
+    return out
+
+
+def leftover_batch_ids(
+    source_ids: list[str],
+    *,
+    max_batch_nodes: int = 120,
+    exclude_ids: set[str] | None = None,
+    keep_ids: set[str] | None = None,
+    sink_ids: set[str] | None = None,
+) -> list[str]:
+    """When dual_units are exhausted, take a plain leftover chunk."""
+    exclude_ids = exclude_ids or set()
+    keep_ids = keep_ids or set()
+    out: list[str] = []
+    for nid in source_ids:
+        sid = str(nid or "").strip()
+        if not sid or sid.startswith("region:"):
+            continue
+        if sid in exclude_ids or sid in keep_ids:
+            continue
+        if sink_ids is not None and sid not in sink_ids:
+            continue
+        out.append(sid)
+        if len(out) >= int(max_batch_nodes):
+            break
+    return out
+
+
+def park_positions(
+    src_pos: dict[str, tuple[float, float]],
+    node_ids: list[str],
+    *,
+    sink_pos: dict[str, tuple[float, float]] | None = None,
+    pad: float = 280.0,
+    links: list[tuple[str, str]] | None = None,
+) -> list[dict[str, Any]]:
+    """Park batch via compose_orbit-style block sweep (not fixed right)."""
+    pts = {nid: src_pos[nid] for nid in node_ids if nid in src_pos}
+    if not pts:
+        out: list[dict[str, Any]] = []
+        cols = max(4, int(len(node_ids) ** 0.5) or 1)
+        for i, nid in enumerate(node_ids):
+            out.append(
+                {
+                    "fabric_node_id": nid,
+                    "x": float((i % cols) * 160),
+                    "y": float((i // cols) * 120),
+                }
+            )
+        return out
+
+    # Normalize local bbox to origin before orbit attach.
+    xs = [p[0] for p in pts.values()]
+    ys = [p[1] for p in pts.values()]
+    min_x, min_y = min(xs), min(ys)
+    local = {nid: (x - min_x, y - min_y) for nid, (x, y) in pts.items()}
+    world, _meta = orbit_attach_to_sink(
+        local,
+        sink_pos or {},
+        links=links or [],
+        pad=pad,
+    )
+    return positions_to_patch(world)
+
+
+def units_as_batch_rows(
+    units: list[DualUnit],
+    names: dict[str, str] | None = None,
+) -> list[dict[str, Any]]:
+    names = names or {}
+    rows: list[dict[str, Any]] = []
+    for u in units:
+        d = u.as_dict(names)
+        rows.append(
+            {
+                "unit_id": d.get("unit_id"),
+                "node_count": d.get("node_count"),
+                "portals": [d.get("portal_a"), d.get("portal_b")],
+                "names": [d.get("portal_a_name"), d.get("portal_b_name")],
+                "node_ids": d.get("node_ids") or [],
+            }
+        )
+    return rows
+
+
+def subgraph_state(state: LayoutState, members: set[str]) -> LayoutState:
+    """Induced subgraph for one dual-unit (local layout_dual_unit)."""
+    ids = {m for m in members if m}
+    adj = {n: {v for v in state.adj.get(n, ()) if v in ids} for n in ids}
+    links = [(a, b) for a, b in state.links if a in ids and b in ids]
+    return LayoutState(
+        positions={n: state.positions[n] for n in ids if n in state.positions},
+        names={n: state.names.get(n, n) for n in ids},
+        layers={n: state.layers.get(n, "access") for n in ids},
+        links=links,
+        adj=adj,
+        meta={"ids": sorted(ids)},
+    )
+
+
+def _bbox(pos: dict[str, tuple[float, float]]) -> tuple[float, float, float, float]:
+    xs = [p[0] for p in pos.values()]
+    ys = [p[1] for p in pos.values()]
+    return min(xs), min(ys), max(xs), max(ys)
+
+
+def _translate(
+    pos: dict[str, tuple[float, float]], dx: float, dy: float
+) -> dict[str, tuple[float, float]]:
+    return {n: (x + dx, y + dy) for n, (x, y) in pos.items()}
+
+
+def orbit_attach_to_sink(
+    local: dict[str, tuple[float, float]],
+    sink_pos: dict[str, tuple[float, float]],
+    *,
+    links: list[tuple[str, str]] | None = None,
+    pad: float = 280.0,
+    angle_step: int = 30,
+    radii: tuple[float, ...] = (0.75, 1.0, 1.25, 1.55, 1.95),
+    cand_cap: int = 96,
+) -> tuple[dict[str, tuple[float, float]], dict[str, Any]]:
+    """Block-sweep attach: reuse compose_orbit orphan/portal orbit pick.
+
+    Scores candidates by (partial crossings, footprint hits, bridge length)
+    against the existing sink world — same spirit as compose_orbit hang-block.
+    """
+    from netx_api.mcp_remote.topology.layout_ops.compose_orbit import (
+        _orbit_candidates_one_portal,
+        _orbit_candidates_orphan,
+        _orbit_candidates_two_portal,
+        _pick_best,
+        _thin_candidates,
+    )
+    from netx_api.mcp_remote.topology.layout_ops.compose_views import (
+        _rank_shared_pivots,
+        _rigid_align_to_world,
+    )
+
+    link_list = list(links or [])
+    if not local:
+        return {}, {"via": "empty", "cands": 0}
+
+    if not sink_pos:
+        xs = [p[0] for p in local.values()]
+        ys = [p[1] for p in local.values()]
+        min_x, min_y = min(xs), min(ys)
+        seeded = {
+            n: (x - min_x + 40.0, y - min_y + 40.0) for n, (x, y) in local.items()
+        }
+        return seeded, {"via": "seed", "cands": 1, "score": [0, 0, 0.0]}
+
+    world = dict(sink_pos)
+    shared_raw = [nid for nid in local if nid in world]
+    # membership hint: portals already on sink count as glue
+    membership = {n: 2 for n in shared_raw}
+    shared = _rank_shared_pivots(shared_raw, membership)
+    exclusive = [n for n in local if n not in set(shared)]
+    old_ids = set(world)
+
+    if shared:
+        prefer = None
+        wxs = [p[0] for p in world.values()]
+        wys = [p[1] for p in world.values()]
+        if wxs and wys:
+            prefer = (0.5 * (min(wxs) + max(wxs)), 0.5 * (min(wys) + max(wys)))
+        aligned = _rigid_align_to_world(
+            local,
+            world,
+            shared,
+            prefer_center=prefer,
+            links=link_list,
+        )
+        if len(shared) >= 2:
+            cands = _orbit_candidates_two_portal(
+                world,
+                aligned,
+                shared[:2],
+                exclusive,
+                local,
+                angle_step=angle_step,
+            )
+            via = "orbit_dual"
+        else:
+            cands = _orbit_candidates_one_portal(
+                world,
+                aligned,
+                shared[0],
+                exclusive,
+                angle_step=angle_step,
+                radii=radii,
+            )
+            via = "orbit_portal"
+    else:
+        cands = _orbit_candidates_orphan(
+            world,
+            local,
+            pad=pad,
+            angle_step=angle_step,
+            radii=radii,
+            links=link_list,
+        )
+        via = "orbit_orphan"
+
+    cands = _thin_candidates(cands, cand_cap)
+
+    new_ids = set(exclusive) if exclusive else set(local) - old_ids
+    # Fresh rescore every call — never sticky pick.
+    best, best_sc, best_i = _pick_best(cands, link_list, new_ids, old_ids)
+    out = {n: best[n] for n in local if n in best}
+    meta = {
+        "via": via,
+        "shared_n": len(shared),
+        "shared": shared[:4],
+        "cands": len(cands),
+        "best_i": best_i,
+        "score": [best_sc[0], best_sc[1], round(best_sc[2], 1)],
+        "exclusive_n": len(exclusive),
+    }
+    return out, meta
+
+
+def layout_and_pack_batch(
+    state: LayoutState,
+    units: list[DualUnit],
+    *,
+    sink_pos: dict[str, tuple[float, float]] | None = None,
+    pad: float = 280.0,
+    unit_gap: float = 220.0,
+    params: LayoutParams | None = None,
+    links: list[tuple[str, str]] | None = None,
+) -> tuple[dict[str, tuple[float, float]], list[dict[str, Any]], dict[str, Any]]:
+    """layout_dual_unit each unit, strip-pack, then orbit-attach onto sink.
+
+    Attach uses compose_orbit block sweep (partial crossings / overlap /
+    bridge length) — not a fixed right-side park.
+    """
+    params = params or LayoutParams()
+    reports: list[dict[str, Any]] = []
+    packed: dict[str, tuple[float, float]] = {}
+    cursor_x = 0.0
+    row_max_h = 0.0
+
+    for u in units:
+        members = u.member_ids()
+        if not members:
+            continue
+        sub = subgraph_state(state, members)
+        # Seed missing coords so layout_dual_unit has a canvas.
+        for n in members:
+            if n not in sub.positions:
+                sub.positions[n] = (0.0, 0.0)
+        op = layout_dual_unit(sub, params, unit=u)
+        local = {
+            n: op.state.positions[n]
+            for n in members
+            if n in op.state.positions
+        }
+        if not local:
+            reports.append(
+                {
+                    "unit_id": u.unit_id,
+                    "accepted": False,
+                    "note": "no_positions",
+                    "unit_crossings": None,
+                }
+            )
+            continue
+
+        # Align shared portals already placed by an earlier unit in this batch.
+        shared = [p for p in (u.portal_a, u.portal_b) if p in packed and p in local]
+        if shared:
+            # Translate so first shared portal matches world.
+            piv = shared[0]
+            dx = packed[piv][0] - local[piv][0]
+            dy = packed[piv][1] - local[piv][1]
+            local = _translate(local, dx, dy)
+            # Exclusive members only — keep prior portal coords.
+            for n, xy in local.items():
+                if n not in packed:
+                    packed[n] = xy
+            min_x, min_y, max_x, max_y = _bbox(
+                {n: packed[n] for n in members if n in packed}
+            )
+            cursor_x = max(cursor_x, max_x + float(unit_gap))
+            row_max_h = max(row_max_h, max_y - min_y)
+        else:
+            min_x, min_y, max_x, max_y = _bbox(local)
+            local = _translate(local, cursor_x - min_x, -min_y)
+            for n, xy in local.items():
+                if n not in packed:
+                    packed[n] = xy
+            w = max_x - min_x
+            h = max_y - min_y
+            cursor_x += w + float(unit_gap)
+            row_max_h = max(row_max_h, h)
+
+        reports.append(
+            {
+                "unit_id": u.unit_id,
+                "accepted": bool(op.params.get("accepted")),
+                "unit_crossings": op.params.get("unit_crossings"),
+                "note": op.note,
+                "node_count": len(members),
+                "portals": [u.portal_a, u.portal_b],
+            }
+        )
+
+    if not packed:
+        return {}, reports, {"via": "empty"}
+
+    # Prefer fabric links from caller (sink∪source); fall back to state links.
+    attach_links = list(links) if links is not None else list(state.links)
+    world, attach_meta = orbit_attach_to_sink(
+        packed,
+        sink_pos or {},
+        links=attach_links,
+        pad=pad,
+    )
+    return world, reports, attach_meta
+
+
+def positions_to_patch(
+    pos: dict[str, tuple[float, float]],
+) -> list[dict[str, Any]]:
+    return [
+        {"fabric_node_id": nid, "x": float(xy[0]), "y": float(xy[1])}
+        for nid, xy in sorted(pos.items())
+    ]
+
+
+def merge_view_links(
+    *payloads: dict[str, Any],
+) -> list[tuple[str, str]]:
+    """Undirected fabric pairs from one or more view GET payloads."""
+    seen: set[tuple[str, str]] = set()
+    out: list[tuple[str, str]] = []
+    for payload in payloads:
+        for e in payload.get("edges") or []:
+            if not isinstance(e, dict):
+                continue
+            a = str(e.get("a_node_id") or e.get("source") or "").strip()
+            b = str(e.get("b_node_id") or e.get("target") or "").strip()
+            if not a or not b or a == b:
+                continue
+            key = (a, b) if a < b else (b, a)
+            if key in seen:
+                continue
+            seen.add(key)
+            out.append(key)
+    return out

netx_api/mcp_remote/topology/layout_ops/skeleton.py

@@ -0,0 +1,231 @@
+"""Atomic op: build corridor skeleton (Tutte + island pack)."""
+
+from __future__ import annotations
+
+from collections import defaultdict, deque
+from itertools import product
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.graph_util import (
+    bbox,
+    connected_components,
+    order_ans,
+    spine_backbone,
+)
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _place_component_tutte(
+    comp: list[str],
+    state: LayoutState,
+    params: LayoutParams,
+) -> tuple[dict[str, tuple[float, float]], list[str]]:
+    """Tutte embedding for one component. Returns positions + spine order (EN)."""
+    g, layers, names = state.adj, state.layers, state.names
+    edge_pairs = state.links
+    ans = sorted([n for n in comp if layers[n] == "agg"], key=lambda n: names[n])
+    ens = [n for n in comp if layers[n] == "access"]
+    cores = [n for n in comp if layers[n] == "core"]
+    ens_set = set(ens)
+    an_order = order_ans(ans, ens_set, g, names)
+
+    pinned: dict[str, tuple[float, float]] = {}
+    for i, a in enumerate(an_order):
+        pinned[a] = (i * params.an_gap, params.an_y)
+    for i, c in enumerate(sorted(cores, key=lambda n: names[n])):
+        pinned[c] = (
+            (len(an_order) - 1) * params.an_gap / 2 + i * params.an_gap,
+            params.an_y - params.an_gap,
+        )
+
+    pos: dict[str, list[float]] = {
+        n: [float(pinned[n][0]), float(pinned[n][1])] for n in pinned
+    }
+    if ens:
+        dist: dict[str, int] = {}
+        nearest: dict[str, str] = {}
+        q: deque[str] = deque()
+        for a in an_order:
+            for nb in g.get(a, ()):
+                if nb in ens_set and nb not in dist:
+                    dist[nb] = 1
+                    nearest[nb] = a
+                    q.append(nb)
+        while q:
+            u = q.popleft()
+            for v in g.get(u, ()):
+                if v in ens_set and v not in dist:
+                    dist[v] = dist[u] + 1
+                    nearest[v] = nearest[u]
+                    q.append(v)
+        for n in ens:
+            if n not in dist:
+                dist[n] = max(dist.values(), default=1) + 1
+                nearest[n] = an_order[0] if an_order else n
+        buckets: dict[tuple[str, int], list[str]] = defaultdict(list)
+        for n in ens:
+            buckets[(nearest[n], dist[n])].append(n)
+        for (an, d), group in buckets.items():
+            group.sort(key=lambda n: names[n])
+            ax = pinned[an][0] if an in pinned else 0.0
+            for k, n in enumerate(group):
+                pos[n] = [ax + (k - (len(group) - 1) / 2) * 0.35, float(d)]
+
+        for _ in range(80):
+            for n in ens:
+                neigh = [v for v in g.get(n, ()) if v in pos]
+                if not neigh:
+                    continue
+                pos[n][0] = sum(pos[v][0] for v in neigh) / len(neigh)
+                pos[n][1] = sum(pos[v][1] for v in neigh) / len(neigh)
+            for n, xy in pinned.items():
+                pos[n][0], pos[n][1] = float(xy[0]), float(xy[1])
+            for n in ens:
+                if pos[n][1] < params.an_y + 0.4:
+                    pos[n][1] = params.an_y + 0.4
+
+    raw = {n: (pos[n][0], pos[n][1]) for n in pos}
+    x0, y0, x1, y1 = bbox(raw)
+    bw, bh = max(x1 - x0, 1e-6), max(y1 - y0, 1e-6)
+    unit = {n: ((x - x0) / bw, (y - y0) / bh) for n, (x, y) in raw.items()}
+    w = max(params.an_gap * max(len(an_order), 1), 1120.0) * params.width_mul
+    h = max(140.0 * (1 + len(ens) ** 0.5), 840.0) * params.height_mul
+    node_set = set(comp)
+    internal = [(a, b) for a, b in edge_pairs if a in node_set and b in node_set]
+
+    best, best_c = None, 10**9
+    for fx, fy in product([False, True], repeat=2):
+        p = {
+            n: (((1 - x) if fx else x) * w, ((1 - y) if fy else y) * h)
+            for n, (x, y) in unit.items()
+        }
+        if an_order and ens:
+            if sum(p[a][1] for a in an_order) / len(an_order) > sum(
+                p[e][1] for e in ens
+            ) / len(ens):
+                continue
+        c = count_edge_crossings(p, internal)
+        if c < best_c:
+            best_c, best = c, p
+    assert best is not None
+    x0, y0, _, _ = bbox(best)
+    placed = {n: (x - x0, y - y0) for n, (x, y) in best.items()}
+    spine = spine_backbone(ens, g, names) if ens else []
+    return placed, spine
+
+
+def _pack_islands(
+    islands: list[dict[str, tuple[float, float]]],
+    edge_pairs: list[tuple[str, str]],
+    params: LayoutParams,
+) -> dict[str, tuple[float, float]]:
+    islands = sorted(islands, key=lambda p: -len(p))
+    cols = 2 if len(islands) >= 3 else 1
+    n = len(islands)
+
+    def build(flips: list[bool]) -> dict[str, tuple[float, float]]:
+        cells: list[list[dict]] = [[] for _ in range(cols)]
+        for i, isl in enumerate(islands):
+            loc = dict(isl)
+            if flips[i]:
+                x0, _, x1, _ = bbox(loc)
+                mid = (x0 + x1) / 2
+                loc = {nid: (2 * mid - x, y) for nid, (x, y) in loc.items()}
+            cells[i % cols].append(loc)
+        col_w = []
+        for c in range(cols):
+            w = 0.0
+            for loc in cells[c]:
+                x0, y0, x1, y1 = bbox(loc)
+                w = max(w, x1 - x0 + params.island_pad_x)
+            col_w.append(max(w, 1.0))
+        out: dict[str, tuple[float, float]] = {}
+        for c in range(cols):
+            oy = 0.0
+            ox = sum(col_w[:c])
+            for loc in cells[c]:
+                x0, y0, x1, y1 = bbox(loc)
+                pad = (col_w[c] - params.island_pad_x - (x1 - x0)) / 2
+                for nid, (x, y) in loc.items():
+                    out[nid] = (ox + pad + (x - x0), oy + (y - y0))
+                oy += (y1 - y0) + params.island_pad_y
+        return out
+
+    k = min(n, 5)
+    best, best_c = None, 10**9
+    for bits in product([False, True], repeat=k):
+        flips = list(bits) + [False] * (n - k)
+        p = build(flips)
+        c = count_edge_crossings(p, edge_pairs)
+        if c < best_c:
+            best_c, best = c, p
+    assert best is not None
+    return best
+
+
+def build_skeleton(state: LayoutState, params: LayoutParams | None = None) -> OpResult:
+    """Place Tutte corridor skeleton; pin agg+core+spine; leave sides for next op."""
+    params = params or LayoutParams()
+    st = state.copy()
+    ids = list(st.meta.get("ids") or st.names.keys())
+    active = {i for i in ids if st.layers.get(i) in ("core", "agg", "access")}
+    others = sorted(
+        [i for i in ids if st.layers.get(i) == "other"], key=lambda n: st.names[n]
+    )
+    comps = connected_components(active, st.adj)
+    islands: list[dict[str, tuple[float, float]]] = []
+    spine_all: set[str] = set()
+    for c in comps:
+        placed, spine = _place_component_tutte(c, st, params)
+        if placed:
+            islands.append(placed)
+            spine_all.update(spine)
+    pos = _pack_islands(islands, st.links, params) if islands else {}
+
+    if others:
+        if pos:
+            x0, y0, x1, y1 = bbox(pos)
+            fallback_x, fallback_y = x1 + params.island_pad_x * 0.4, y0
+        else:
+            fallback_x, fallback_y = 0.0, 0.0
+        for i, n in enumerate(others):
+            neigh = [v for v in st.adj.get(n, ()) if v in pos]
+            if neigh:
+                mx = sum(pos[v][0] for v in neigh) / len(neigh)
+                my = sum(pos[v][1] for v in neigh) / len(neigh)
+                pos[n] = (
+                    mx + params.pitch * (1.0 + (i % 3) * 0.35),
+                    my + params.side * ((i % 5) - 2) * 0.25,
+                )
+            else:
+                pos[n] = (
+                    fallback_x + (i % 3) * params.pitch,
+                    fallback_y + (i // 3) * params.side,
+                )
+    for n in ids:
+        if n not in pos:
+            pos[n] = (0.0, 0.0)
+
+    st.positions = pos
+    st.spine = spine_all
+    pin = {
+        n
+        for n in pos
+        if st.layers.get(n) in ("agg", "core") or n in spine_all
+    }
+    st.pinned = pin
+    st.last_moved = set(pos.keys())
+    st.meta["components"] = len(comps)
+    return OpResult(
+        state=st,
+        moved=set(pos.keys()),
+        op="build_skeleton",
+        params={
+            "width_mul": params.width_mul,
+            "height_mul": params.height_mul,
+            "an_gap": params.an_gap,
+            "spine_n": len(spine_all),
+            "pinned_n": len(pin),
+        },
+        note="Tutte islands packed; pinned=agg+core+spine",
+    )

netx_api/mcp_remote/topology/layout_ops/soft_petals.py

@@ -0,0 +1,323 @@
+"""Stage-2: per soft-block stub fan + spine; accept only if global crossings drop."""
+
+from __future__ import annotations
+
+import math
+from collections import deque
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings
+from netx_api.mcp_remote.topology.layout_ops.channels import order_stubs_crossing_aware
+from netx_api.mcp_remote.topology.layout_ops.hotspots import fix_overlaps_local
+from netx_api.mcp_remote.topology.layout_ops.partition import partition_soft_blocks, pick_hub_seeds
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _longest_path(
+    comp: set[str], adj: dict[str, set[str]], prefer: str | None = None
+) -> list[str]:
+    if not comp:
+        return []
+    if len(comp) == 1:
+        return [next(iter(comp))]
+
+    def far(src: str) -> tuple[str, list[str]]:
+        prev: dict[str, str | None] = {src: None}
+        q: deque[str] = deque([src])
+        last = src
+        while q:
+            u = q.popleft()
+            last = u
+            for v in adj.get(u, ()):
+                if v in comp and v not in prev:
+                    prev[v] = u
+                    q.append(v)
+        path = [last]
+        while prev[path[-1]] is not None:
+            path.append(prev[path[-1]])  # type: ignore[index]
+        path.reverse()
+        return last, path
+
+    start = prefer if prefer in comp else next(iter(comp))
+    a, _ = far(start)
+    _, path = far(a)
+    return path
+
+
+def soft_petals_greedy(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    hub_id: str | None = None,
+    step: float | None = None,
+    min_block_size: int = 3,
+) -> OpResult:
+    """Fan stubs around each soft-block hub; keep a block only if crossings fall."""
+    params = params or LayoutParams()
+    step_px = float(step if step is not None else max(params.pitch * 0.92, 160.0))
+    pos = dict(state.positions)
+    links = list(state.links)
+    adj = state.adj
+    cross = count_edge_crossings(pos, links)
+
+    blocks = partition_soft_blocks(state, mode="hub_territory")
+    hubs = set(pick_hub_seeds(state))
+    for n, ly in state.layers.items():
+        if ly == "core":
+            hubs.add(n)
+
+    if hub_id:
+        hub_id = str(hub_id).strip()
+        blocks = [b for b in blocks if b.hub_id == hub_id]
+        if not blocks:
+            return OpResult(
+                state=state,
+                moved=set(),
+                op="soft_petals",
+                params={"hub_id": hub_id},
+                note="hub_not_found",
+            )
+
+    def _commit_trial(
+        trial: dict[str, tuple[float, float]],
+        *,
+        hub: str,
+        hx: float,
+        hy: float,
+        best_c: int,
+    ) -> tuple[dict[str, tuple[float, float]] | None, int]:
+        c1 = count_edge_crossings(trial, links)
+        if c1 >= best_c:
+            return None, best_c
+        st_try = state.copy()
+        st_try.positions = trial
+        stf = fix_overlaps_local(st_try, params).state
+        for hid in hubs:
+            if hid != hub and hid in pos:
+                stf.positions[hid] = pos[hid]
+        stf.positions[hub] = (hx, hy)
+        trial2 = {k: (float(v[0]), float(v[1])) for k, v in stf.positions.items()}
+        c2 = count_edge_crossings(trial2, links)
+        if c2 < best_c:
+            return trial2, c2
+        return None, best_c
+
+    accepted: list[dict[str, Any]] = []
+    for b in sorted(blocks, key=lambda x: -len(x.node_ids)):
+        hub = b.hub_id
+        members = [n for n in b.node_ids if n in pos]
+        if not hub or hub not in pos or len(members) < min_block_size:
+            continue
+        pinned = set(hubs)
+        hx, hy = pos[hub]
+        member_set = set(members)
+        stubs = [
+            n
+            for n in adj.get(hub, ())
+            if n in member_set and n not in pinned
+        ]
+        if len(stubs) < 1:
+            continue
+
+        owner: dict[str, str] = {}
+        q: deque[str] = deque()
+        for stub in stubs:
+            owner[stub] = stub
+            q.append(stub)
+        while q:
+            u = q.popleft()
+            for v in adj.get(u, ()):
+                if v not in member_set or v == hub or v in owner or v in pinned:
+                    continue
+                owner[v] = owner[u]
+                q.append(v)
+
+        def ang(s: str) -> float:
+            x, y = pos[s]
+            return math.atan2(y - hy, x - hx)
+
+        best_trial = None
+        best_c = cross
+        kind = None
+
+        # 1) Rigid rotate existing petal around hub (keeps local structure).
+        movable = [n for n in members if n != hub and n not in pinned]
+        for deg in (-90, -60, -45, -30, -15, 15, 30, 45, 60, 90):
+            rad = math.radians(deg)
+            ca, sa = math.cos(rad), math.sin(rad)
+            trial = dict(pos)
+            for n in movable:
+                x, y = pos[n]
+                dx, dy = x - hx, y - hy
+                trial[n] = (hx + dx * ca - dy * sa, hy + dx * sa + dy * ca)
+            trial[hub] = (hx, hy)
+            got, best_c = _commit_trial(trial, hub=hub, hx=hx, hy=hy, best_c=best_c)
+            if got is not None:
+                best_trial = got
+                kind = f"rotate:{deg}"
+
+        # 2) Swap adjacent stub territories by exchanging polar angles of nodes.
+        stubs_geo = sorted(stubs, key=ang)
+        if len(stubs_geo) >= 2:
+            for i in range(len(stubs_geo) - 1):
+                a, bstub = stubs_geo[i], stubs_geo[i + 1]
+                ta = {n for n, o in owner.items() if o == a} | {a}
+                tb = {n for n, o in owner.items() if o == bstub} | {bstub}
+                aa, ab = ang(a), ang(bstub)
+                delta = ab - aa
+                trial = dict(pos)
+                for n in ta:
+                    if n in pinned:
+                        continue
+                    x, y = pos[n]
+                    dx, dy = x - hx, y - hy
+                    r = math.hypot(dx, dy)
+                    th = math.atan2(dy, dx) + delta
+                    trial[n] = (hx + r * math.cos(th), hy + r * math.sin(th))
+                for n in tb:
+                    if n in pinned:
+                        continue
+                    x, y = pos[n]
+                    dx, dy = x - hx, y - hy
+                    r = math.hypot(dx, dy)
+                    th = math.atan2(dy, dx) - delta
+                    trial[n] = (hx + r * math.cos(th), hy + r * math.sin(th))
+                trial[hub] = (hx, hy)
+                got, best_c = _commit_trial(trial, hub=hub, hx=hx, hy=hy, best_c=best_c)
+                if got is not None:
+                    best_trial = got
+                    kind = f"swap:{a[:6]}-{bstub[:6]}"
+
+        # 3) Full re-fan with geo / crossing-aware orders + cyclic rotations.
+        stubs_x = order_stubs_crossing_aware(hub, stubs, adj, member_set, owner)
+        orders: list[list[str]] = []
+        for base in (stubs_geo, stubs_x):
+            for k in range(len(base)):
+                orders.append(base[k:] + base[:k])
+            orders.append(list(reversed(base)))
+
+        seen_ord: set[tuple[str, ...]] = set()
+        for stubs_ord in orders:
+            key = tuple(stubs_ord)
+            if key in seen_ord:
+                continue
+            seen_ord.add(key)
+            angs = [ang(s) for s in stubs_ord]
+            a0, a1 = min(angs) - 0.1, max(angs) + 0.1
+            if a1 - a0 < 0.7:
+                mid = sum(angs) / len(angs)
+                a0, a1 = mid - 1.0, mid + 1.0
+            # Also try a wider fan spanning more of the circle when crowded.
+            spans = [(a0, a1)]
+            if len(stubs_ord) >= 3:
+                mid = 0.5 * (a0 + a1)
+                half = max(0.9, 0.55 * (a1 - a0) + 0.4)
+                spans.append((mid - half, mid + half))
+
+            for sa0, sa1 in spans:
+                trial = dict(pos)
+                for i, stub in enumerate(stubs_ord):
+                    mid = (
+                        0.5 * (sa0 + sa1)
+                        if len(stubs_ord) == 1
+                        else sa0 + (sa1 - sa0) * i / (len(stubs_ord) - 1)
+                    )
+                    ux, uy = math.cos(mid), math.sin(mid)
+                    trial[stub] = (hx + ux * step_px, hy + uy * step_px)
+                    terr = {n for n, o in owner.items() if o == stub}
+                    spine = _longest_path(terr, adj, prefer=stub)
+                    if stub in spine:
+                        si = spine.index(stub)
+                        fwd, bwd = spine[si:], list(reversed(spine[: si + 1]))
+                        spine = fwd if len(fwd) >= len(bwd) else bwd
+                    sx, sy = trial[stub]
+                    for k, n in enumerate(spine):
+                        if n in pinned or n == stub:
+                            continue
+                        trial[n] = (
+                            sx + ux * step_px * max(k, 1),
+                            sy + uy * step_px * max(k, 1),
+                        )
+                    for n in terr:
+                        if n in pinned or n == stub or n in spine:
+                            continue
+                        portals = [v for v in adj.get(n, ()) if v in trial]
+                        if not portals:
+                            continue
+                        px, py = trial[portals[0]]
+                        trial[n] = (px - uy * step_px * 0.7, py + ux * step_px * 0.7)
+
+                trial[hub] = (hx, hy)
+                got, best_c = _commit_trial(trial, hub=hub, hx=hx, hy=hy, best_c=best_c)
+                if got is not None:
+                    best_trial = got
+                    kind = "refan"
+
+        if best_trial is None:
+            continue
+        pos = best_trial
+        cross = best_c
+        accepted.append(
+            {
+                "hub_id": hub,
+                "hub_name": state.names.get(hub, hub),
+                "crossings": cross,
+                "n": len(members),
+                "kind": kind,
+            }
+        )
+
+    if not accepted:
+        return OpResult(
+            state=state,
+            moved=set(),
+            op="soft_petals",
+            params={"accepted_n": 0, "hub_id": hub_id},
+            note="no_block_improved",
+        )
+
+    out = state.copy()
+    out.positions = pos
+    moved = {
+        n
+        for n, p in pos.items()
+        if n in state.positions and p != state.positions[n]
+    }
+    out.meta = dict(out.meta or {})
+    out.meta["soft_petals"] = {
+        "accepted": accepted,
+        "crossings_after": cross,
+    }
+    return OpResult(
+        state=out,
+        moved=moved,
+        op="soft_petals",
+        params={
+            "accepted_n": len(accepted),
+            "accepted": accepted,
+            "hub_id": hub_id,
+            "crossings_after": cross,
+        },
+        note=f"soft_petals:{len(accepted)} blocks",
+    )
+
+
+def soft_petals_params_from_overrides(
+    overrides: dict[str, Any] | None,
+) -> dict[str, Any]:
+    out: dict[str, Any] = {}
+    if not overrides:
+        return out
+    if overrides.get("hub_id"):
+        out["hub_id"] = str(overrides["hub_id"]).strip()
+    if overrides.get("step") is not None:
+        try:
+            out["step"] = float(overrides["step"])
+        except (TypeError, ValueError):
+            pass
+    if overrides.get("min_block_size") is not None:
+        try:
+            out["min_block_size"] = int(overrides["min_block_size"])
+        except (TypeError, ValueError):
+            pass
+    return out

netx_api/mcp_remote/topology/layout_ops/state.py

@@ -0,0 +1,78 @@
+"""Layout state shared by atomic ops."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any
+
+
+@dataclass
+class LayoutParams:
+    """Tunable knobs for recipe / ops (agent edits these, not ad-hoc scripts)."""
+
+    pitch: float = 200.0
+    side: float = 170.0
+    an_gap: float = 420.0
+    an_y: float = 0.0
+    island_pad_x: float = 220.0
+    island_pad_y: float = 200.0
+    width_mul: float = 3.5
+    height_mul: float = 2.0
+    x_gain: float = 1.8
+    lane: float = 260.0
+    target_nn: float = 150.0
+    scale_cap: float = 2.2
+    target_util: float = 0.08
+    min_util: float = 0.03
+    pack_min_scale: float = 0.55
+    pack_iters: int = 4
+    # Cap uniform pack so nn_p50 does not fall below this (keeps icons readable).
+    pack_nn_floor: float = 140.0
+    overlap_iters: int = 160
+    overlap_step: float = 3.0
+    margin: float = 160.0
+    # explode_clusters / enforce_min_gap
+    cluster_thr: float = 8.0
+    cluster_gap: float = 35.0
+    min_center_gap: float = 160.0
+
+
+@dataclass
+class LayoutState:
+    positions: dict[str, tuple[float, float]] = field(default_factory=dict)
+    pinned: set[str] = field(default_factory=set)
+    names: dict[str, str] = field(default_factory=dict)
+    layers: dict[str, str] = field(default_factory=dict)
+    # Optional fabric numeric level (1 / 1.1 / 2 …); used by freeze_levels.
+    levels: dict[str, float] = field(default_factory=dict)
+    links: list[tuple[str, str]] = field(default_factory=list)
+    adj: dict[str, set[str]] = field(default_factory=dict)
+    spine: set[str] = field(default_factory=set)
+    # None = whole graph active; else only these ids may be moved by scoped ops.
+    scope: set[str] | None = None
+    meta: dict[str, Any] = field(default_factory=dict)
+    last_moved: set[str] = field(default_factory=set)
+
+    def copy(self) -> LayoutState:
+        return LayoutState(
+            positions=dict(self.positions),
+            pinned=set(self.pinned),
+            names=dict(self.names),
+            layers=dict(self.layers),
+            levels=dict(self.levels),
+            links=list(self.links),
+            adj={k: set(v) for k, v in self.adj.items()},
+            spine=set(self.spine),
+            scope=None if self.scope is None else set(self.scope),
+            meta=dict(self.meta),
+            last_moved=set(self.last_moved),
+        )
+
+
+@dataclass
+class OpResult:
+    state: LayoutState
+    moved: set[str]
+    op: str
+    params: dict[str, Any] = field(default_factory=dict)
+    note: str = ""

netx_api/mcp_remote/topology/layout_ops/suggest_sink_hubs.py

@@ -0,0 +1,262 @@
+"""Suggest next non-dual sink batches: rank hub territories for move_nodes(park).
+
+After the one-shot dual_unit eye is fixed, remaining access should migrate by
+hub territory (not another dual sink). This module turns structure hubs +
+soft_blocks into ordered batches of fabric_node_ids.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+_LAYER_RANK = {"agg": 0, "core": 1, "access": 2, "external": 3, "other": 4}
+
+
+def _as_id_set(raw: Any) -> set[str]:
+    out: set[str] = set()
+    if raw is None:
+        return out
+    if isinstance(raw, (str, bytes)):
+        s = str(raw).strip()
+        if s:
+            out.add(s)
+        return out
+    if isinstance(raw, (list, tuple, set)):
+        for x in raw:
+            s = str(x or "").strip()
+            if s and not s.startswith("region:"):
+                out.add(s)
+    return out
+
+
+def _portal_ids_from_dual(dual_units: dict[str, Any] | None) -> set[str]:
+    out: set[str] = set()
+    if not isinstance(dual_units, dict):
+        return out
+    for u in dual_units.get("units") or []:
+        if not isinstance(u, dict):
+            continue
+        for k in ("portal_a", "portal_b"):
+            pid = str(u.get(k) or "").strip()
+            if pid:
+                out.add(pid)
+    return out
+
+
+def _hub_index(hubs: list[dict[str, Any]]) -> dict[str, dict[str, Any]]:
+    out: dict[str, dict[str, Any]] = {}
+    for h in hubs or []:
+        if not isinstance(h, dict):
+            continue
+        hid = str(h.get("fabric_node_id") or "").strip()
+        if hid:
+            out[hid] = h
+    return out
+
+
+def _blocks_by_hub(soft_blocks: dict[str, Any] | None) -> dict[str, dict[str, Any]]:
+    out: dict[str, dict[str, Any]] = {}
+    if not isinstance(soft_blocks, dict):
+        return out
+    for b in soft_blocks.get("blocks") or []:
+        if not isinstance(b, dict):
+            continue
+        hid = str(b.get("hub_id") or "").strip()
+        if not hid:
+            continue
+        # Prefer larger territory if duplicate hub rows
+        prev = out.get(hid)
+        n = len([x for x in (b.get("node_ids") or []) if x])
+        if prev is None or n > len([x for x in (prev.get("node_ids") or []) if x]):
+            out[hid] = b
+    return out
+
+
+def suggest_sink_hub_batches(
+    *,
+    hubs: list[dict[str, Any]] | None,
+    soft_blocks: dict[str, Any] | None,
+    source_ids: set[str],
+    sink_ids: set[str] | None = None,
+    exclude_ids: set[str] | None = None,
+    dual_units: dict[str, Any] | None = None,
+    min_territory: int = 1,
+    min_move_n: int = 1,
+    top_n: int = 12,
+    include_hub: bool = True,
+    only_layers: list[str] | None = None,
+) -> dict[str, Any]:
+    """Rank hub territories still on ``source_ids`` and not already on sink.
+
+    Ranking (desc): remaining move count → remaining access stubs → prefer agg
+    over core → structure hub score. Eye portals / ``exclude_ids`` never lead a
+    batch (and are dropped from id lists).
+    """
+    src = {str(x) for x in (source_ids or ()) if str(x) and not str(x).startswith("region:")}
+    on_sink = {str(x) for x in (sink_ids or ()) if str(x)}
+    excluded = set(exclude_ids or ())
+    excluded |= _portal_ids_from_dual(dual_units)
+
+    layer_allow: set[str] | None = None
+    if only_layers:
+        layer_allow = {str(x).strip().lower() for x in only_layers if str(x).strip()}
+
+    by_hub = _hub_index(list(hubs or []))
+    blocks = _blocks_by_hub(soft_blocks)
+
+    # Ensure every structure hub has a block (fallback: hub + stub_ids).
+    for hid, h in by_hub.items():
+        if hid in blocks:
+            continue
+        stubs = [str(x) for x in (h.get("stub_ids") or []) if str(x)]
+        blocks[hid] = {
+            "hub_id": hid,
+            "method": "hub_stubs",
+            "node_ids": [hid, *stubs],
+            "node_count": 1 + len(stubs),
+        }
+
+    batches: list[dict[str, Any]] = []
+    for hid, block in blocks.items():
+        hmeta = by_hub.get(hid) or {}
+        layer = str(hmeta.get("layer") or "other").lower()
+        if layer_allow is not None and layer not in layer_allow:
+            continue
+
+        raw_ids = [str(x) for x in (block.get("node_ids") or []) if str(x)]
+        # Still on source, not yet on sink, not excluded portals
+        move_ids = [
+            nid
+            for nid in raw_ids
+            if nid in src and nid not in on_sink and nid not in excluded
+        ]
+        if not include_hub:
+            move_ids = [nid for nid in move_ids if nid != hid]
+        # Hub may already be on sink — still migrate remaining territory
+        if hid in on_sink and include_hub:
+            move_ids = [nid for nid in move_ids if nid != hid]
+
+        # Dedup preserve order
+        seen: set[str] = set()
+        ordered: list[str] = []
+        # Eye portals / exclude_ids never lead a batch, but leftover stubs under
+        # an already-sunk or portal hub may still migrate.
+        if include_hub and hid in src and hid not in on_sink and hid not in excluded:
+            ordered.append(hid)
+            seen.add(hid)
+        for nid in move_ids:
+            if nid in seen:
+                continue
+            seen.add(nid)
+            ordered.append(nid)
+        # Portal hub with nothing left to move (except itself) → skip
+        if hid in excluded and not ordered:
+            continue
+
+        if len(ordered) < max(1, int(min_move_n)):
+            continue
+
+        # Territory signal: prefer structure territory, else remaining stubs
+        struct_terr = int(hmeta.get("territory") or 0)
+        remaining_n = len(ordered)
+        # Count non-hub members as remaining territory proxy
+        remaining_terr = max(0, remaining_n - (1 if hid in ordered else 0))
+        if remaining_terr < max(0, int(min_territory)):
+            continue
+
+        access_n = int(hmeta.get("access_neighbors") or 0)
+        score = float(hmeta.get("score") or 0.0)
+        batches.append(
+            {
+                "hub_id": hid,
+                "hub_name": str(hmeta.get("name") or hid),
+                "layer": layer,
+                "degree": int(hmeta.get("degree") or 0),
+                "structure_territory": struct_terr,
+                "structure_access_neighbors": access_n,
+                "structure_score": score,
+                "remaining_n": remaining_n,
+                "remaining_territory": remaining_terr,
+                "block_method": str(block.get("method") or ""),
+                "fabric_node_ids": ordered,
+                "already_on_sink": hid in on_sink,
+                "excluded_from_batch": sorted(
+                    nid for nid in raw_ids if nid in excluded or nid in on_sink
+                )[:40],
+            }
+        )
+
+    batches.sort(
+        key=lambda b: (
+            -int(b["remaining_territory"]),
+            -int(b["remaining_n"]),
+            _LAYER_RANK.get(str(b["layer"]), 9),
+            -float(b["structure_score"]),
+            str(b["hub_name"]),
+        )
+    )
+
+    # Orphan leftovers: source ids with no hub territory still need park batches.
+    covered: set[str] = set()
+    for b in batches:
+        covered |= set(b.get("fabric_node_ids") or [])
+    orphans = sorted(
+        nid
+        for nid in src
+        if nid not in on_sink and nid not in excluded and nid not in covered
+    )
+    orphan_batches: list[dict[str, Any]] = []
+    if orphans and len(batches) < max(1, min(40, int(top_n))):
+        # Chunk orphans so park stays stable (~8 per batch).
+        chunk = 8
+        for i in range(0, len(orphans), chunk):
+            ids = orphans[i : i + chunk]
+            orphan_batches.append(
+                {
+                    "hub_id": ids[0],
+                    "hub_name": f"orphan_batch_{i // chunk + 1}",
+                    "layer": "other",
+                    "degree": 0,
+                    "structure_territory": 0,
+                    "structure_access_neighbors": 0,
+                    "structure_score": 0.0,
+                    "remaining_n": len(ids),
+                    "remaining_territory": len(ids),
+                    "block_method": "orphan_leftovers",
+                    "fabric_node_ids": ids,
+                    "already_on_sink": False,
+                    "excluded_from_batch": [],
+                    "orphan": True,
+                }
+            )
+        batches.extend(orphan_batches)
+
+    top = batches[: max(1, min(40, int(top_n)))]
+    for i, b in enumerate(top, start=1):
+        b["rank"] = i
+
+    return {
+        "ok": True,
+        "batch_count": len(top),
+        "excluded_n": len(excluded),
+        "excluded_ids": sorted(excluded)[:40],
+        "source_n": len(src),
+        "sink_n": len(on_sink),
+        "orphan_n": len(orphans),
+        "batches": top,
+        "hint": (
+            "Pick batches[0] (or pick=N) → layoutTopologyView move_nodes "
+            "source→sink with park=true. Do NOT sinkTopologyDualUnits again. "
+            "Eye portals are excluded from leading a batch. "
+            "orphan_batch_* = disconnected leftovers (park in chunks)."
+        ),
+    }
+
+
+def pick_batch(report: dict[str, Any], pick: int = 1) -> dict[str, Any] | None:
+    batches = list(report.get("batches") or [])
+    if not batches:
+        return None
+    idx = max(1, min(len(batches), int(pick or 1))) - 1
+    return dict(batches[idx])

netx_api/mcp_remote/topology/layout_ops/transforms.py

@@ -0,0 +1,844 @@
+"""Atomic transforms: pin select, regional scale, pack util, resolve overlaps."""
+
+from __future__ import annotations
+
+import math
+from collections import defaultdict
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    ICON_SIZE,
+    REC_CENTER_DX,
+    REC_CENTER_DY,
+    node_footprint,
+)
+from netx_api.mcp_remote.topology.layout_ops.graph_util import bbox
+from netx_api.mcp_remote.topology.layout_ops.scope import active_nodes, movable_nodes
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def select_pins(
+    state: LayoutState,
+    *,
+    mode: str = "spine",
+    complement_of_last: bool = False,
+) -> OpResult:
+    """Set pinned set by rule. Does not move positions."""
+    st = state.copy()
+    pos = st.positions
+    if mode == "spine":
+        pin = {
+            n
+            for n in pos
+            if st.layers.get(n) in ("agg", "core") or n in st.spine
+        }
+    elif mode == "agg":
+        pin = {n for n in pos if st.layers.get(n) in ("agg", "core")}
+    elif mode == "high_degree":
+        deg = sorted(pos.keys(), key=lambda n: (-len(st.adj.get(n, ())), st.names.get(n, n)))
+        k = max(3, len(pos) // 10)
+        pin = set(deg[:k])
+    elif mode == "bbox_quartile":
+        if not pos:
+            pin = set()
+        else:
+            x0, y0, x1, y1 = bbox(pos)
+            mid_x = (x0 + x1) / 2
+            # pin left half (already "stable" after lateral stretch)
+            pin = {n for n, (x, _) in pos.items() if x <= mid_x}
+    elif mode == "prev_moved":
+        pin = set(st.last_moved)
+    else:
+        pin = set(st.pinned)
+
+    if complement_of_last and st.last_moved:
+        # pin everyone except last_moved (stabilize what we just placed)
+        pin = set(pos.keys()) - set(st.last_moved)
+
+    st.pinned = pin
+    return OpResult(
+        state=st,
+        moved=set(),
+        op="select_pins",
+        params={"mode": mode, "complement_of_last": complement_of_last, "pinned_n": len(pin)},
+        note=f"pins={len(pin)} mode={mode}",
+    )
+
+
+def scale_region(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    sx: float | None = None,
+    sy: float | None = None,
+    factor: float | None = None,
+    node_ids: set[str] | None = None,
+    only_unpinned: bool = True,
+    anchor: str = "centroid",
+) -> OpResult:
+    """Scale a subset of nodes about an anchor. Pinned stay fixed when only_unpinned."""
+    params = params or LayoutParams()
+    st = state.copy()
+    pos = dict(st.positions)
+    if factor is not None:
+        sx = sy = float(factor)
+    sx = 1.0 if sx is None else float(sx)
+    sy = 1.0 if sy is None else float(sy)
+    if abs(sx - 1.0) < 1e-12 and abs(sy - 1.0) < 1e-12:
+        return OpResult(state=st, moved=set(), op="scale_region", note="noop")
+
+    if node_ids is not None:
+        targets = {n for n in node_ids if n in pos}
+    else:
+        targets = active_nodes(st)
+    if only_unpinned:
+        targets = {n for n in targets if n not in st.pinned}
+        # hard anchors never move
+        targets = {n for n in targets if st.layers.get(n) not in ("agg", "core")}
+    if not targets:
+        return OpResult(state=st, moved=set(), op="scale_region", note="no targets")
+
+    if anchor == "origin":
+        ax, ay = 0.0, 0.0
+    else:
+        ax = sum(pos[n][0] for n in targets) / len(targets)
+        ay = sum(pos[n][1] for n in targets) / len(targets)
+
+    moved: set[str] = set()
+    for n in targets:
+        x, y = pos[n]
+        pos[n] = (ax + (x - ax) * sx, ay + (y - ay) * sy)
+        moved.add(n)
+    st.positions = pos
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="scale_region",
+        params={"sx": sx, "sy": sy, "moved_n": len(moved), "only_unpinned": only_unpinned},
+        note=f"scaled {len(moved)} nodes sx={sx} sy={sy}",
+    )
+
+
+def scale_edge_axes(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """Anisotropic stretch so median |dx|/|dy| of edges approach pitch/side."""
+    params = params or LayoutParams()
+    st = state.copy()
+    pos = st.positions
+    dxs = [
+        abs(pos[a][0] - pos[b][0])
+        for a, b in st.links
+        if a in pos and b in pos and abs(pos[a][0] - pos[b][0]) > 1e-6
+    ]
+    dys = [
+        abs(pos[a][1] - pos[b][1])
+        for a, b in st.links
+        if a in pos and b in pos and abs(pos[a][1] - pos[b][1]) > 1e-6
+    ]
+
+    def _med(vals: list[float]) -> float:
+        vals = sorted(vals)
+        return vals[len(vals) // 2] if vals else 0.0
+
+    med_dx, med_dy = _med(dxs), _med(dys)
+    sx = sy = 1.0
+    if 1e-6 < med_dx < params.pitch:
+        sx = min(params.pitch / med_dx, 1.8)
+    if 1e-6 < med_dy < params.side:
+        sy = min(params.side / med_dy, 1.8)
+    out = scale_region(
+        st,
+        params,
+        sx=sx,
+        sy=sy,
+        node_ids=active_nodes(st),
+        only_unpinned=False,
+        anchor="centroid",
+    )
+    out.op = "scale_edge_axes"
+    out.params = {**out.params, "med_dx": round(med_dx, 2), "med_dy": round(med_dy, 2)}
+    out.note = f"edge-axis stretch sx={sx:.3f} sy={sy:.3f}"
+    return out
+
+
+def compress_long_edges(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    edge_len_cap: float | None = None,
+    pull: float = 0.45,
+    iters: int = 4,
+    frozen: set[str] | None = None,
+) -> OpResult:
+    """Shorten edges longer than ``edge_len_cap`` by walking ends toward mid.
+
+    This is the densify that works when nn is already sweet but bbox is huge
+    from metro bridges (uniform pack cannot raise util without crushing nn).
+    Frozen nodes (e.g. a small portal set) stay put; the other end takes the
+    full pull.
+    """
+    params = params or LayoutParams()
+    st = state.copy()
+    pos = {n: (float(p[0]), float(p[1])) for n, p in st.positions.items()}
+    freeze = set(frozen or ())
+    cap = float(edge_len_cap) if edge_len_cap is not None else max(
+        700.0, float(params.target_nn) * 4.5
+    )
+    pull = max(0.05, min(float(pull), 0.9))
+    iters = max(1, int(iters))
+    moved: set[str] = set()
+    shortened = 0
+
+    def _util() -> float:
+        ids = list(active_nodes(st))
+        if len(ids) < 2:
+            return 0.0
+        sub = {n: pos[n] for n in ids if n in pos}
+        x0, y0, x1, y1 = bbox(sub)
+        return len(ids) * REC_CENTER_DX * REC_CENTER_DY / max((x1 - x0) * (y1 - y0), 1e-6)
+
+    util_before = _util()
+    for _ in range(iters):
+        progress = 0
+        for a, b in list(st.links):
+            if a not in pos or b not in pos:
+                continue
+            ax, ay = pos[a]
+            bx, by = pos[b]
+            dx, dy = bx - ax, by - ay
+            L = math.hypot(dx, dy)
+            if L <= cap + 1e-6:
+                continue
+            mx, my = 0.5 * (ax + bx), 0.5 * (ay + by)
+            # Desired length after this step.
+            target = L - pull * (L - cap)
+            target = max(cap, target)
+            if target >= L - 1e-6:
+                continue
+            half = 0.5 * target
+            ux, uy = dx / L, dy / L
+            new_a = (mx - ux * half, my - uy * half)
+            new_b = (mx + ux * half, my + uy * half)
+            a_fr, b_fr = a in freeze, b in freeze
+            if a_fr and b_fr:
+                continue
+            trial = dict(pos)
+            if a_fr and not b_fr:
+                # Keep a; place b on the ray at distance target.
+                trial[b] = (ax + ux * target, ay + uy * target)
+            elif b_fr and not a_fr:
+                trial[a] = (bx - ux * target, by - uy * target)
+            else:
+                trial[a], trial[b] = new_a, new_b
+            movers = {n for n in (a, b) if trial[n] != pos[n]}
+            # Reject if a mover lands too close to another center (avoid ov).
+            min_gap = max(80.0, float(params.min_center_gap) * 0.55)
+            too_close = False
+            for n in movers:
+                nx, ny = trial[n]
+                for m, (mx2, my2) in trial.items():
+                    if m == n:
+                        continue
+                    if math.hypot(nx - mx2, ny - my2) < min_gap:
+                        too_close = True
+                        break
+                if too_close:
+                    break
+            if too_close:
+                continue
+            for n in movers:
+                pos[n] = trial[n]
+                moved.add(n)
+            shortened += 1
+            progress += 1
+        if progress == 0:
+            break
+
+    st.positions = pos
+    st.last_moved = moved
+    util_after = _util()
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="compress_long_edges",
+        params={
+            "edge_len_cap": round(cap, 1),
+            "pull": round(pull, 3),
+            "iters": iters,
+            "shortened": shortened,
+            "moved_n": len(moved),
+            "util_before": round(util_before, 4),
+            "util_after": round(util_after, 4),
+            "frozen_n": len(freeze),
+        },
+        note=(
+            f"compress_long cap={cap:.0f} shortened={shortened} "
+            f"util {util_before:.4f}→{util_after:.4f}"
+        ),
+    )
+
+
+def pack_toward_portals(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    portal_ids: list[str] | set[str] | None = None,
+    scale: float | None = None,
+) -> OpResult:
+    """Pull non-portal nodes toward nearest portal (shorten long metro spokes).
+
+    Uniform bbox pack cannot raise util when nn is already in the sweet band —
+    empty area lives in long corridors. This compresses leaves toward portals
+    while leaving portal coordinates fixed (semi-rigid dual-unit style).
+    """
+    params = params or LayoutParams()
+    st = state.copy()
+    portals = {str(p) for p in (portal_ids or []) if str(p) and str(p) in st.positions}
+    if len(portals) < 1:
+        return OpResult(
+            state=st, moved=set(), op="pack_toward_portals", note="no portals"
+        )
+    # Mild default: 0.82 keeps local clusters readable; caller may tighten.
+    s = 0.82 if scale is None else float(scale)
+    s = max(0.35, min(s, 0.98))
+    targets = [n for n in active_nodes(st) if n not in portals]
+    if not targets:
+        return OpResult(
+            state=st, moved=set(), op="pack_toward_portals", note="no leaves"
+        )
+    pos = dict(st.positions)
+    moved: set[str] = set()
+    for n in targets:
+        x, y = pos[n]
+        best_p = None
+        best_d = None
+        for p in portals:
+            px, py = pos[p]
+            d = math.hypot(x - px, y - py)
+            if best_d is None or d < best_d:
+                best_d = d
+                best_p = p
+        if best_p is None or best_d is None or best_d < 1e-6:
+            continue
+        px, py = pos[best_p]
+        pos[n] = (px + (x - px) * s, py + (y - py) * s)
+        moved.add(n)
+    st.positions = pos
+    st.last_moved = moved
+    ids = list(active_nodes(st))
+    sub = {n: pos[n] for n in ids}
+    x0, y0, x1, y1 = bbox(sub)
+    util = len(ids) * REC_CENTER_DX * REC_CENTER_DY / max((x1 - x0) * (y1 - y0), 1e-6)
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="pack_toward_portals",
+        params={
+            "scale": round(s, 4),
+            "portal_n": len(portals),
+            "moved_n": len(moved),
+            "util_after": round(util, 4),
+            "nn_p50_after": round(_nn_p50(pos, ids), 2),
+        },
+        note=f"portal-pack s={s:.2f} moved={len(moved)} util→{util:.4f}",
+    )
+
+
+def _nn_p50(pos: dict[str, tuple[float, float]], ids: list[str]) -> float:
+    nns: list[float] = []
+    for a in ids:
+        ax, ay = pos[a]
+        best = min(
+            (math.hypot(ax - pos[b][0], ay - pos[b][1]) for b in ids if b != a),
+            default=None,
+        )
+        if best is not None:
+            nns.append(best)
+    if not nns:
+        return 0.0
+    nns.sort()
+    return float(nns[len(nns) // 2])
+
+
+def pack_utilization(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """Shrink active scope toward centroid until util≈target (multi-iter).
+
+    Each iter clamps scale to [pack_min_scale, 1] and also refuses to push
+    nn_p50 below ``pack_nn_floor`` (default 140) — otherwise a sparse-but-
+    well-spaced canvas gets crushed and fix_overlaps blows crossings.
+    Use with resolve_overlaps afterwards when pack still leaves footprint hits.
+    """
+    params = params or LayoutParams()
+    st = state.copy()
+    targets = list(active_nodes(st))
+    if len(targets) < 2:
+        return OpResult(state=st, moved=set(), op="pack_utilization", note="too few")
+
+    pos = dict(st.positions)
+    moved: set[str] = set()
+    util_before = None
+    scales: list[float] = []
+    iters = max(1, int(params.pack_iters))
+    # Allow aggressive pack when caller sets pack_min_scale < 0.2 (MCP tune).
+    floor = max(0.05, min(float(params.pack_min_scale), 0.95))
+    nn_floor = max(40.0, float(getattr(params, "pack_nn_floor", 140.0) or 140.0))
+    nn_capped = False
+
+    for _ in range(iters):
+        sub = {n: pos[n] for n in targets}
+        x0, y0, x1, y1 = bbox(sub)
+        bw, bh = max(x1 - x0, 1e-6), max(y1 - y0, 1e-6)
+        util = len(targets) * REC_CENTER_DX * REC_CENTER_DY / (bw * bh)
+        if util_before is None:
+            util_before = util
+        if util >= params.target_util:
+            break
+        nn = _nn_p50(pos, targets)
+        if nn <= nn_floor + 1e-6:
+            # Already at/below floor — do not shrink further.
+            nn_capped = True
+            break
+        s = math.sqrt(util / max(params.target_util, 1e-6))
+        # Hard cap: never project nn_p50 below floor (similarity shrink).
+        s_nn = nn_floor / nn
+        if s < s_nn:
+            nn_capped = True
+            s = s_nn
+        s = max(floor, min(s, 1.0))
+        if s >= 0.999:
+            break
+        # Guard: refuse a step that would still undershoot the floor.
+        if nn * s < nn_floor - 1e-3:
+            nn_capped = True
+            break
+        cx = (x0 + x1) / 2
+        cy = (y0 + y1) / 2
+        snap = {n: pos[n] for n in targets}
+        for n in targets:
+            x, y = pos[n]
+            pos[n] = (cx + (x - cx) * s, cy + (y - cy) * s)
+            moved.add(n)
+        nn_step = _nn_p50(pos, targets)
+        if nn_step + 1e-6 < nn_floor:
+            # Revert step — nn metric can drop faster than uniform scale predicts.
+            for n, p in snap.items():
+                pos[n] = p
+            nn_capped = True
+            break
+        scales.append(round(s, 4))
+        if nn_capped:
+            break
+
+    st.positions = pos
+    st.last_moved = moved
+    sub = {n: pos[n] for n in targets}
+    x0, y0, x1, y1 = bbox(sub)
+    util_after = len(targets) * REC_CENTER_DX * REC_CENTER_DY / max((x1 - x0) * (y1 - y0), 1e-6)
+    nn_after = _nn_p50(pos, targets)
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="pack_utilization",
+        params={
+            "scales": scales,
+            "util_before": round(float(util_before or 0.0), 4),
+            "util_after": round(util_after, 4),
+            "target_util": params.target_util,
+            "nn_p50_after": round(nn_after, 2),
+            "pack_nn_floor": nn_floor,
+            "nn_capped": nn_capped,
+            "scope_n": len(targets),
+        },
+        note=(
+            (
+                f"pack iters={len(scales)} util {util_before:.4f}→{util_after:.4f}"
+                + (" (nn floor)" if nn_capped else "")
+            )
+            if scales
+            else (
+                f"nn floor blocks pack (nn≈{nn_after:.1f})"
+                if nn_capped
+                else f"util already ok ({util_before:.4f})"
+            )
+        ),
+    )
+
+
+def _footprints(
+    pos: dict[str, tuple[float, float]], names: dict[str, str]
+) -> dict[str, tuple[float, float, float, float]]:
+    out = {}
+    for n, (x, y) in pos.items():
+        fx0, fy0, fx1, fy1 = node_footprint(names.get(n, n))
+        out[n] = (x + fx0, y + fy0, x + fx1, y + fy1)
+    return out
+
+
+def resolve_overlaps(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    mode: str = "lateral",
+) -> OpResult:
+    """Push movable nodes to clear footprint overlaps.
+
+    mode=lateral (default): project pushes onto ±X (corridor sides) to preserve
+    Tutte chain crossings. mode=free: isotropic push (can raise crossings a lot).
+    Hard-pinned = agg/core.
+    """
+    params = params or LayoutParams()
+    st = state.copy()
+    pos = {n: [float(p[0]), float(p[1])] for n, p in st.positions.items()}
+    ids = list(pos)
+    # Move only scoped non-agg/core; outsiders are frozen obstacles.
+    can_move = movable_nodes(st, respect_pins=False)
+    # Spine = accepted pure-chain units; keep them atomic during unstick.
+    hard = {n for n in ids if st.layers.get(n) in ("agg", "core")} | set(st.spine)
+    pinned_soft = set(st.pinned) - hard
+    moved: set[str] = set()
+
+    for _ in range(params.overlap_iters):
+        fps = _footprints({n: (pos[n][0], pos[n][1]) for n in ids}, st.names)
+        cell = max(REC_CENTER_DX, 80.0)
+        buckets: dict[tuple[int, int], list[str]] = defaultdict(list)
+        for n in ids:
+            ax0, ay0, ax1, ay1 = fps[n]
+            cx = int(((ax0 + ax1) / 2) // cell)
+            cy = int(((ay0 + ay1) / 2) // cell)
+            buckets[(cx, cy)].append(n)
+
+        hits = 0
+        for n in ids:
+            if n not in can_move:
+                continue
+            # Prefer moving non-spine; spine only if still overlapping
+            ax0, ay0, ax1, ay1 = fps[n]
+            cx = int(((ax0 + ax1) / 2) // cell)
+            cy = int(((ay0 + ay1) / 2) // cell)
+            fx = fy = 0.0
+            local_hits = 0
+            for dx in (-1, 0, 1):
+                for dy in (-1, 0, 1):
+                    for m in buckets[(cx + dx, cy + dy)]:
+                        if m == n:
+                            continue
+                        bx0, by0, bx1, by1 = fps[m]
+                        if not (ax0 < bx1 and ax1 > bx0 and ay0 < by1 and ay1 > by0):
+                            continue
+                        local_hits += 1
+                        mx = (ax0 + ax1) / 2 - (bx0 + bx1) / 2
+                        my = (ay0 + ay1) / 2 - (by0 + by1) / 2
+                        if abs(mx) + abs(my) < 1e-9:
+                            mx = 1.0 if (hash(n) & 1) else -1.0
+                            my = 0.0
+                        ox = min(ax1, bx1) - max(ax0, bx0)
+                        oy = min(ay1, by1) - max(ay0, by0)
+                        push = 0.5 * max(ox, oy, ICON_SIZE)
+                        if m in hard:
+                            push *= 2.0
+                        L = math.hypot(mx, my) or 1.0
+                        ux, uy = mx / L, my / L
+                        if mode == "lateral":
+                            # keep chain Y; separate in X (+ small Y for label)
+                            fx += math.copysign(push, ux if abs(ux) > 1e-9 else 1.0)
+                            fy += 0.25 * push if uy >= 0 else -0.15 * push
+                        else:
+                            fx += ux * push
+                            fy += uy * push
+            if local_hits == 0:
+                continue
+            if n in pinned_soft and mode == "lateral" and local_hits < 2:
+                # spine lightly held unless multi-overlap
+                continue
+            hits += local_hits
+            L = math.hypot(fx, fy)
+            if L > params.overlap_step:
+                fx *= params.overlap_step / L
+                fy *= params.overlap_step / L
+            if L > 1e-9:
+                pos[n][0] += fx
+                pos[n][1] += fy
+                moved.add(n)
+        if hits == 0:
+            break
+
+    st.positions = {n: (pos[n][0], pos[n][1]) for n in ids}
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="resolve_overlaps",
+        params={"iters": params.overlap_iters, "moved_n": len(moved), "mode": mode},
+        note=f"unstick mode={mode} moved={len(moved)} scope={len(can_move)}",
+    )
+
+
+def explode_clusters(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    thr: float | None = None,
+    gap: float | None = None,
+    axis: str = "along",
+) -> OpResult:
+    """Spread near-coincident clusters (generic atom).
+
+    Union nodes with center dist ≤ thr; lay cluster on a short line.
+    axis=along: parallel to average neighbor edge (usually fewer new crossings).
+    axis=perp: side-lane / normal direction.
+    Only moves nodes in current scope (non agg/core).
+    """
+    params = params or LayoutParams()
+    thr = params.cluster_thr if thr is None else float(thr)
+    gap = params.cluster_gap if gap is None else float(gap)
+    st = state.copy()
+    pos = dict(st.positions)
+    ids = list(pos)
+    if len(ids) < 2:
+        return OpResult(state=st, moved=set(), op="explode_clusters", note="too few")
+
+    can_move = movable_nodes(st, respect_pins=False)
+    hard = {n for n in ids if st.layers.get(n) in ("agg", "core")}
+    parent = {n: n for n in ids}
+
+    def find(x: str) -> str:
+        while parent[x] != x:
+            parent[x] = parent[parent[x]]
+            x = parent[x]
+        return x
+
+    def uni(a: str, b: str) -> None:
+        ra, rb = find(a), find(b)
+        if ra != rb:
+            parent[rb] = ra
+
+    cell = max(thr, 1.0)
+    buckets: dict[tuple[int, int], list[str]] = defaultdict(list)
+    for n, (x, y) in pos.items():
+        buckets[(int(x // cell), int(y // cell))].append(n)
+    for n, (x, y) in pos.items():
+        cx, cy = int(x // cell), int(y // cell)
+        for dx in (-1, 0, 1):
+            for dy in (-1, 0, 1):
+                for m in buckets[(cx + dx, cy + dy)]:
+                    if m <= n:
+                        continue
+                    if math.hypot(x - pos[m][0], y - pos[m][1]) <= thr:
+                        uni(n, m)
+
+    groups: dict[str, list[str]] = defaultdict(list)
+    for n in ids:
+        groups[find(n)].append(n)
+
+    moved: set[str] = set()
+    for grp in groups.values():
+        if len(grp) < 2:
+            continue
+        grp = sorted(grp, key=lambda n: st.names.get(n, n))
+        vx = vy = 0.0
+        for n in grp:
+            for m in st.adj.get(n, ()):
+                if m not in pos:
+                    continue
+                dx = pos[m][0] - pos[n][0]
+                dy = pos[m][1] - pos[n][1]
+                if abs(dx) + abs(dy) < 1e-9:
+                    continue
+                if dx < 0:
+                    dx, dy = -dx, -dy
+                vx += dx
+                vy += dy
+        L = math.hypot(vx, vy)
+        if L < 1e-9:
+            ax, ay = 1.0, 0.0
+        else:
+            ax, ay = vx / L, vy / L
+        if axis == "perp":
+            tx, ty = -ay, ax
+        else:
+            tx, ty = ax, ay
+        cx = sum(pos[n][0] for n in grp) / len(grp)
+        cy = sum(pos[n][1] for n in grp) / len(grp)
+        # Only explode clusters that touch the active scope; only move can_move.
+        movable = [n for n in grp if n in can_move]
+        if not movable:
+            continue
+        for i, n in enumerate(movable):
+            off = (i - (len(movable) - 1) / 2) * gap
+            pos[n] = (cx + tx * off, cy + ty * off)
+            moved.add(n)
+
+    st.positions = pos
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="explode_clusters",
+        params={
+            "thr": thr,
+            "gap": gap,
+            "axis": axis,
+            "moved_n": len(moved),
+            "scope_n": len(can_move),
+        },
+        note=f"exploded axis={axis} moved={len(moved)} scope={len(can_move)}",
+    )
+
+
+def enforce_min_gap(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    min_gap: float | None = None,
+    iters: int | None = None,
+) -> OpResult:
+    """Push centers apart until euclidean nearest-neighbor ≥ min_gap (unpinned)."""
+    params = params or LayoutParams()
+    min_gap = params.min_center_gap if min_gap is None else float(min_gap)
+    iters = params.overlap_iters if iters is None else int(iters)
+    st = state.copy()
+    pos = {n: [float(p[0]), float(p[1])] for n, p in st.positions.items()}
+    ids = list(pos)
+    can_move = movable_nodes(st, respect_pins=False)
+    hard = {n for n in ids if st.layers.get(n) in ("agg", "core")}
+    moved: set[str] = set()
+    cell = max(min_gap, 1.0)
+    step = params.overlap_step
+
+    for _ in range(iters):
+        buckets: dict[tuple[int, int], list[str]] = defaultdict(list)
+        for n in ids:
+            buckets[(int(pos[n][0] // cell), int(pos[n][1] // cell))].append(n)
+        hits = 0
+        for n in ids:
+            if n not in can_move:
+                continue
+            cx, cy = int(pos[n][0] // cell), int(pos[n][1] // cell)
+            fx = fy = 0.0
+            for dx in (-1, 0, 1):
+                for dy in (-1, 0, 1):
+                    for m in buckets[(cx + dx, cy + dy)]:
+                        if m == n:
+                            continue
+                        vx = pos[n][0] - pos[m][0]
+                        vy = pos[n][1] - pos[m][1]
+                        d = math.hypot(vx, vy)
+                        if d >= min_gap:
+                            continue
+                        hits += 1
+                        if d < 1e-6:
+                            ang = (hash(n) ^ hash(m)) % 360
+                            rad = math.radians(float(ang))
+                            vx, vy = math.cos(rad), math.sin(rad)
+                            d = 1e-6
+                        else:
+                            vx /= d
+                            vy /= d
+                        push = (min_gap - d) * (1.0 if m in hard else 0.5)
+                        fx += vx * push
+                        fy += vy * push
+            L = math.hypot(fx, fy)
+            if L > step:
+                fx *= step / L
+                fy *= step / L
+            if L > 1e-9:
+                pos[n][0] += fx
+                pos[n][1] += fy
+                moved.add(n)
+        if hits == 0:
+            break
+
+    st.positions = {n: (pos[n][0], pos[n][1]) for n in ids}
+    st.last_moved = moved
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="enforce_min_gap",
+        params={"min_gap": min_gap, "moved_n": len(moved)},
+        note=f"min_gap={min_gap} moved={len(moved)}",
+    )
+
+
+def soft_nn_scale(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """Capped isotropic scale by nn_p50 toward target_nn (active scope)."""
+    params = params or LayoutParams()
+    st = state.copy()
+    ids = list(active_nodes(st))
+    pos = st.positions
+    if len(ids) < 2:
+        return OpResult(state=st, moved=set(), op="soft_nn_scale", note="too few")
+    nns = []
+    for a in ids:
+        ax, ay = pos[a]
+        best = min(
+            (math.hypot(ax - pos[b][0], ay - pos[b][1]) for b in ids if b != a),
+            default=None,
+        )
+        if best is not None:
+            nns.append(best)
+    nns.sort()
+    nn = nns[len(nns) // 2] if nns else 0.0
+    if nn <= 1e-9 or nn >= params.target_nn:
+        return OpResult(
+            state=st,
+            moved=set(),
+            op="soft_nn_scale",
+            params={"nn_p50": round(nn, 2), "scope_n": len(ids)},
+            note="no scale",
+        )
+    s = min(params.target_nn / nn, params.scale_cap)
+    out = scale_region(
+        st, params, factor=s, node_ids=set(ids), only_unpinned=False, anchor="centroid"
+    )
+    out.op = "soft_nn_scale"
+    out.params = {
+        **out.params,
+        "nn_p50_before": round(nn, 2),
+        "scale": round(s, 3),
+        "scope_n": len(ids),
+    }
+    out.note = f"nn soft scale s={s:.3f} scope={len(ids)}"
+    return out
+
+
+def normalize_origin(
+    state: LayoutState, params: LayoutParams | None = None
+) -> OpResult:
+    """Translate so min corner is at margin."""
+    params = params or LayoutParams()
+    st = state.copy()
+    if not st.positions:
+        return OpResult(state=st, moved=set(), op="normalize_origin", note="empty")
+    x0, y0, _, _ = bbox(st.positions)
+    m = params.margin
+    pos = {
+        n: (x - x0 + m, y - y0 + m) for n, (x, y) in st.positions.items()
+    }
+    st.positions = pos
+    st.last_moved = set(pos.keys())
+    return OpResult(
+        state=st,
+        moved=st.last_moved,
+        op="normalize_origin",
+        params={"margin": m},
+        note="origin+margin",
+    )
+
+
+OPS: dict[str, Any] = {
+    "select_pins": select_pins,
+    "scale_region": scale_region,
+    "scale_edge_axes": scale_edge_axes,
+    "pack_utilization": pack_utilization,
+    "resolve_overlaps": resolve_overlaps,
+    "explode_clusters": explode_clusters,
+    "enforce_min_gap": enforce_min_gap,
+    "soft_nn_scale": soft_nn_scale,
+    "normalize_origin": normalize_origin,
+}

netx_api/mcp_remote/topology/layout_ops/untangle.py

@@ -0,0 +1,395 @@
+"""Greedy local untangle: move low-degree nodes to cut crossings (keep ov=0)."""
+
+from __future__ import annotations
+
+import math
+import random
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    count_edge_crossings,
+    crossing_participation,
+    crossings_involving_node,
+    node_footprint,
+    top_crossing_nodes,
+)
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutParams, LayoutState, OpResult
+
+
+def _box(nid: str, pos: dict[str, tuple[float, float]], names: dict[str, str]):
+    x, y = pos[nid]
+    minx, miny, maxx, maxy = node_footprint(names.get(nid, ""))
+    return (x + minx, y + miny, x + maxx, y + maxy)
+
+
+def _node_overlaps_any(
+    node: str, pos: dict[str, tuple[float, float]], names: dict[str, str]
+) -> bool:
+    ax0, ay0, ax1, ay1 = _box(node, pos, names)
+    for b, (x, y) in pos.items():
+        if b == node:
+            continue
+        if abs(x - pos[node][0]) > 80 and abs(y - pos[node][1]) > 60:
+            continue
+        bx0, by0, bx1, by1 = _box(b, pos, names)
+        if ax0 < bx1 and ax1 > bx0 and ay0 < by1 and ay1 > by0:
+            return True
+    return False
+
+
+def _participation(
+    pos: dict[str, tuple[float, float]], links: list[tuple[str, str]]
+) -> dict[str, int]:
+    _n, hit = crossing_participation(pos, links)
+    return hit
+
+
+# Max jump from current position — prevents radial *scale* candidates from
+# flinging nodes across a giant star and exploding util/bbox.
+# Default raised: BTM-scale stars need ~900px jumps to escape petal local minima.
+_MAX_JUMP = 900.0
+_MAX_FROM_NBS = 1100.0
+# Sparse metro compose can have spokes of several thousand px; a hard 1200
+# cap makes untangle a no-op on the worst bridges.
+_MAX_JUMP_CAP = 4800.0
+
+
+def _candidates(
+    pos: dict[str, tuple[float, float]],
+    node: str,
+    adj: dict[str, set[str]],
+    rng: random.Random,
+    *,
+    max_jump: float = _MAX_JUMP,
+    angle_step: int = 15,
+    random_n: int = 12,
+) -> list[tuple[float, float]]:
+    x, y = pos[node]
+    jump = max(200.0, min(float(max_jump), _MAX_JUMP_CAP))
+    nbs_cap = max(_MAX_FROM_NBS, jump * 1.25)
+    step = max(10, int(angle_step))
+    cands: list[tuple[float, float]] = []
+    radii = [80, 120, 180, 260, 360, 480]
+    if jump > 520:
+        radii = radii + [640, 800]
+    if jump > 1200:
+        radii = radii + [1200, 1600, 2200, min(jump, 3200)]
+    if step >= 25:
+        radii = [r for r in radii if r >= 120]
+    for ang in range(0, 360, step):
+        for r in radii:
+            if r > jump + 1:
+                continue
+            rad = math.radians(ang)
+            cands.append((x + r * math.cos(rad), y + r * math.sin(rad)))
+    nbs = [pos[v] for v in adj.get(node, ()) if v in pos]
+    if nbs:
+        cx = sum(p[0] for p in nbs) / len(nbs)
+        cy = sum(p[1] for p in nbs) / len(nbs)
+        dx, dy = x - cx, y - cy
+        L = math.hypot(dx, dy) or 1.0
+        # Unit steps along/away from neighbor centroid — NOT unbounded *s* of L.
+        ux, uy = dx / L, dy / L
+        for s in (-360.0, -220.0, -120.0, 120.0, 220.0, 360.0):
+            if abs(s) <= jump:
+                cands.append((x + ux * s, y + uy * s))
+                cands.append((cx + ux * abs(s), cy + uy * abs(s)))
+        px, py = -uy, ux
+        for s in (-240, -160, -80, 80, 160, 240):
+            if abs(s) <= jump:
+                cands.append((x + px * s, y + py * s))
+    span = min(350.0, jump * 0.7)
+    for _ in range(max(0, int(random_n))):
+        cands.append((x + rng.uniform(-span, span), y + rng.uniform(-span, span)))
+    # Drop absurd jumps (legacy radial scale could send nodes 10k+ px away).
+    out: list[tuple[float, float]] = []
+    for cx_, cy_ in cands:
+        if math.hypot(cx_ - x, cy_ - y) > jump:
+            continue
+        if nbs:
+            mx = sum(p[0] for p in nbs) / len(nbs)
+            my = sum(p[1] for p in nbs) / len(nbs)
+            if math.hypot(cx_ - mx, cy_ - my) > nbs_cap:
+                continue
+        out.append((cx_, cy_))
+    return out
+
+
+def untangle_crossings(
+    state: LayoutState,
+    params: LayoutParams | None = None,
+    *,
+    max_rounds: int = 200,
+    max_degree: int = 7,
+    target_crossings: int = 60,
+    seed: int = 7,
+    protect_rings: bool = False,
+    protect_rigid: bool | str = "portals",
+    moves_per_round: int = 3,
+    max_jump: float | None = None,
+    frozen_ids: set[str] | None = None,
+    focus_ids: list[str] | None = None,
+    rank_cap: int | None = None,
+    angle_step: int | None = None,
+    refresh_every: int | None = None,
+) -> OpResult:
+    """Move low-degree nodes greedily to reduce global edge crossings.
+
+    ``protect_rigid`` (default ``portals``): freeze shared dual-unit pivots
+    only — corridors/tails may still move. Use ``all`` to freeze every
+    compose-group member, or ``false`` to freeze nothing.
+    """
+    params = params or LayoutParams()
+    st = state.copy()
+    pos = dict(st.positions)
+    names = dict(st.names)
+    links = list(st.links)
+    adj = {n: set(st.adj.get(n, ())) for n in pos}
+    rng = random.Random(seed)
+    jump = float(max_jump if max_jump is not None else _MAX_JUMP)
+    jump = max(200.0, min(jump, _MAX_JUMP_CAP))
+    n_links = len(links)
+    # Auto-throttle on large E so MCP stdio stays interactive.
+    if rank_cap is None:
+        rank_cap = 36 if n_links >= 400 else (60 if n_links >= 200 else 140)
+    if angle_step is None:
+        angle_step = 30 if n_links >= 400 else (20 if n_links >= 200 else 15)
+    if refresh_every is None:
+        refresh_every = 3 if n_links >= 200 else 1
+    rank_cap = max(8, int(rank_cap))
+    angle_step = max(10, int(angle_step))
+    refresh_every = max(1, int(refresh_every))
+    random_n = 4 if n_links >= 400 else (8 if n_links >= 200 else 12)
+
+    cur_c = count_edge_crossings(pos, links)
+    start_c = cur_c
+    moved: set[str] = set()
+    frozen: set[str] = set(frozen_ids or ())
+    if not frozen and protect_rigid not in (False, "false", "off", "none", "0"):
+        from netx_api.mcp_remote.topology.layout_ops.rigid_units import frozen_ids_for_protect
+
+        frozen = frozen_ids_for_protect(st, protect_rigid)
+    faces = None
+    pierce0 = 0
+    if protect_rings:
+        from netx_api.mcp_remote.topology.layout_ops.ring_faces import (
+            count_ring_pierces,
+            extract_ring_faces,
+        )
+
+        faces = extract_ring_faces(st)
+        pierce0 = int(count_ring_pierces(pos, links, faces).get("pierce_crossings") or 0)
+
+    focus: set[str] = {str(x) for x in (focus_ids or []) if str(x)}
+    hit: dict[str, int] = {}
+    from netx_api.mcp_remote.topology.layout_jobs import raise_if_cancelled, report_progress
+
+    rounds_total = max(1, int(max_rounds))
+    for _round in range(rounds_total):
+        if _round % max(1, refresh_every) == 0:
+            raise_if_cancelled()
+            pct = 70.0 + 4.0 * (_round / rounds_total)
+            report_progress(
+                "untangle",
+                pct=min(74.0, pct),
+                message=f"round {_round + 1}/{rounds_total} x={cur_c}",
+                step=_round + 1,
+                total_steps=rounds_total,
+                crossings=cur_c,
+            )
+        if _round % refresh_every == 0 or not hit:
+            hit = _participation(pos, links)
+        if not hit:
+            break
+        # Prefer analyze.top_crossing_nodes / explicit focus_ids first.
+        if not focus:
+            focus = {
+                str(r["fabric_node_id"])
+                for r in top_crossing_nodes(
+                    pos, links, names=names, adj=adj, top_n=5, participation=hit
+                )
+            }
+        # High-degree hubs in top_nodes cannot move under max_degree — pull in
+        # their low-degree neighbors that still participate in crossings.
+        movable_focus: set[str] = set()
+        for nid in focus:
+            if len(adj.get(nid, ())) < max_degree and nid in hit:
+                movable_focus.add(nid)
+            for nb in adj.get(nid, ()):
+                if nb in hit and len(adj.get(nb, ())) < max_degree:
+                    movable_focus.add(nb)
+        prefer = movable_focus or focus
+        ranked = sorted(
+            hit.keys(),
+            key=lambda n: (
+                0 if n in prefer else 1,
+                -hit[n] / max(len(adj.get(n, ())), 1),
+                len(adj.get(n, ())),
+                -hit[n],
+            ),
+        )
+        improved = False
+        moves_left = max(1, int(moves_per_round))
+        for node in ranked[:rank_cap]:
+            if moves_left <= 0:
+                break
+            if node in frozen:
+                continue
+            deg = len(adj.get(node, ()))
+            if deg >= max_degree:
+                continue
+            before = crossings_involving_node(node, pos, links, adj)
+            if before <= 0:
+                continue
+            best: tuple[int, tuple[float, float]] | None = None
+            x0, y0 = pos[node]
+            for cand in _candidates(
+                pos,
+                node,
+                adj,
+                rng,
+                max_jump=jump,
+                angle_step=angle_step,
+                random_n=random_n,
+            ):
+                if math.hypot(cand[0] - x0, cand[1] - y0) > jump:
+                    continue
+                trial = dict(pos)
+                trial[node] = cand
+                if _node_overlaps_any(node, trial, names):
+                    continue
+                after = crossings_involving_node(node, trial, links, adj)
+                if after >= before:
+                    continue
+                g2 = cur_c - before + after
+                if g2 >= cur_c:
+                    continue
+                if faces is not None:
+                    from netx_api.mcp_remote.topology.layout_ops.ring_faces import count_ring_pierces
+
+                    pierce1 = int(
+                        count_ring_pierces(trial, links, faces).get("pierce_crossings")
+                        or 0
+                    )
+                    if pierce1 > pierce0:
+                        continue
+                if best is None or g2 < best[0]:
+                    best = (g2, cand)
+            if best is None:
+                continue
+            after_local = best[0] - cur_c + before
+            pos[node] = best[1]
+            moved.add(node)
+            cur_c = best[0]
+            # Stale hit is ok between refreshes; keep local estimate for ranking.
+            if after_local > 0:
+                hit[node] = after_local
+            else:
+                hit.pop(node, None)
+            if faces is not None:
+                from netx_api.mcp_remote.topology.layout_ops.ring_faces import count_ring_pierces
+
+                pierce0 = int(
+                    count_ring_pierces(pos, links, faces).get("pierce_crossings") or 0
+                )
+            improved = True
+            moves_left -= 1
+        if not improved:
+            break
+        if cur_c <= target_crossings:
+            break
+
+    protect_mode = (
+        protect_rigid
+        if isinstance(protect_rigid, str)
+        else ("portals" if protect_rigid else "off")
+    )
+    st.positions = pos
+    # Reuse last hit for meta (avoid an extra O(E²) pass on large graphs).
+    if not hit:
+        hit = _participation(pos, links)
+    st.meta["untangle"] = {
+        "start_crossings": start_c,
+        "end_crossings": cur_c,
+        "moved_n": len(moved),
+        "max_degree": max_degree,
+        "target_crossings": target_crossings,
+        "max_jump": jump,
+        "protect_rings": bool(protect_rings),
+        "protect_rigid": protect_mode,
+        "frozen_n": len(frozen),
+        "moves_per_round": int(moves_per_round),
+        "focus_n": len(focus),
+        "rank_cap": rank_cap,
+        "angle_step": angle_step,
+        "refresh_every": refresh_every,
+        "top_crossing_nodes": top_crossing_nodes(
+            pos, links, names=names, adj=adj, top_n=5, participation=hit
+        ),
+    }
+    return OpResult(
+        state=st,
+        moved=moved,
+        op="untangle_crossings",
+        params={
+            "max_rounds": max_rounds,
+            "max_degree": max_degree,
+            "target_crossings": target_crossings,
+            "seed": seed,
+            "protect_rings": bool(protect_rings),
+            "protect_rigid": protect_mode,
+            "frozen_n": len(frozen),
+            "moves_per_round": int(moves_per_round),
+            "max_jump": jump,
+            "focus_n": len(focus),
+        },
+        note=f"untangle {start_c}->{cur_c} moved={len(moved)} frozen={len(frozen)}",
+    )
+
+
+def untangle_params_from_overrides(overrides: dict[str, Any] | None) -> dict[str, Any]:
+    """Pull untangle knobs from layout params overrides (optional)."""
+    o = overrides or {}
+    out: dict[str, Any] = {}
+    for key, cast, default in (
+        ("max_rounds", int, 200),
+        ("max_degree", int, 7),
+        ("target_crossings", int, 60),
+        ("seed", int, 7),
+        ("moves_per_round", int, 3),
+    ):
+        if key not in o or o[key] is None:
+            out[key] = default
+            continue
+        try:
+            out[key] = cast(o[key])
+        except (TypeError, ValueError):
+            out[key] = default
+    if "protect_rings" in o:
+        out["protect_rings"] = bool(o["protect_rings"])
+    if "protect_rigid" in o:
+        v = o["protect_rigid"]
+        if isinstance(v, bool):
+            # true → portals (semi-rigid); false → off
+            out["protect_rigid"] = "portals" if v else "off"
+        else:
+            key = str(v).strip().lower()
+            if key in {"0", "false", "no", "off", "none"}:
+                out["protect_rigid"] = "off"
+            elif key in {"1", "true", "yes", "on", "portals", "skeleton"}:
+                out["protect_rigid"] = "portals"
+            elif key in {"all", "full", "rigid"}:
+                out["protect_rigid"] = "all"
+            else:
+                out["protect_rigid"] = key
+    if o.get("max_jump") is not None:
+        try:
+            out["max_jump"] = float(o["max_jump"])
+        except (TypeError, ValueError):
+            pass
+    focus = o.get("focus_ids") or o.get("focus_node_ids")
+    if isinstance(focus, list):
+        out["focus_ids"] = [str(x).strip() for x in focus if str(x).strip()]
+    elif isinstance(focus, str) and focus.strip():
+        out["focus_ids"] = [s.strip() for s in focus.split(",") if s.strip()]
+    return out

netx_api/mcp_remote/topology/layout_sight.py

@@ -0,0 +1,286 @@
+"""Layout "sight" for agents: crossing hotspots + spatial blocks with coords.
+
+Gives enough geometry to hand-drag like a human without loading the full canvas
+into chat. Used by analyzeTopologyViewLayout(detail=hotspots|blocks).
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    collapse_links,
+    segments_properly_intersect,
+    top_crossing_edges,
+    top_crossing_nodes,
+)
+
+
+def _short_name(name: str) -> str:
+    parts = (name or "").split("-")
+    return parts[1] if len(parts) >= 2 else (name or "")[:16]
+
+
+def _pos_map(nodes: list[dict[str, Any]]) -> dict[str, tuple[float, float, str]]:
+    out: dict[str, tuple[float, float, str]] = {}
+    for n in nodes:
+        if not isinstance(n, dict):
+            continue
+        nid = str(n.get("fabric_node_id") or "").strip()
+        if not nid:
+            continue
+        try:
+            x, y = float(n.get("x")), float(n.get("y"))
+        except (TypeError, ValueError):
+            continue
+        out[nid] = (x, y, str(n.get("name") or nid))
+    return out
+
+
+def list_crossings(
+    nodes: list[dict[str, Any]],
+    edges: list[dict[str, Any]],
+    *,
+    limit: int = 40,
+) -> dict[str, Any]:
+    pos = _pos_map(nodes)
+    links = [(a, b) for a, b in collapse_links(edges) if a in pos and b in pos]
+    crosses: list[dict[str, Any]] = []
+    hit: dict[str, int] = {}
+    edge_hit: dict[tuple[str, str], int] = {}
+    total_cross = 0
+    for i, (a, b) in enumerate(links):
+        p1, p2 = (pos[a][0], pos[a][1]), (pos[b][0], pos[b][1])
+        e1 = (a, b) if a < b else (b, a)
+        for c, d in links[i + 1 :]:
+            if len({a, b, c, d}) < 4:
+                continue
+            p3, p4 = (pos[c][0], pos[c][1]), (pos[d][0], pos[d][1])
+            if not segments_properly_intersect(p1, p2, p3, p4):
+                continue
+            total_cross += 1
+            e2 = (c, d) if c < d else (d, c)
+            for nid in (a, b, c, d):
+                hit[nid] = hit.get(nid, 0) + 1
+            edge_hit[e1] = edge_hit.get(e1, 0) + 1
+            edge_hit[e2] = edge_hit.get(e2, 0) + 1
+            if len(crosses) < limit:
+                crosses.append(
+                    {
+                        "e1": {
+                            "a": a,
+                            "b": b,
+                            "a_name": _short_name(pos[a][2]),
+                            "b_name": _short_name(pos[b][2]),
+                        },
+                        "e2": {
+                            "a": c,
+                            "b": d,
+                            "a_name": _short_name(pos[c][2]),
+                            "b_name": _short_name(pos[d][2]),
+                        },
+                    }
+                )
+    # Rank drag candidates: high crossing participation / low degree.
+    adj: dict[str, set[str]] = {n: set() for n in pos}
+    for a, b in links:
+        adj[a].add(b)
+        adj[b].add(a)
+
+    from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import orbit_lite_suggest
+
+    pos_xy = {k: (v[0], v[1]) for k, v in pos.items()}
+    names = {k: v[2] for k, v in pos.items()}
+    candidates = []
+    for nid, cnt in sorted(hit.items(), key=lambda kv: -kv[1]):
+        deg = len(adj.get(nid, ()))
+        if deg >= 8:
+            continue
+        x, y, name = pos[nid]
+        suggest = orbit_lite_suggest(
+            nid,
+            pos_xy,
+            links,
+            adj,
+            names,
+            max_jump=360.0,
+            angle_step=45,
+            top_k=3,
+        )
+        best_delta = suggest[0]["delta_crossings_est"] if suggest else 0
+        candidates.append(
+            {
+                "fabric_node_id": nid,
+                "name": name,
+                "short": _short_name(name),
+                "x": round(x, 1),
+                "y": round(y, 1),
+                "degree": deg,
+                "in_crossings": cnt,
+                "score": round(cnt / max(deg, 1), 3),
+                "suggest_xy": suggest[:3],
+                "delta_crossings_est": best_delta,
+            }
+        )
+        if len(candidates) >= min(30, limit):
+            break
+    top5 = top_crossing_nodes(
+        pos_xy, links, names=names, adj=adj, top_n=5, participation=hit
+    )
+    top_e = top_crossing_edges(
+        pos_xy, links, names=names, top_n=5, edge_participation=edge_hit
+    )
+    # Fallback: when no drag_candidates (all hot nodes are high-degree hubs),
+    # suggest from edge_clearance.top and crossing top_nodes regardless of degree.
+    if not candidates:
+        from netx_api.mcp_remote.topology.layout_metrics import compute_edge_clearance
+
+        ec = compute_edge_clearance(pos_xy, links, names=names, top_n=5)
+        for eh in (ec.get("top_edge_hits") or [])[:5]:
+            nid = str(eh.get("fabric_node_id") or "")
+            if not nid or nid not in pos:
+                continue
+            x, y, name = pos[nid]
+            deg = len(adj.get(nid, ()))
+            candidates.append({
+                "fabric_node_id": nid,
+                "name": name,
+                "short": _short_name(name),
+                "x": round(x, 1),
+                "y": round(y, 1),
+                "degree": deg,
+                "in_crossings": hit.get(nid, 0),
+                "score": 0.0,
+                "suggest_xy": [],
+                "delta_crossings_est": 0,
+                "reason": "edge_clearance_hit",
+            })
+        for tn in top5:
+            nid = str(tn.get("fabric_node_id") or "")
+            if not nid or nid not in pos:
+                continue
+            if any(c["fabric_node_id"] == nid for c in candidates):
+                continue
+            x, y, name = pos[nid]
+            deg = len(adj.get(nid, ()))
+            candidates.append({
+                "fabric_node_id": nid,
+                "name": name,
+                "short": _short_name(name),
+                "x": round(x, 1),
+                "y": round(y, 1),
+                "degree": deg,
+                "in_crossings": tn.get("crossing_hits", 0),
+                "score": round(tn.get("crossing_hits", 0) / max(deg, 1), 3),
+                "suggest_xy": [],
+                "delta_crossings_est": 0,
+                "reason": "top_crossing_high_degree",
+            })
+    return {
+        "edge_crossings": total_cross,
+        "crossings_listed": len(crosses),
+        "crossings": crosses,
+        "top_nodes": top5,
+        "top_edges": top_e,
+        "drag_candidates": candidates,
+        "tip": (
+            "先看 top_nodes / top_edges（交叉最重前5网元与前5边）；"
+            "再动 drag_candidates（低度数可挪点，suggest_xy=orbit_lite）；"
+            "完整扫角用 layoutTopologyView action=orbit_sweep(preview,node_id)→pick。"
+            "勿本地穷举坐标。"
+        ),
+    }
+
+
+def spatial_blocks(
+    nodes: list[dict[str, Any]],
+    edges: list[dict[str, Any]],
+    *,
+    cell: float = 600.0,
+    top_k: int = 12,
+) -> dict[str, Any]:
+    """Bucket nodes into coarse grid cells; report densest / most-crossing blocks."""
+    pos = _pos_map(nodes)
+    if not pos or cell <= 1:
+        return {"blocks": [], "cell": cell}
+    links = [(a, b) for a, b in collapse_links(edges) if a in pos and b in pos]
+
+    def cell_key(x: float, y: float) -> tuple[int, int]:
+        return (int(math.floor(x / cell)), int(math.floor(y / cell)))
+
+    buckets: dict[tuple[int, int], list[str]] = {}
+    for nid, (x, y, _) in pos.items():
+        buckets.setdefault(cell_key(x, y), []).append(nid)
+
+    # Count crossings whose both midpoints fall in a cell (or either endpoint).
+    cell_cross: dict[tuple[int, int], int] = {k: 0 for k in buckets}
+    for i, (a, b) in enumerate(links):
+        p1 = (pos[a][0], pos[a][1])
+        p2 = (pos[b][0], pos[b][1])
+        for c, d in links[i + 1 :]:
+            if len({a, b, c, d}) < 4:
+                continue
+            p3 = (pos[c][0], pos[c][1])
+            p4 = (pos[d][0], pos[d][1])
+            if not segments_properly_intersect(p1, p2, p3, p4):
+                continue
+            mx = (p1[0] + p2[0] + p3[0] + p4[0]) / 4
+            my = (p1[1] + p2[1] + p3[1] + p4[1]) / 4
+            ck = cell_key(mx, my)
+            cell_cross[ck] = cell_cross.get(ck, 0) + 1
+
+    blocks = []
+    for (cx, cy), ids in buckets.items():
+        xs = [pos[i][0] for i in ids]
+        ys = [pos[i][1] for i in ids]
+        sample = sorted(ids, key=lambda i: pos[i][2])[:8]
+        blocks.append(
+            {
+                "cell": [cx, cy],
+                "bbox": [
+                    round(min(xs), 1),
+                    round(min(ys), 1),
+                    round(max(xs), 1),
+                    round(max(ys), 1),
+                ],
+                "node_count": len(ids),
+                "crossings_near": cell_cross.get((cx, cy), 0),
+                "sample_nodes": [
+                    {
+                        "fabric_node_id": i,
+                        "short": _short_name(pos[i][2]),
+                        "x": round(pos[i][0], 1),
+                        "y": round(pos[i][1], 1),
+                    }
+                    for i in sample
+                ],
+            }
+        )
+    blocks.sort(key=lambda b: (-int(b["crossings_near"]), -int(b["node_count"])))
+    return {
+        "cell": cell,
+        "blocks": blocks[:top_k],
+        "tip": (
+            "分块手拖：选 crossings_near 高的 block，"
+            "getTopologyView / queryTopologyNeighborhood 看邻接，"
+            "再 updateTopologyViewPositions 挪该块内的点。"
+        ),
+    }
+
+
+def build_sight(
+    nodes: list[dict[str, Any]],
+    edges: list[dict[str, Any]],
+    *,
+    mode: str = "hotspots",
+    limit: int = 40,
+    cell: float = 600.0,
+) -> dict[str, Any]:
+    mode = (mode or "hotspots").strip().lower()
+    out: dict[str, Any] = {"mode": mode}
+    if mode in {"hotspots", "both", "all"}:
+        out["hotspots"] = list_crossings(nodes, edges, limit=limit)
+    if mode in {"blocks", "both", "all"}:
+        out["blocks"] = spatial_blocks(nodes, edges, cell=cell, top_k=min(16, max(6, limit // 3)))
+    return out

netx_api/mcp_remote/topology/layout_stats.py

@@ -0,0 +1,819 @@
+"""Topology layout statistics + composite score for parameter search.
+
+Complements layout_metrics.analyze_positions with density / emptiness /
+edge-stretch stats and a single comparable score in [0, 100].
+"""
+
+from __future__ import annotations
+
+import math
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import (
+    REC_CENTER_DX,
+    REC_CENTER_DY,
+    analyze_positions,
+    grade_layout,
+    node_footprint,
+)
+
+# Sweet-spot nearest-neighbor band (px): readable but not empty.
+NN_SWEET_LO = 140.0
+NN_SWEET_HI = 220.0
+# Ideal space_utilization band (n * rec_tile / bbox_area).
+# Floor aligned with real mid-size IPRAN / hand golden (~0.08–0.10), not 0.12 fantasy.
+UTIL_SWEET_LO = 0.08
+UTIL_SWEET_HI = 0.45
+# Ideal median undirected edge length / recommended pitch.
+EDGE_SWEET_LO = 0.7
+EDGE_SWEET_HI = 2.2
+
+# Score profiles: weights must sum to 1.0.
+# default = metro/flat canvases; eye = dual-portal arc-band sinks (diagonals expected).
+_SCORE_WEIGHTS: dict[str, dict[str, float]] = {
+    "default": {
+        "overlap": 0.24,
+        "crossing": 0.17,
+        "utilization": 0.13,
+        "chain": 0.10,
+        "rings": 0.06,
+        "edge_clearance": 0.10,
+        "edge_axis": 0.06,
+        "grid": 0.05,
+        "nn": 0.04,
+        "hull": 0.03,
+        "stretch": 0.02,
+    },
+    "eye": {
+        "overlap": 0.24,
+        "crossing": 0.16,
+        "utilization": 0.14,
+        "chain": 0.10,
+        "rings": 0.05,
+        "edge_clearance": 0.12,
+        "edge_axis": 0.03,  # arc-band eyes intentionally diagonal
+        "grid": 0.05,
+        "nn": 0.04,
+        "hull": 0.05,
+        "stretch": 0.02,
+    },
+}
+
+
+def resolve_score_profile(
+    requested: str | None,
+    *,
+    node_count: int = 0,
+    space_utilization: float | None = None,
+    axis_frac: float | None = None,
+) -> str:
+    """Map request → default|eye. ``auto`` uses size/sparsity/axis heuristic."""
+    raw = str(requested or "auto").strip().lower() or "auto"
+    if raw in {"default", "metro", "flat", "corridor"}:
+        return "default"
+    if raw in {"eye", "dual", "dual_unit", "access_eye"}:
+        return "eye"
+    util = float(space_utilization) if space_utilization is not None else 1.0
+    axis = float(axis_frac) if axis_frac is not None else 1.0
+    # Large, still-sparse, diagonal-heavy → treat as dual-eye sink for scoring.
+    if int(node_count) >= 80 and util < 0.10 and axis < 0.55:
+        return "eye"
+    return "default"
+
+
+def _convex_hull(points: list[tuple[float, float]]) -> list[tuple[float, float]]:
+    """Andrew monotone chain; returns hull CCW, or [] if < 3 unique points."""
+    pts = sorted(set(points))
+    if len(pts) <= 2:
+        return pts
+
+    def cross(o: tuple[float, float], a: tuple[float, float], b: tuple[float, float]) -> float:
+        return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0])
+
+    lower: list[tuple[float, float]] = []
+    for p in pts:
+        while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0:
+            lower.pop()
+        lower.append(p)
+    upper: list[tuple[float, float]] = []
+    for p in reversed(pts):
+        while len(upper) >= 2 and cross(upper[-2], upper[-1], p) <= 0:
+            upper.pop()
+        upper.append(p)
+    return lower[:-1] + upper[:-1]
+
+
+def _polygon_area(poly: list[tuple[float, float]]) -> float:
+    if len(poly) < 3:
+        return 0.0
+    a = 0.0
+    for i in range(len(poly)):
+        x0, y0 = poly[i]
+        x1, y1 = poly[(i + 1) % len(poly)]
+        a += x0 * y1 - x1 * y0
+    return abs(a) * 0.5
+
+
+def _pct(sorted_vals: list[float], p: float) -> float | None:
+    if not sorted_vals:
+        return None
+    idx = min(len(sorted_vals) - 1, max(0, int(round((len(sorted_vals) - 1) * p))))
+    return round(sorted_vals[idx], 2)
+
+
+def _band_score(x: float, lo: float, hi: float, *, hard_lo: float = 0.0, hard_hi: float | None = None) -> float:
+    """1.0 inside [lo,hi], linear falloff outside toward hard bounds → 0."""
+    if lo <= x <= hi:
+        return 1.0
+    if x < lo:
+        floor = hard_lo
+        if x <= floor:
+            return 0.0
+        return max(0.0, (x - floor) / max(lo - floor, 1e-9))
+    ceiling = hard_hi if hard_hi is not None else hi * 3.0
+    if x >= ceiling:
+        return 0.0
+    return max(0.0, (ceiling - x) / max(ceiling - hi, 1e-9))
+
+
+def compute_density_stats(
+    pos: dict[str, tuple[float, float]],
+    names: dict[str, str],
+    links: list[tuple[str, str]],
+) -> dict[str, Any]:
+    """Emptiness / compactness / edge-stretch stats (no crossing count)."""
+    n = len(pos)
+    if n == 0:
+        return {
+            "bbox_area": 0.0,
+            "hull_area": 0.0,
+            "space_utilization": 0.0,
+            "hull_utilization": 0.0,
+            "footprint_fill": 0.0,
+            "grid_occupancy": 0.0,
+            "empty_grid_ratio": 1.0,
+            "aspect_ratio": 1.0,
+            "edge_len_p50": None,
+            "edge_len_p90": None,
+            "edge_stretch_p50": None,
+            "whitespace_index": 1.0,
+        }
+
+    xs = [p[0] for p in pos.values()]
+    ys = [p[1] for p in pos.values()]
+    bw = max(xs) - min(xs)
+    bh = max(ys) - min(ys)
+    bbox_area = max(bw * bh, 1.0)
+    tile = REC_CENTER_DX * REC_CENTER_DY
+    space_utilization = n * tile / bbox_area
+
+    hull = _convex_hull(list(pos.values()))
+    hull_area = max(_polygon_area(hull), 1.0) if len(hull) >= 3 else bbox_area
+    hull_utilization = n * tile / hull_area
+
+    # Sum of icon+label AABBs / bbox (can exceed 1 if overlaps)
+    fp_area = 0.0
+    for nid, (x, y) in pos.items():
+        fx0, fy0, fx1, fy1 = node_footprint(names.get(nid, nid))
+        fp_area += max(fx1 - fx0, 1.0) * max(fy1 - fy0, 1.0)
+    footprint_fill = fp_area / bbox_area
+
+    # Grid occupancy: cells of size pitch×side covering bbox
+    cell_w, cell_h = REC_CENTER_DX, REC_CENTER_DY
+    x0, y0 = min(xs), min(ys)
+    cols = max(1, int(math.ceil(bw / cell_w)) + 1)
+    rows = max(1, int(math.ceil(bh / cell_h)) + 1)
+    occupied: set[tuple[int, int]] = set()
+    for x, y in pos.values():
+        occupied.add((int((x - x0) // cell_w), int((y - y0) // cell_h)))
+    total_cells = cols * rows
+    grid_occupancy = len(occupied) / total_cells
+    empty_grid_ratio = 1.0 - grid_occupancy
+
+    aspect = (bw / bh) if bh > 1e-9 else 999.0
+
+    pitch = math.hypot(REC_CENTER_DX, REC_CENTER_DY) / math.sqrt(2.0)  # ~185
+    edge_lens: list[float] = []
+    for a, b in links:
+        if a not in pos or b not in pos:
+            continue
+        edge_lens.append(math.hypot(pos[a][0] - pos[b][0], pos[a][1] - pos[b][1]))
+    edge_lens.sort()
+    el_p50 = _pct(edge_lens, 0.5)
+    el_p90 = _pct(edge_lens, 0.9)
+    stretch_p50 = round(el_p50 / pitch, 3) if el_p50 else None
+
+    # whitespace_index: 0 compact, 1 desolate (based on util + empty grid)
+    whitespace_index = round(
+        0.55 * (1.0 - min(space_utilization / UTIL_SWEET_LO, 1.0))
+        + 0.45 * empty_grid_ratio,
+        4,
+    )
+
+    return {
+        "bbox_area": round(bbox_area, 1),
+        "hull_area": round(hull_area, 1),
+        "space_utilization": round(space_utilization, 4),
+        "hull_utilization": round(hull_utilization, 4),
+        "footprint_fill": round(footprint_fill, 4),
+        "grid_occupancy": round(grid_occupancy, 4),
+        "empty_grid_ratio": round(empty_grid_ratio, 4),
+        "aspect_ratio": round(aspect, 3),
+        "edge_len_p50": el_p50,
+        "edge_len_p90": el_p90,
+        "edge_stretch_p50": stretch_p50,
+        "whitespace_index": whitespace_index,
+        "grid_cells": [cols, rows],
+        "occupied_cells": len(occupied),
+    }
+
+
+def score_layout_components(
+    metrics: dict[str, Any],
+    *,
+    score_profile: str | None = None,
+) -> dict[str, Any]:
+    """Weighted sub-scores in [0,1] + total in [0,100].
+
+    Hard gate: any footprint/label overlap → total capped near 0 (still report parts).
+    Profiles: ``default`` (metro) | ``eye`` (dual-portal arc sink) | ``auto``.
+    """
+    n = int(metrics.get("node_count") or 0)
+    overlaps = int(metrics.get("footprint_overlap_pairs") or 0)
+    label_ov = int(metrics.get("label_overlap_pairs") or 0)
+    cpl = float(metrics.get("crossings_per_link") or 0.0)
+    nn = float(metrics.get("nn_p50") or 0.0)
+    util = float(metrics.get("space_utilization") or 0.0)
+    hull_u = float(metrics.get("hull_utilization") or util)
+    grid_occ = float(metrics.get("grid_occupancy") or 0.0)
+    stretch = metrics.get("edge_stretch_p50")
+    stretch_f = float(stretch) if stretch is not None else 1.0
+    white = float(metrics.get("whitespace_index") or 0.0)
+    chain_s = float(metrics.get("chain_score") if metrics.get("chain_score") is not None else 1.0)
+    rings_s = float(metrics.get("rings_score") if metrics.get("rings_score") is not None else 1.0)
+    edge_clr_s = float(
+        metrics.get("edge_clearance_score")
+        if metrics.get("edge_clearance_score") is not None
+        else 1.0
+    )
+    edge_axis_s = float(
+        metrics.get("edge_axis_score")
+        if metrics.get("edge_axis_score") is not None
+        else 1.0
+    )
+    axis_frac = metrics.get("axis_frac")
+    try:
+        axis_frac_f = float(axis_frac) if axis_frac is not None else None
+    except (TypeError, ValueError):
+        axis_frac_f = None
+
+    profile = resolve_score_profile(
+        score_profile if score_profile is not None else metrics.get("score_profile"),
+        node_count=n,
+        space_utilization=util,
+        axis_frac=axis_frac_f,
+    )
+    weights = dict(_SCORE_WEIGHTS.get(profile) or _SCORE_WEIGHTS["default"])
+
+    # Crossing: UME mid-size ~0.16; 0 → 1.0, 0.30 → 0
+    if n <= 50:
+        cpl_ok, cpl_bad = 0.05, 0.20
+    elif n <= 200:
+        cpl_ok, cpl_bad = 0.10, 0.25
+    else:
+        cpl_ok, cpl_bad = 0.16, 0.30
+    if cpl <= cpl_ok:
+        cross_s = 1.0
+    elif cpl >= cpl_bad:
+        cross_s = 0.0
+    else:
+        cross_s = 1.0 - (cpl - cpl_ok) / max(cpl_bad - cpl_ok, 1e-9)
+
+    overlap_s = 1.0 if overlaps == 0 and label_ov == 0 else 0.0
+    nn_s = _band_score(nn, NN_SWEET_LO, NN_SWEET_HI, hard_lo=40.0, hard_hi=500.0)
+    # Eye sinks: blend bbox util with hull util so long corridors don't auto-fail.
+    if profile == "eye":
+        util_blend = 0.55 * util + 0.45 * hull_u
+        util_s = _band_score(util_blend, UTIL_SWEET_LO, UTIL_SWEET_HI, hard_lo=0.02, hard_hi=1.2)
+        hull_s = _band_score(hull_u, UTIL_SWEET_LO, UTIL_SWEET_HI + 0.1, hard_lo=0.03, hard_hi=1.5)
+    else:
+        util_s = _band_score(util, UTIL_SWEET_LO, UTIL_SWEET_HI, hard_lo=0.02, hard_hi=1.2)
+        hull_s = _band_score(hull_u, UTIL_SWEET_LO, UTIL_SWEET_HI + 0.1, hard_lo=0.03, hard_hi=1.5)
+    grid_s = _band_score(grid_occ, 0.20, 0.75, hard_lo=0.02, hard_hi=1.0)
+    stretch_s = _band_score(stretch_f, EDGE_SWEET_LO, EDGE_SWEET_HI, hard_lo=0.2, hard_hi=8.0)
+    white_s = max(0.0, 1.0 - white)  # less whitespace → better (diagnostic only)
+    chain_s = max(0.0, min(1.0, chain_s))
+    rings_s = max(0.0, min(1.0, rings_s))
+    edge_clr_s = max(0.0, min(1.0, edge_clr_s))
+    edge_axis_s = max(0.0, min(1.0, edge_axis_s))
+
+    parts = {
+        "overlap": round(overlap_s, 4),
+        "crossing": round(cross_s, 4),
+        "utilization": round(util_s, 4),
+        "chain": round(chain_s, 4),
+        "rings": round(rings_s, 4),
+        "edge_clearance": round(edge_clr_s, 4),
+        "edge_axis": round(edge_axis_s, 4),
+        "grid": round(grid_s, 4),
+        "nn": round(nn_s, 4),
+        "hull": round(hull_s, 4),
+        "stretch": round(stretch_s, 4),
+        # Diagnostic only — not in weights (avoid double-count with util/grid).
+        "compactness": round(white_s, 4),
+    }
+    total = sum(parts[k] * weights[k] for k in weights)
+    if overlap_s < 1.0:
+        total *= 0.15  # hard gate: overlaps wreck the score
+    total_100 = round(100.0 * total, 2)
+
+    return {
+        "total": total_100,
+        "parts": parts,
+        "weights": weights,
+        "score_profile": profile,
+        "targets": {
+            "nn_sweet": [NN_SWEET_LO, NN_SWEET_HI],
+            "util_sweet": [UTIL_SWEET_LO, UTIL_SWEET_HI],
+            "edge_stretch_sweet": [EDGE_SWEET_LO, EDGE_SWEET_HI],
+            "cpl_ok": cpl_ok,
+            "cpl_bad": cpl_bad,
+            "chain_ok": 0.75,
+            "chain_warn": 0.45,
+            "rings_ok": 0.75,
+            "rings_warn": 0.45,
+            "edge_clearance_ok": 0.85,
+            "edge_clearance_warn": 0.45,
+            "edge_axis_ok": 0.75,
+            "edge_axis_warn": 0.45,
+        },
+        "rank_key": [
+            0 if overlap_s >= 1.0 else 1,
+            -total_100,
+            int(metrics.get("edge_crossings") or 0),
+            -util,
+            -edge_clr_s,
+            -chain_s,
+            -rings_s,
+            -edge_axis_s,
+        ],
+        "hint": (
+            f"total∈[0,100] profile={profile}. Overlaps hard-gate. "
+            "Clearance blends nodes_hit + hits/link. "
+            "Eye profile softens axis (arc bands) and rings; raises util/clearance. "
+            "compactness is diagnostic only (not weighted)."
+        ),
+    }
+
+
+def _status_from_score(part: float, *, fail_below: float = 0.01, warn_below: float = 0.55) -> str:
+    if part <= fail_below:
+        return "fail"
+    if part < warn_below:
+        return "warn"
+    return "ok"
+
+
+def _sparsity_status(util: float, white: float, grid_occ: float) -> str:
+    # Aligned with UTIL_SWEET_LO=0.08: below sweet → warn; desolate → fail.
+    if util < 0.04 or white > 0.85 or grid_occ < 0.04:
+        return "fail"
+    if util < UTIL_SWEET_LO or white > 0.65 or grid_occ < 0.12:
+        return "warn"
+    return "ok"
+
+
+def _headline(verdict_overall: str, dims: dict[str, Any]) -> str:
+    bad = [k for k, v in dims.items() if v.get("status") == "fail"]
+    warn = [k for k, v in dims.items() if v.get("status") == "warn"]
+    labels = {
+        "overlap": "重叠",
+        "crossing": "交叉",
+        "spacing": "间距",
+        "sparsity": "稀疏/空旷",
+        "edges": "边长",
+        "chains": "直链不成一体",
+        "rings": "最小环被穿",
+        "edge_clearance": "网元贴边",
+        "edge_axis": "斜边过多",
+    }
+    if bad:
+        return "问题：" + "、".join(labels.get(k, k) for k in bad)
+    if warn:
+        return "可改进：" + "、".join(labels.get(k, k) for k in warn)
+    if verdict_overall == "ok":
+        return "布图验收通过"
+    return f"overall={verdict_overall}"
+
+
+def build_layout_report(metrics: dict[str, Any]) -> dict[str, Any]:
+    """One structured report for the single analyze tool (agent-facing)."""
+    score = metrics.get("score") or score_layout_components(metrics)
+    grade = metrics.get("grade") or grade_layout(metrics)
+    parts = score.get("parts") or {}
+    targets = score.get("targets") or {}
+
+    fp = int(metrics.get("footprint_overlap_pairs") or 0)
+    lbl = int(metrics.get("label_overlap_pairs") or 0)
+    util = float(metrics.get("space_utilization") or 0.0)
+    white = float(metrics.get("whitespace_index") or 0.0)
+    grid_occ = float(metrics.get("grid_occupancy") or 0.0)
+    nn_p50 = metrics.get("nn_p50")
+    stretch = metrics.get("edge_stretch_p50")
+
+    overlap = {
+        "status": "ok" if fp == 0 and lbl == 0 else "fail",
+        "score": parts.get("overlap"),
+        "footprint_pairs": fp,
+        "label_pairs": lbl,
+        "hard_zero": True,
+        "tip": "图标+名称 AABB 不得互挡；有重叠则总分硬门控。",
+    }
+    top_x = metrics.get("top_crossing_nodes") or []
+    top_e = metrics.get("top_crossing_edges") or []
+    crossing = {
+        "status": grade.get("crossing_grade") or _status_from_score(float(parts.get("crossing") or 0)),
+        "score": parts.get("crossing"),
+        "edge_crossings": metrics.get("edge_crossings"),
+        "crossings_per_link": metrics.get("crossings_per_link"),
+        "crossings_per_node": metrics.get("crossings_per_node"),
+        "top_nodes": top_x,
+        "top_edges": top_e,
+        "budget": {
+            "cpl_ok": targets.get("cpl_ok"),
+            "cpl_bad": targets.get("cpl_bad"),
+            "cross_warn": (grade.get("budgets") or {}).get("cross_warn"),
+            "cross_fail": (grade.get("budgets") or {}).get("cross_fail"),
+        },
+        "tip": (
+            "无向 NE↔NE 真交叉（共端点不算）；优先看 crossings_per_link。"
+            " top_nodes=交叉最重前5网元；top_edges=交叉点最多的前5条边；"
+            "阶段2优先处理它们的端点/邻边。"
+        ),
+    }
+    spacing = {
+        "status": (
+            "fail"
+            if nn_p50 is not None and float(nn_p50) < 80
+            else "warn"
+            if nn_p50 is not None and float(nn_p50) < 150
+            else "ok"
+            if nn_p50 is not None
+            else "warn"
+        ),
+        "score": parts.get("nn"),
+        "nn_min": metrics.get("nn_min"),
+        "nn_p10": metrics.get("nn_p10"),
+        "nn_p50": nn_p50,
+        "pairs_closer_than_min_dist": metrics.get("pairs_closer_than_min_dist"),
+        "sweet": targets.get("nn_sweet") or [NN_SWEET_LO, NN_SWEET_HI],
+        "tip": "中位最近邻宜在 140–220；过小挤、过大浪费。",
+    }
+    sparsity = {
+        "status": _sparsity_status(util, white, grid_occ),
+        "score": parts.get("utilization"),
+        "space_utilization": util,
+        "hull_utilization": metrics.get("hull_utilization"),
+        "grid_occupancy": grid_occ,
+        "empty_grid_ratio": metrics.get("empty_grid_ratio"),
+        "whitespace_index": white,
+        "footprint_fill": metrics.get("footprint_fill"),
+        "aspect_ratio": metrics.get("aspect_ratio"),
+        "sweet_util": targets.get("util_sweet") or [UTIL_SWEET_LO, UTIL_SWEET_HI],
+        "tip": (
+            "util=推荐瓦片×n/bbox；grid_occupancy=有点格子占比；"
+            "whitespace_index→1 表示太空旷。目标 util≈0.12–0.45。"
+        ),
+    }
+    edges = {
+        "status": _status_from_score(float(parts.get("stretch") or 0), fail_below=0.15, warn_below=0.5),
+        "score": parts.get("stretch"),
+        "edge_len_p50": metrics.get("edge_len_p50"),
+        "edge_len_p90": metrics.get("edge_len_p90"),
+        "edge_stretch_p50": stretch,
+        "sweet": targets.get("edge_stretch_sweet") or [EDGE_SWEET_LO, EDGE_SWEET_HI],
+        "tip": "中位边长/推荐步长；过大=走廊被拉爆，过小=叠在一起。",
+    }
+
+    chain_ok = float(targets.get("chain_ok") or 0.75)
+    chain_warn = float(targets.get("chain_warn") or 0.45)
+    rings_ok = float(targets.get("rings_ok") or 0.75)
+    rings_warn = float(targets.get("rings_warn") or 0.45)
+    chain_part = float(parts.get("chain") if parts.get("chain") is not None else 1.0)
+    rings_part = float(parts.get("rings") if parts.get("rings") is not None else 1.0)
+
+    def _mid_status(part: float, ok: float, warn: float) -> str:
+        if part >= ok:
+            return "ok"
+        if part >= warn:
+            return "warn"
+        return "fail"
+
+    chains = {
+        "status": _mid_status(chain_part, chain_ok, chain_warn),
+        "score": parts.get("chain"),
+        "chain_count": metrics.get("chain_count"),
+        "chain_nodes": metrics.get("chain_nodes"),
+        "straightness_p50": metrics.get("chain_straightness_p50"),
+        "straightness_mean": metrics.get("chain_straightness_mean"),
+        "kink_count": metrics.get("chain_kink_count"),
+        "kink_frac": metrics.get("chain_kink_frac"),
+        "budget": {"ok": chain_ok, "warn": chain_warn},
+        "tip": metrics.get("chain_tip")
+        or "deg≤2 走廊应近似共线成一体；折角少、chord/path≈1。",
+    }
+    rings = {
+        "status": _mid_status(rings_part, rings_ok, rings_warn),
+        "score": parts.get("rings"),
+        "ring_count": metrics.get("ring_count"),
+        "rings_pierced": metrics.get("rings_pierced"),
+        "pierce_crossings": metrics.get("ring_pierce_crossings"),
+        "budget": {"ok": rings_ok, "warn": rings_warn},
+        "tip": metrics.get("rings_tip")
+        or "弦无关短环（3–8）边界不应被环外边穿越。",
+    }
+    clr_ok = float(targets.get("edge_clearance_ok") or 0.85)
+    clr_warn = float(targets.get("edge_clearance_warn") or 0.45)
+    clr_part = float(
+        parts.get("edge_clearance") if parts.get("edge_clearance") is not None else 1.0
+    )
+    edge_clearance = {
+        "status": _mid_status(clr_part, clr_ok, clr_warn),
+        "score": parts.get("edge_clearance"),
+        "hits": metrics.get("edge_clearance_hits"),
+        "nodes_hit": metrics.get("nodes_hit"),
+        "min_clearance_p50": metrics.get("min_clearance_p50"),
+        "thr": metrics.get("edge_clearance_thr"),
+        "top": metrics.get("top_edge_hits") or [],
+        "budget": {"ok": clr_ok, "warn": clr_warn},
+        "tip": metrics.get("edge_clearance_tip")
+        or "非关联边不得擦过网元；阶段2 clear_edge_hits。",
+    }
+    axis_ok = float(targets.get("edge_axis_ok") or 0.75)
+    axis_warn = float(targets.get("edge_axis_warn") or 0.45)
+    axis_part = float(
+        parts.get("edge_axis") if parts.get("edge_axis") is not None else 1.0
+    )
+    edge_axis = {
+        "status": _mid_status(axis_part, axis_ok, axis_warn),
+        "score": parts.get("edge_axis"),
+        "axis_frac": metrics.get("axis_frac"),
+        "horiz_frac": metrics.get("horiz_frac"),
+        "vert_frac": metrics.get("vert_frac"),
+        "diag_frac": metrics.get("diag_frac"),
+        "horiz_n": metrics.get("horiz_n"),
+        "vert_n": metrics.get("vert_n"),
+        "diag_n": metrics.get("diag_n"),
+        "tol_deg": metrics.get("edge_axis_tol_deg"),
+        "top_skew": metrics.get("top_skew_edges") or [],
+        "budget": {"ok": axis_ok, "warn": axis_warn},
+        "tip": metrics.get("edge_axis_tip")
+        or "边宜水平/垂直（水平优先）；阶段2 straighten_channels / polish。",
+    }
+
+    dims = {
+        "overlap": overlap,
+        "crossing": crossing,
+        "spacing": spacing,
+        "sparsity": sparsity,
+        "edges": edges,
+        "chains": chains,
+        "rings": rings,
+        "edge_clearance": edge_clearance,
+        "edge_axis": edge_axis,
+    }
+    overall = grade.get("overall") or "fail"
+    # sparsity / mid-tier fails should surface in overall
+    order = {"ok": 0, "warn": 1, "fail": 2}
+    for d in dims.values():
+        st = str(d.get("status") or "ok")
+        if order.get(st, 0) > order.get(overall, 0):
+            overall = st
+
+    issues = list(grade.get("issues") or [])
+    if sparsity["status"] == "fail" and not any("space_utilization" in i for i in issues):
+        issues.append(f"sparsity: util={util} whitespace={white} grid={grid_occ}")
+    if sparsity["status"] == "warn" and not any("space_utilization" in i for i in issues):
+        issues.append(f"sparsity_warn: util={util} whitespace={white}")
+    if chains["status"] != "ok":
+        issues.append(
+            f"chain_{chains['status']}: straight_p50={chains.get('straightness_p50')} "
+            f"kinks={chains.get('kink_count')} score={chains.get('score')}"
+        )
+    if rings["status"] != "ok":
+        issues.append(
+            f"rings_{rings['status']}: pierced={rings.get('rings_pierced')}/"
+            f"{rings.get('ring_count')} pierce_x={rings.get('pierce_crossings')} "
+            f"score={rings.get('score')}"
+        )
+    if edge_clearance["status"] != "ok":
+        issues.append(
+            f"edge_clearance_{edge_clearance['status']}: "
+            f"hits={edge_clearance.get('hits')} nodes={edge_clearance.get('nodes_hit')} "
+            f"score={edge_clearance.get('score')}"
+        )
+    if edge_axis["status"] != "ok":
+        issues.append(
+            f"edge_axis_{edge_axis['status']}: "
+            f"H={edge_axis.get('horiz_n')} V={edge_axis.get('vert_n')} "
+            f"D={edge_axis.get('diag_n')} score={edge_axis.get('score')}"
+        )
+
+    return {
+        "verdict": {
+            "overall": overall,
+            "total": score.get("total"),
+            "headline": _headline(overall, dims),
+            "issues": issues,
+        },
+        "size": {
+            "nodes": metrics.get("node_count"),
+            "links": metrics.get("link_count"),
+            "bbox": metrics.get("bbox"),
+            "bbox_area": metrics.get("bbox_area"),
+            "hull_area": metrics.get("hull_area"),
+        },
+        "overlap": overlap,
+        "crossing": crossing,
+        "spacing": spacing,
+        "sparsity": sparsity,
+        "edges": edges,
+        "chains": chains,
+        "rings": rings,
+        "edge_clearance": edge_clearance,
+        "edge_axis": edge_axis,
+        "score": {
+            "total": score.get("total"),
+            "parts": parts,
+            "weights": score.get("weights"),
+            "rank_key": score.get("rank_key"),
+            "targets": targets,
+            "hint": score.get("hint"),
+        },
+        "guide": {
+            "spacing": metrics.get("spacing_guide"),
+            "crossing_definition": metrics.get("crossing_definition"),
+            "how_to_read": (
+                "只看本工具即可验收：verdict.total∈[0,100]；"
+                "overlap/crossing/spacing/sparsity/edges/chains/rings/"
+                "edge_clearance/edge_axis 各有 status。"
+                f"score_profile={score.get('score_profile') or 'default'}："
+                "eye 下调 axis/rings、上调 util/clearance；"
+                "贴边分=节点命中∪hits/link；compactness 仅诊断不加权。"
+                "扫参用 score.rank_key（先零重叠，再高 total）。"
+            ),
+        },
+    }
+
+
+def analyze_layout_stats(
+    nodes: list[dict[str, Any]],
+    edges: list[dict[str, Any]],
+    *,
+    with_meta: bool = False,
+    ume_reference: bool = False,
+    fast: bool = False,
+    score_profile: str | None = "auto",
+) -> dict[str, Any]:
+    """Full stats: flat metrics + composite score + unified report.
+
+    ``fast=True`` skips ring-pierce (expensive on giant metro canvases) and
+    still scores overlap/crossing/util/chains for apply gates + agent QA.
+    ``score_profile``: auto|default|eye — eye softens axis/rings for dual sinks.
+    """
+    base = analyze_positions(nodes, edges, with_meta=with_meta)
+    pos: dict[str, tuple[float, float]] = {}
+    names: dict[str, str] = {}
+    for n in nodes:
+        if not isinstance(n, dict):
+            continue
+        fid = str(n.get("fabric_node_id") or n.get("id") or "").strip()
+        if not fid:
+            continue
+        try:
+            x = float(n.get("x") if n.get("x") is not None else 0.0)
+            y = float(n.get("y") if n.get("y") is not None else 0.0)
+        except (TypeError, ValueError):
+            x, y = 0.0, 0.0
+        pos[fid] = (x, y)
+        names[fid] = str(n.get("name") or n.get("label") or fid)
+
+    from netx_api.mcp_remote.topology.layout_metrics import (
+        collapse_links,
+        compute_edge_axis,
+        compute_edge_clearance,
+    )
+    from netx_api.mcp_remote.topology.layout_topology_quality import (
+        compute_chain_cohesion,
+        compute_topology_quality,
+    )
+
+    links = collapse_links(edges)
+    density = compute_density_stats(pos, names, links)
+    if fast:
+        chain_q = compute_chain_cohesion(pos, links)
+        rings_q = {
+            "ring_count": None,
+            "rings_pierced": None,
+            "pierce_crossings": None,
+            "score": 1.0,
+            "tip": "fast score: ring pierce skipped (n/links large).",
+        }
+    else:
+        quality = compute_topology_quality(pos, links)
+        chain_q = quality.get("chains") or {}
+        rings_q = quality.get("rings") or {}
+    clr_q = compute_edge_clearance(pos, links, names=names)
+    axis_q = compute_edge_axis(pos, links, names=names)
+    merged = {**base, **density}
+    merged["space_utilization"] = density["space_utilization"]
+    merged["chain_score"] = chain_q.get("score", 1.0)
+    merged["chain_count"] = chain_q.get("chain_count")
+    merged["chain_nodes"] = chain_q.get("chain_nodes")
+    merged["chain_straightness_p50"] = chain_q.get("straightness_p50")
+    merged["chain_straightness_mean"] = chain_q.get("straightness_mean")
+    merged["chain_kink_count"] = chain_q.get("kink_count")
+    merged["chain_kink_frac"] = chain_q.get("kink_frac")
+    merged["chain_tip"] = chain_q.get("tip")
+    merged["rings_score"] = rings_q.get("score", 1.0)
+    merged["ring_count"] = rings_q.get("ring_count")
+    merged["rings_pierced"] = rings_q.get("rings_pierced")
+    merged["ring_pierce_crossings"] = rings_q.get("pierce_crossings")
+    merged["rings_tip"] = rings_q.get("tip")
+    merged["edge_clearance_score"] = clr_q.get("edge_clearance_score", 1.0)
+    merged["edge_clearance_hits"] = clr_q.get("edge_clearance_hits")
+    merged["nodes_hit"] = clr_q.get("nodes_hit")
+    merged["min_clearance_p50"] = clr_q.get("min_clearance_p50")
+    merged["top_edge_hits"] = clr_q.get("top_edge_hits") or []
+    merged["edge_clearance_tip"] = clr_q.get("edge_clearance_tip")
+    merged["edge_clearance_thr"] = clr_q.get("edge_clearance_thr")
+    merged["edge_clearance_skipped"] = clr_q.get("edge_clearance_skipped")
+    merged["hit_per_link"] = clr_q.get("hit_per_link")
+    merged["edge_axis_score"] = axis_q.get("edge_axis_score", 1.0)
+    merged["axis_frac"] = axis_q.get("axis_frac")
+    merged["horiz_frac"] = axis_q.get("horiz_frac")
+    merged["vert_frac"] = axis_q.get("vert_frac")
+    merged["diag_frac"] = axis_q.get("diag_frac")
+    merged["horiz_n"] = axis_q.get("horiz_n")
+    merged["vert_n"] = axis_q.get("vert_n")
+    merged["diag_n"] = axis_q.get("diag_n")
+    merged["top_skew_edges"] = axis_q.get("top_skew_edges") or []
+    merged["edge_axis_tip"] = axis_q.get("edge_axis_tip")
+    merged["edge_axis_tol_deg"] = axis_q.get("edge_axis_tol_deg")
+    merged["edge_axis_tol_px"] = axis_q.get("edge_axis_tol_px")
+    merged["score_profile"] = score_profile
+    score = score_layout_components(merged, score_profile=score_profile)
+    grade = grade_layout(merged, ume_reference=ume_reference)
+    packed = {
+        **merged,
+        "score": score,
+        "grade": grade,
+        "summary": {
+            "total": score["total"],
+            "overall": grade.get("overall"),
+            "score_profile": score.get("score_profile"),
+            "crossings": merged.get("edge_crossings"),
+            "cpl": merged.get("crossings_per_link"),
+            "overlaps": merged.get("footprint_overlap_pairs"),
+            "nn_p50": merged.get("nn_p50"),
+            "util": merged.get("space_utilization"),
+            "grid_occ": merged.get("grid_occupancy"),
+            "whitespace": merged.get("whitespace_index"),
+            "edge_stretch": merged.get("edge_stretch_p50"),
+            "chain": merged.get("chain_score"),
+            "rings": merged.get("rings_score"),
+            "rings_pierced": merged.get("rings_pierced"),
+            "edge_clearance": merged.get("edge_clearance_score"),
+            "edge_clearance_hits": merged.get("edge_clearance_hits"),
+            "edge_axis": merged.get("edge_axis_score"),
+            "axis_frac": merged.get("axis_frac"),
+            "horiz_frac": merged.get("horiz_frac"),
+            "diag_n": merged.get("diag_n"),
+            "top_crossing": [
+                {
+                    "name": r.get("name"),
+                    "hits": r.get("crossing_hits"),
+                    "id": r.get("fabric_node_id"),
+                }
+                for r in (merged.get("top_crossing_nodes") or [])[:5]
+            ],
+            "top_crossing_edges": [
+                {
+                    "label": r.get("label"),
+                    "hits": r.get("crossing_hits"),
+                    "a": r.get("a_name"),
+                    "b": r.get("b_name"),
+                    "a_id": r.get("a_node_id"),
+                    "b_id": r.get("b_node_id"),
+                }
+                for r in (merged.get("top_crossing_edges") or [])[:5]
+            ],
+        },
+    }
+    packed["report"] = build_layout_report(packed)
+    # Align summary.overall with report (includes sparsity)
+    packed["summary"]["overall"] = packed["report"]["verdict"]["overall"]
+    packed["summary"]["headline"] = packed["report"]["verdict"]["headline"]
+    return packed
+
+

netx_api/mcp_remote/topology/layout_structure.py

@@ -0,0 +1,625 @@
+"""Graph structure stats for layout planning (shape / gravity / blocks).
+
+Read-only: no coordinates required. Feeds analyzeTopologyViewLayout(detail=structure).
+
+Order of judgment (important):
+  1) global / per-component **shape** (chains first — simplest & common)
+  2) hub gravity (core_bar / agg_bar / mixed) only when not chain-like
+  3) complex canvases = pack of blocks; lay each block, then compose
+  4) soft_blocks: hub territories inside giant CCs (+ optional igraph leftovers)
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict, deque
+from statistics import mean, median
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import collapse_links
+from netx_api.mcp_remote.topology.layout_ops.graph_util import infer_layer
+
+
+def _pct(xs: list[int], p: float) -> float:
+    if not xs:
+        return 0.0
+    s = sorted(xs)
+    if len(s) == 1:
+        return float(s[0])
+    i = min(len(s) - 1, max(0, int(round((len(s) - 1) * p))))
+    return float(s[i])
+
+
+def _connected_components(
+    ids: list[str], adj: dict[str, set[str]], names: dict[str, str]
+) -> list[list[str]]:
+    seen: set[str] = set()
+    out: list[list[str]] = []
+    for s in sorted(ids, key=lambda x: names.get(x, x)):
+        if s in seen:
+            continue
+        q: deque[str] = deque([s])
+        seen.add(s)
+        comp: list[str] = []
+        while q:
+            u = q.popleft()
+            comp.append(u)
+            for v in adj.get(u, ()):
+                if v not in seen:
+                    seen.add(v)
+                    q.append(v)
+        out.append(comp)
+    out.sort(key=lambda c: (-len(c), names.get(c[0], c[0])))
+    return out
+
+
+def _farthest(
+    start: str, allowed: set[str], adj: dict[str, set[str]]
+) -> tuple[tuple[str, int], dict[str, str | None]]:
+    q: deque[tuple[str, int]] = deque([(start, 0)])
+    seen = {start}
+    best = (start, 0)
+    parent: dict[str, str | None] = {start: None}
+    while q:
+        u, d = q.popleft()
+        if d > best[1]:
+            best = (u, d)
+        for v in adj.get(u, ()):
+            if v in allowed and v not in seen:
+                seen.add(v)
+                parent[v] = u
+                q.append((v, d + 1))
+    return best, parent
+
+
+def _diameter_path(
+    comp: list[str], adj: dict[str, set[str]], names: dict[str, str]
+) -> list[str]:
+    s = set(comp)
+    if not s:
+        return []
+    leaves = [n for n in comp if len(adj.get(n, ())) <= 1] or list(comp)
+    start = sorted(leaves, key=lambda n: names.get(n, n))[0]
+    (e1, _), _ = _farthest(start, s, adj)
+    (e2, _), parent = _farthest(e1, s, adj)
+    path = [e2]
+    while parent[path[-1]] is not None:
+        path.append(parent[path[-1]] or "")
+    path.reverse()
+    return [n for n in path if n]
+
+
+def _block_shape(
+    comp: list[str],
+    adj: dict[str, set[str]],
+    names: dict[str, str],
+    layers: dict[str, str],
+) -> dict[str, Any]:
+    """Classify one connected component: chain | star | mesh | tiny."""
+    n = len(comp)
+    if n <= 2:
+        return {
+            "shape": "tiny",
+            "node_count": n,
+            "link_count": sum(len(adj[u]) for u in comp) // 2,
+            "spine_len": n,
+            "chain_frac": 1.0,
+            "max_degree": max((len(adj[u]) for u in comp), default=0),
+            "mean_degree": round(mean(len(adj[u]) for u in comp), 2) if comp else 0.0,
+        }
+    degs = [len(adj[u]) for u in comp]
+    low = sum(1 for d in degs if d <= 2) / n
+    max_d = max(degs)
+    mean_d = mean(degs)
+    path = _diameter_path(comp, adj, names)
+    spine_len = len(path)
+    # nodes on diameter path / n — high ⇒ path-like
+    on_spine = spine_len / n
+    # edges ≈ n-1 ⇒ tree/path; denser ⇒ mesh
+    e = sum(degs) // 2
+    treeish = e <= n  # forest/tree (path has e=n-1)
+
+    shape = "mesh"
+    # Path / corridor first (CN/AN on spine with deg≤5 still counts as chain)
+    nearly_path = mean_d <= 2.45 and low >= 0.62 and max_d <= 5 and e <= n + 2
+    if nearly_path or (treeish and low >= 0.65 and mean_d <= 2.5 and max_d <= 5):
+        shape = "chain"
+    elif treeish and low >= 0.72 and mean_d <= 2.35 and max_d <= 6:
+        shape = "chain"
+    elif on_spine >= 0.25 and low >= 0.68 and mean_d <= 2.4 and max_d <= 5:
+        shape = "chain"
+    elif max_d >= 7 and low >= 0.55 and on_spine < 0.4:
+        shape = "star"
+    elif max_d >= 8 or (mean_d >= 2.8 and e > n + max(2, n // 10)):
+        shape = "mesh"
+    else:
+        if on_spine >= 0.35 and low >= 0.6:
+            shape = "chain"
+        elif max_d >= 6 and low >= 0.5:
+            shape = "star"
+
+    hubs = sorted(
+        [u for u in comp if layers.get(u) in {"core", "agg"}],
+        key=lambda u: (-len(adj[u]), names.get(u, u)),
+    )[:4]
+    return {
+        "shape": shape,
+        "node_count": n,
+        "link_count": e,
+        "spine_len": spine_len,
+        "on_spine_frac": round(on_spine, 3),
+        "chain_frac": round(low, 3),  # frac deg<=2
+        "max_degree": max_d,
+        "mean_degree": round(mean_d, 2),
+        "treeish": treeish,
+        "hub_ids": hubs,
+        "spine_ends": [path[0], path[-1]] if path else [],
+        "spine_end_names": [names.get(path[0], ""), names.get(path[-1], "")] if path else [],
+    }
+
+
+def _bfs_territory(
+    hubs: list[str],
+    access: set[str],
+    adj: dict[str, set[str]],
+) -> dict[str, str]:
+    """Multi-source Voronoi on access: each access node → nearest hub (via stubs)."""
+    owner: dict[str, str] = {}
+    q: deque[str] = deque()
+    for h in sorted(hubs):
+        for nb in adj.get(h, ()):
+            if nb in access and nb not in owner:
+                owner[nb] = h
+                q.append(nb)
+    while q:
+        u = q.popleft()
+        h = owner[u]
+        for v in adj.get(u, ()):
+            if v in access and v not in owner:
+                owner[v] = h
+                q.append(v)
+    return owner
+
+
+def analyze_graph_structure(
+    nodes: list[dict[str, Any]],
+    edges: list[dict[str, Any]],
+    *,
+    hub_top_k: int = 12,
+    stub_top_k: int = 24,
+) -> dict[str, Any]:
+    """Return shape/blocks, hubs, gravity type, recipe preference."""
+    names: dict[str, str] = {}
+    layers: dict[str, str] = {}
+    ids: list[str] = []
+    for n in nodes:
+        if not isinstance(n, dict):
+            continue
+        fid = str(n.get("fabric_node_id") or n.get("id") or "").strip()
+        if not fid:
+            continue
+        nm = str(n.get("name") or n.get("label") or fid)
+        ids.append(fid)
+        names[fid] = nm
+        layers[fid] = infer_layer(nm, n.get("role"), n.get("level"))
+
+    adj: dict[str, set[str]] = {i: set() for i in ids}
+    links = collapse_links(edges)
+    for a, b in links:
+        if a in adj and b in adj:
+            adj[a].add(b)
+            adj[b].add(a)
+
+    deg = {i: len(adj[i]) for i in ids}
+    access = {i for i in ids if layers.get(i) == "access"}
+    layer_known = sum(
+        1 for i in ids if layers.get(i) in {"external", "core", "agg", "access"}
+    )
+    if layer_known < max(3, len(ids) // 5) and ids:
+        ranked = sorted(ids, key=lambda i: (-deg[i], names[i]))
+        hub_budget = max(2, min(8, len(ids) // 15 + 2))
+        for i in ranked[:hub_budget]:
+            if layers.get(i) == "other":
+                layers[i] = "agg"
+        for i in ids:
+            if layers.get(i) == "other" and deg[i] <= 2:
+                layers[i] = "access"
+        access = {i for i in ids if layers.get(i) == "access"}
+
+    # --- blocks / shape first ---
+    comps = _connected_components(ids, adj, names)
+    blocks: list[dict[str, Any]] = []
+    for idx, comp in enumerate(comps):
+        b = _block_shape(comp, adj, names, layers)
+        b["block_id"] = idx
+        b["sample_names"] = [names[n] for n in sorted(comp, key=lambda x: names[x])[:3]]
+        blocks.append(b)
+
+    n_nodes = max(len(ids), 1)
+    chain_nodes = sum(b["node_count"] for b in blocks if b["shape"] in {"chain", "tiny"})
+    star_nodes = sum(b["node_count"] for b in blocks if b["shape"] == "star")
+    mesh_nodes = sum(b["node_count"] for b in blocks if b["shape"] == "mesh")
+    chain_frac_nodes = chain_nodes / n_nodes
+    global_low = (sum(1 for i in ids if deg[i] <= 2) / n_nodes) if ids else 0.0
+    global_mean = mean(deg.values()) if deg else 0.0
+    global_max = max(deg.values()) if deg else 0
+
+    primary_shape = "mesh"
+    if chain_frac_nodes >= 0.55 or (global_low >= 0.72 and global_mean <= 2.35 and global_max <= 6):
+        primary_shape = "chains"
+    elif star_nodes / n_nodes >= 0.45:
+        primary_shape = "star"
+    elif mesh_nodes / n_nodes >= 0.45:
+        primary_shape = "mesh"
+    elif chain_frac_nodes >= 0.35:
+        primary_shape = "mixed_blocks"  # chain corridors + some hub blocks
+
+    shape_block = {
+        "primary": primary_shape,
+        "component_count": len(comps),
+        "chain_node_frac": round(chain_frac_nodes, 3),
+        "star_node_frac": round(star_nodes / n_nodes, 3),
+        "mesh_node_frac": round(mesh_nodes / n_nodes, 3),
+        "deg_le2_frac": round(global_low, 3),
+        "mean_degree": round(global_mean, 2),
+        "max_degree": global_max,
+        "blocks": blocks[:40],
+        "strategy": (
+            "Lay each connected component as its own block: "
+            "chain→horizontal spine + short stubs; star→hub + petals; "
+            "mesh→corridor/compact. Pack blocks in rows. "
+            "Do NOT treat a chain canvas as hub-and-spoke rings."
+        ),
+    }
+
+    by_layer: dict[str, list[str]] = defaultdict(list)
+    for i in ids:
+        by_layer[layers.get(i, "other")].append(i)
+
+    owner = _bfs_territory(
+        [i for i in ids if layers.get(i) in {"core", "agg"}],
+        access,
+        adj,
+    )
+    territory_count: dict[str, int] = defaultdict(int)
+    for h in owner.values():
+        territory_count[h] += 1
+
+    def layer_stats(layer: str) -> dict[str, Any]:
+        members = by_layer.get(layer, [])
+        if not members:
+            return {
+                "count": 0,
+                "max_degree": 0,
+                "mean_degree": 0.0,
+                "access_neighbor_sum": 0,
+                "access_cover": 0,
+                "access_cover_frac": 0.0,
+                "territory": 0,
+                "territory_frac": 0.0,
+            }
+        degs = [deg[i] for i in members]
+        covered: set[str] = set()
+        access_nb_sum = 0
+        terr = 0
+        for i in members:
+            nbs = [x for x in adj.get(i, ()) if x in access]
+            access_nb_sum += len(nbs)
+            covered.update(nbs)
+            terr += territory_count.get(i, 0)
+        n_access = max(len(access), 1)
+        return {
+            "count": len(members),
+            "max_degree": max(degs) if degs else 0,
+            "mean_degree": round(mean(degs), 2) if degs else 0.0,
+            "access_neighbor_sum": access_nb_sum,
+            "access_cover": len(covered),
+            "access_cover_frac": round(len(covered) / n_access, 3),
+            "territory": terr,
+            "territory_frac": round(terr / n_access, 3),
+        }
+
+    layer_block = {
+        "external": layer_stats("external"),
+        "core": layer_stats("core"),
+        "agg": layer_stats("agg"),
+        "access": {
+            "count": len(access),
+            "max_degree": max((deg[i] for i in access), default=0),
+            "mean_degree": round(mean([deg[i] for i in access]), 2) if access else 0.0,
+        },
+        "other": layer_stats("other"),
+    }
+
+    hub_scores: list[tuple[float, str]] = []
+    for i in ids:
+        if layers.get(i) not in {"core", "agg"}:
+            continue
+        an = sum(1 for x in adj.get(i, ()) if x in access)
+        score = deg[i] * 1.0 + an * 2.0 + territory_count.get(i, 0) * 0.5
+        hub_scores.append((score, i))
+    hub_scores.sort(key=lambda t: (-t[0], names[t[1]]))
+    hubs_out: list[dict[str, Any]] = []
+    for score, i in hub_scores[: max(1, min(40, hub_top_k))]:
+        an = sorted(
+            (x for x in adj.get(i, ()) if x in access),
+            key=lambda x: (-deg[x], names[x]),
+        )
+        hubs_out.append(
+            {
+                "fabric_node_id": i,
+                "name": names[i],
+                "layer": layers[i],
+                "degree": deg[i],
+                "access_neighbors": len(an),
+                "territory": territory_count.get(i, 0),
+                "score": round(score, 2),
+                "stub_ids": an[:8],
+            }
+        )
+
+    stub_rows: list[dict[str, Any]] = []
+    for h in hubs_out[:8]:
+        hid = h["fabric_node_id"]
+        for sid in h.get("stub_ids") or []:
+            stub_rows.append(
+                {
+                    "hub_id": hid,
+                    "hub_name": h["name"],
+                    "stub_id": sid,
+                    "stub_name": names.get(sid, sid),
+                    "stub_degree": deg.get(sid, 0),
+                }
+            )
+    stub_rows.sort(key=lambda r: (-r["stub_degree"], r["hub_name"], r["stub_name"]))
+    stub_rows = stub_rows[: max(1, min(60, stub_top_k))]
+
+    core_s = layer_block["core"]
+    agg_s = layer_block["agg"]
+    rationale: list[str] = []
+    gravity_type = "unclear"
+    confidence = 0.35
+    anchor_layer = "access"
+    decorative: list[str] = []
+    geometry = "chain_rows"
+    recipe_pref = ["corridor", "compact", "rings"]
+
+    c_terr = float(core_s["territory_frac"])
+    a_terr = float(agg_s["territory_frac"])
+    c_deg = int(core_s["max_degree"])
+    a_deg = int(agg_s["max_degree"])
+    c_n = int(core_s["count"])
+    a_n = int(agg_s["count"])
+
+    # 1) Chain-first — never fall through to hub-bar on path graphs
+    if primary_shape == "chains":
+        gravity_type = "chains"
+        confidence = min(0.97, 0.55 + chain_frac_nodes * 0.4)
+        anchor_layer = "access"
+        geometry = "chain_rows"
+        recipe_pref = ["corridor", "compact", "rings"]
+        rationale.append(
+            f"shape=chains; deg_le2_frac={global_low:.2f}; "
+            f"components={len(comps)}; lay spines+stubs per block"
+        )
+    elif primary_shape == "mixed_blocks":
+        gravity_type = "mixed_blocks"
+        confidence = 0.7
+        anchor_layer = "agg" if a_n else ("core" if c_n else "access")
+        geometry = "block_pack"
+        recipe_pref = ["corridor", "compact", "rings"]
+        rationale.append("mixed chain blocks + hub blocks; process per component")
+    elif c_n == 0 and a_n > 0 and (a_terr >= 0.35 or a_deg >= 4):
+        gravity_type = "agg_bar"
+        confidence = min(0.95, 0.55 + a_terr * 0.4)
+        anchor_layer = "agg"
+        geometry = "agg_bar_mid"
+        recipe_pref = ["rings", "compact", "corridor"]
+        rationale.append("no_core_layer; agg holds access territory")
+        if a_n >= 2:
+            rationale.append(
+                "dual_agg: rings uses min-ring path nesting (parallel corridors) "
+                "before per-AN petals"
+            )
+    elif c_n > 0 and (
+        c_terr >= a_terr * 1.35 or (c_deg >= max(6, int(a_deg * 1.4)) and c_terr >= 0.25)
+    ):
+        gravity_type = "core_bar"
+        confidence = min(0.95, 0.5 + c_terr * 0.45)
+        anchor_layer = "core"
+        if a_n > 0 and a_terr < 0.2 and a_deg <= max(4, c_deg // 2):
+            decorative.append("agg")
+            rationale.append("agg_low_attachment; treat as decorative bar")
+        core_hubs = [h for h in hubs_out if h["layer"] == "core"][:4]
+        if len(core_hubs) >= 2 and core_hubs[1]["degree"] >= max(
+            4, core_hubs[0]["degree"] * 0.5
+        ):
+            geometry = "core_center"
+            rationale.append("dual_core_hubs; prefer horizontal beam at mid/upper-mid")
+        else:
+            geometry = "core_top"
+            rationale.append("core_holds_access; default core beam above access fans")
+        recipe_pref = ["compact", "corridor", "rings"]
+        rationale.append("core_territory_or_degree_dominates_agg")
+        rationale.append(
+            "prefer dual_units → sinkTopologyDualUnits; "
+            "small core: layout compact|corridor"
+        )
+    elif c_n > 0 and a_n > 0 and c_terr >= 0.15 and a_terr >= 0.15:
+        gravity_type = "mixed"
+        confidence = min(0.9, 0.45 + min(c_terr, a_terr) * 0.5)
+        anchor_layer = "core" if c_terr >= a_terr else "agg"
+        geometry = "core_center" if anchor_layer == "core" else "agg_bar_mid"
+        recipe_pref = ["compact", "rings", "corridor"]
+        rationale.append("both_core_and_agg_own_access_territory")
+    elif hubs_out and primary_shape == "star":
+        top = hubs_out[0]
+        gravity_type = "agg_bar" if top["layer"] == "agg" else "core_bar"
+        confidence = 0.55
+        anchor_layer = top["layer"]
+        geometry = "agg_bar_mid" if top["layer"] == "agg" else "core_center"
+        recipe_pref = (
+            ["rings", "compact", "corridor"]
+            if top["layer"] == "agg"
+            else ["compact", "corridor", "rings"]
+        )
+        rationale.append("shape=star; hub-and-spoke")
+    elif hubs_out:
+        top = hubs_out[0]
+        gravity_type = "core_bar" if top["layer"] == "core" else "agg_bar"
+        confidence = 0.45
+        anchor_layer = top["layer"]
+        geometry = "core_center" if top["layer"] == "core" else "agg_bar_mid"
+        recipe_pref = (
+            ["compact", "corridor", "rings"]
+            if top["layer"] == "core"
+            else ["rings", "compact", "corridor"]
+        )
+        rationale.append("fallback_top_hub_layer")
+    else:
+        gravity_type = "chains" if global_low >= 0.6 else "unclear"
+        geometry = "chain_rows" if gravity_type == "chains" else "compact"
+        recipe_pref = ["corridor", "compact", "rings"]
+        rationale.append("no_clear_hubs; corridor baseline")
+
+    degs = list(deg.values())
+    chain_mode = gravity_type in {"chains", "mixed_blocks"}
+    advice = {
+        "pin_anchors": (
+            []
+            if gravity_type == "chains"
+            else [h["fabric_node_id"] for h in hubs_out[:4]]
+        ),
+        "preview_recipes": list(recipe_pref[:2]),
+        "skip_rings_first": gravity_type in {"core_bar", "chains", "mixed_blocks"},
+        "geometry": geometry,
+        "layout_over_rank": True,
+        "decompose_by_component": True,
+        "decompose_soft_blocks": primary_shape in {"star", "mesh", "mixed_blocks"}
+        or (len(comps) <= 2 and star_nodes >= 40),
+        "block_plan": [
+            {
+                "block_id": b["block_id"],
+                "shape": b["shape"],
+                "n": b["node_count"],
+                "how": (
+                    "spine+short stubs, row pack"
+                    if b["shape"] in {"chain", "tiny"}
+                    else (
+                        "hub center + petals"
+                        if b["shape"] == "star"
+                        else "corridor/compact then local untangle"
+                    )
+                ),
+            }
+            for b in blocks[:20]
+        ],
+        "next": (
+            "CHAIN canvas: for each structure.shape.blocks item with shape=chain, "
+            "lay diameter spine horizontally, hang deg-1/short branches as stubs, "
+            "stack components as rows; preview corridor/compact (skip rings); "
+            "untangle residual."
+            if chain_mode
+            else (
+                "Giant star/core_bar: prefer structure.dual_units "
+                "(two portals + ≥2 interior-disjoint corridors). "
+                "Batch sinkTopologyDualUnits (layout_batch) → orbit_sweep → "
+                "polish_crossings → clear_edge_hits. "
+                "Portals may appear in multiple units. "
+                "Small graphs: layout compact|corridor|rings. "
+                "agg_bar: preview rings first."
+            )
+        ),
+        "prefer_dual_units": (
+            not chain_mode
+            and (
+                primary_shape in {"star", "mesh", "mixed_blocks"}
+                or gravity_type in {"core_bar", "mixed", "mixed_blocks"}
+                or star_nodes >= 40
+            )
+        ),
+    }
+
+    # Soft blocks inside giant CCs (hub BFS; igraph optional for leftovers).
+    soft_blocks: dict[str, Any] = {"block_count": 0, "blocks": [], "igraph": False}
+    dual_units: dict[str, Any] = {"unit_count": 0, "units": []}
+    try:
+        from netx_api.mcp_remote.topology.layout_ops.dual_units import dual_units_report
+        from netx_api.mcp_remote.topology.layout_ops.graph_util import build_state_from_nodes_edges
+        from netx_api.mcp_remote.topology.layout_ops.partition import partition_report
+
+        st = build_state_from_nodes_edges(nodes, edges)
+        soft_mode = "soft" if advice.get("decompose_soft_blocks") else "hub_territory"
+        soft_blocks = partition_report(st, mode=soft_mode)
+        dual_units = dual_units_report(st)
+    except Exception as exc:  # pragma: no cover - defensive
+        soft_blocks = {
+            "block_count": 0,
+            "blocks": [],
+            "igraph": False,
+            "error": str(exc)[:120],
+        }
+        dual_units = {"unit_count": 0, "units": [], "error": str(exc)[:120]}
+
+    anchors = []
+    if gravity_type == "chains":
+        # expose spine ends of largest chain blocks as soft anchors
+        for b in blocks[:6]:
+            if b["shape"] not in {"chain", "tiny"}:
+                continue
+            for end, nm in zip(b.get("spine_ends") or [], b.get("spine_end_names") or []):
+                anchors.append(
+                    {
+                        "fabric_node_id": end,
+                        "name": nm,
+                        "layer": layers.get(end, "access"),
+                        "degree": deg.get(end, 0),
+                        "territory": 0,
+                        "role": "spine_end",
+                    }
+                )
+    else:
+        anchors = [
+            {
+                "fabric_node_id": h["fabric_node_id"],
+                "name": h["name"],
+                "layer": h["layer"],
+                "degree": h["degree"],
+                "territory": h["territory"],
+            }
+            for h in hubs_out[:6]
+            if h["layer"] == anchor_layer
+            or (gravity_type in {"mixed", "mixed_blocks"} and h["layer"] in {"core", "agg"})
+        ]
+
+    return {
+        "node_count": len(ids),
+        "link_count": len(links),
+        "degree": {
+            "max": max(degs) if degs else 0,
+            "mean": round(mean(degs), 2) if degs else 0.0,
+            "median": float(median(degs)) if degs else 0.0,
+            "p90": _pct(degs, 0.9),
+            "le2_frac": round(global_low, 3),
+        },
+        "shape": shape_block,
+        "layers": layer_block,
+        "hubs": hubs_out,
+        "stubs": stub_rows,
+        "soft_blocks": soft_blocks,
+        "dual_units": dual_units,
+        "gravity": {
+            "type": gravity_type,
+            "confidence": round(confidence, 3),
+            "anchor_layer": anchor_layer,
+            "decorative_layers": decorative,
+            "geometry_hint": geometry,
+            "recipe_preference": recipe_pref,
+            "rationale": rationale,
+            "anchors": anchors,
+        },
+        "advice": advice,
+        "headline": (
+            f"shape={primary_shape} gravity={gravity_type} "
+            f"comps={len(comps)} geometry={geometry} try={'>'.join(recipe_pref[:2])}"
+        ),
+    }

netx_api/mcp_remote/topology/layout_tool.py

@@ -0,0 +1,990 @@
+"""MCP-facing layout runner: full layout + local fix/relax (no temp scripts)."""
+
+from __future__ import annotations
+
+from dataclasses import replace
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_ops import (
+    LayoutParams,
+    build_state_from_nodes_edges,
+    positions_for_api,
+    run_recipe,
+    score_state,
+)
+from netx_api.mcp_remote.topology.layout_ops.hotspots import (
+    fix_overlaps_local,
+    hotspot_scopes,
+    overlapping_nodes,
+)
+from netx_api.mcp_remote.topology.layout_ops.recipe import RECIPES
+from netx_api.mcp_remote.topology.layout_ops.state import LayoutState
+from netx_api.mcp_remote.topology.layout_ops.transforms import normalize_origin
+from netx_api.mcp_remote.topology.layout_ops.channels import (
+    straighten_channels_greedy,
+    straighten_params_from_overrides,
+)
+from netx_api.mcp_remote.topology.layout_ops.dual_units import (
+    dual_unit_params_from_overrides,
+    layout_dual_unit,
+)
+from netx_api.mcp_remote.topology.layout_ops.rigid_units import groups_from_membership
+from netx_api.mcp_remote.topology.layout_ops.untangle import (
+    untangle_crossings,
+    untangle_params_from_overrides,
+)
+from netx_api.mcp_remote.topology.layout_ops.press_crossings import (
+    park_phantom_nodes,
+    polish_crossings,
+    press_params_from_overrides,
+)
+from netx_api.mcp_remote.topology.layout_ops.clear_edge_hits import (
+    clear_edge_hits,
+    clear_edge_params_from_overrides,
+)
+from netx_api.mcp_remote.topology.layout_ops.compact_bbox import (
+    compact_bbox,
+    compact_bbox_params_from_overrides,
+)
+from netx_api.mcp_remote.topology.layout_ops.pull_far_chains import (
+    pull_far_chains,
+    pull_far_chains_params_from_overrides,
+)
+from netx_api.mcp_remote.topology.layout_ops.orbit_sweep import (
+    apply_orbit_pick,
+    orbit_params_from_overrides,
+    orbit_sweep_node,
+    orbit_sweep_round,
+    orbit_sweep_until_limit,
+)
+from netx_api.mcp_remote.topology.layout_ops.level_util import (
+    apply_level_bands,
+    level_bands_params_from_overrides,
+)
+
+# Public recipe names → internal multipass ids
+RECIPE_ALIASES: dict[str, str] = {
+    "rings": "agg_rings_v1",
+    "corridor": "smd_corridor_v1",
+    "compact": "smd_corridor_compact_v1",
+    "unstick": "smd_corridor_unstick_v1",
+    "agg_rings_v1": "agg_rings_v1",
+    "smd_corridor_v1": "smd_corridor_v1",
+    "smd_corridor_compact_v1": "smd_corridor_compact_v1",
+    "smd_corridor_unstick_v1": "smd_corridor_unstick_v1",
+}
+
+PRESETS: dict[str, dict[str, float]] = {
+    "loose": {
+        "target_nn": 170.0,
+        "scale_cap": 2.8,
+        "target_util": 0.12,
+        "pack_min_scale": 0.35,
+        "pack_iters": 6,
+        "island_pad_x": 200.0,
+        "island_pad_y": 180.0,
+        "lane": 300.0,
+        "x_gain": 1.8,
+        "pitch": 220.0,
+        "side": 200.0,
+        "an_gap": 560.0,
+        "width_mul": 2.4,
+        "height_mul": 1.5,
+    },
+    "balanced": {
+        "target_nn": 155.0,
+        "scale_cap": 2.2,
+        "target_util": 0.18,
+        "pack_min_scale": 0.28,
+        "pack_iters": 6,
+        "island_pad_x": 180.0,
+        "island_pad_y": 160.0,
+        "lane": 260.0,
+        "x_gain": 1.8,
+        "width_mul": 2.0,
+        "height_mul": 1.35,
+    },
+    "dense": {
+        "target_nn": 145.0,
+        "scale_cap": 2.0,
+        "target_util": 0.28,
+        "pack_min_scale": 0.22,
+        "pack_iters": 8,
+        "island_pad_x": 160.0,
+        "island_pad_y": 160.0,
+        "lane": 240.0,
+        "x_gain": 1.8,
+        "width_mul": 1.7,
+        "height_mul": 1.2,
+    },
+}
+
+_PARAM_KEYS = (
+    "target_nn",
+    "scale_cap",
+    "target_util",
+    "pack_min_scale",
+    "pack_iters",
+    "pack_nn_floor",
+    "island_pad_x",
+    "island_pad_y",
+    "cluster_gap",
+    "cluster_thr",
+    "x_gain",
+    "lane",
+    "overlap_iters",
+    "overlap_step",
+    "width_mul",
+    "height_mul",
+    "pitch",
+    "side",
+    "an_gap",
+)
+
+ACTIONS = (
+    "layout",
+    "fix_overlaps",
+    "untangle",
+    "straighten_channels",
+    "layout_dual_unit",
+    "polish_crossings",
+    "clear_edge_hits",
+    "compact_bbox",
+    "pull_far_chains",
+    "align_reference",
+    "orbit_sweep",
+    "level_bands",
+)
+
+
+def resolve_recipe(name: str | None) -> str:
+    key = str(name or "rings").strip().lower() or "rings"
+    if key not in RECIPE_ALIASES:
+        raise ValueError(f"unknown_recipe:{key}")
+    internal = RECIPE_ALIASES[key]
+    if internal not in RECIPES:
+        raise ValueError(f"recipe_not_registered:{internal}")
+    return internal
+
+
+def build_params(
+    *,
+    preset: str = "balanced",
+    overrides: dict[str, Any] | None = None,
+) -> LayoutParams:
+    preset_key = str(preset or "balanced").strip().lower() or "balanced"
+    if preset_key not in PRESETS:
+        raise ValueError(f"unknown_preset:{preset_key}")
+    base = LayoutParams()
+    merged = {**PRESETS[preset_key]}
+    for k, v in (overrides or {}).items():
+        if k not in _PARAM_KEYS or v is None:
+            continue
+        try:
+            merged[k] = float(v) if k not in {"pack_iters", "overlap_iters"} else int(v)
+        except (TypeError, ValueError):
+            continue
+    return replace(base, **merged)
+
+
+def _rank_key(fin: dict[str, Any]) -> tuple:
+    rk = (fin.get("score") or {}).get("rank_key")
+    if isinstance(rk, list) and rk:
+        return tuple(rk)
+    ov = int(fin.get("footprint_overlap_pairs") or 0) + int(
+        fin.get("label_overlap_pairs") or 0
+    )
+    total = float((fin.get("score") or {}).get("total") or 0.0)
+    cross = int(fin.get("edge_crossings") or 0)
+    return (ov, -total, cross)
+
+
+def _tune_grid(base: LayoutParams) -> list[LayoutParams]:
+    """Sweep knobs that actually move util/nn/crossings (incl. skeleton scale)."""
+    out: list[LayoutParams] = []
+    for tu, pms in (
+        (0.08, base.pack_min_scale),
+        (0.14, max(0.45, base.pack_min_scale - 0.05)),
+        (0.20, max(0.40, base.pack_min_scale - 0.10)),
+        (0.12, min(0.70, base.pack_min_scale + 0.05)),
+    ):
+        for wm, hm in ((2.2, 1.4), (2.8, 1.7), (3.5, 2.0)):
+            for tnn in (base.target_nn, 160.0, 140.0):
+                out.append(
+                    replace(
+                        base,
+                        target_util=tu,
+                        pack_min_scale=pms,
+                        target_nn=tnn,
+                        width_mul=wm,
+                        height_mul=hm,
+                    )
+                )
+    seen: set[tuple] = set()
+    uniq: list[LayoutParams] = []
+    for p in out:
+        key = (
+            p.target_util,
+            p.pack_min_scale,
+            p.target_nn,
+            p.width_mul,
+            p.height_mul,
+        )
+        if key in seen:
+            continue
+        seen.add(key)
+        uniq.append(p)
+    return uniq[:12]
+
+
+def _pack_result(
+    st: LayoutState,
+    fin: dict[str, Any],
+    *,
+    action: str,
+    recipe: str | None,
+    recipe_id: str | None,
+    preset: str,
+    params: LayoutParams,
+    tune: bool,
+    tried: list[dict[str, Any]] | None,
+    local: dict[str, Any] | None = None,
+) -> dict[str, Any]:
+    report = dict(fin.get("report") or {})
+    meta = st.meta or {}
+    return {
+        "ok": True,
+        "action": action,
+        "recipe": recipe,
+        "recipe_id": recipe_id,
+        "preset": preset,
+        "tune": bool(tune),
+        "tried": tried,
+        "local": local,
+        "rings_mode": meta.get("rings_mode"),
+        "min_rings": meta.get("min_rings"),
+        "params_used": {
+            k: getattr(params, k)
+            for k in (
+                "target_nn",
+                "scale_cap",
+                "target_util",
+                "pack_min_scale",
+                "island_pad_x",
+                "island_pad_y",
+                "cluster_gap",
+                "x_gain",
+                "lane",
+            )
+        },
+        "node_count": len(st.positions),
+        "positions": positions_for_api(st),
+        "verdict": report.get("verdict"),
+        "size": report.get("size"),
+        "overlap": report.get("overlap"),
+        "crossing": report.get("crossing"),
+        "spacing": report.get("spacing"),
+        "sparsity": report.get("sparsity"),
+        "edges": report.get("edges"),
+        "chains": report.get("chains"),
+        "rings": report.get("rings"),
+        "score": report.get("score"),
+        "summary": fin.get("summary") or {},
+        "guide": report.get("guide"),
+    }
+
+
+def _ensure_zero_overlap(st: LayoutState, params: LayoutParams) -> tuple[LayoutState, dict[str, Any]]:
+    """If any overlap remains, surgical local pull-apart (not global scale)."""
+    before = len(overlapping_nodes(st))
+    if before == 0:
+        return st, {"ran": False, "overlaps_before": 0, "overlaps_after": 0}
+    op = fix_overlaps_local(st, params)
+    after = len(overlapping_nodes(op.state))
+    return op.state, {
+        "ran": True,
+        "overlaps_before": before,
+        "overlaps_after": after,
+        "moved_n": len(op.moved),
+        "note": op.note,
+    }
+
+
+def run_layout_on_graph(
+    nodes: list[dict[str, Any]],
+    edges: list[dict[str, Any]],
+    *,
+    action: str = "layout",
+    recipe: str = "rings",
+    preset: str = "balanced",
+    params: dict[str, Any] | None = None,
+    tune: bool = False,
+) -> dict[str, Any]:
+    """Compute layout or local polish on existing positions. Does not PATCH."""
+    action_key = str(action or "layout").strip().lower() or "layout"
+    if action_key not in ACTIONS:
+        raise ValueError(
+            f"unknown_action:{action_key}; "
+            f"allowed={','.join(ACTIONS)}"
+        )
+    base_params = build_params(preset=preset, overrides=params)
+    st0 = build_state_from_nodes_edges(nodes, edges)
+    park_phantom_nodes(st0)
+
+    # Inject rigid/portal groups from staging membership when provided.
+    if params:
+        raw = params.get("_rigid_membership") or params.get("rigid_membership")
+        if isinstance(raw, list):
+            groups: list[dict[str, Any]] = []
+            pairs: list[tuple[str, list[str]]] = []
+            any_pivots = False
+            for row in raw:
+                if not isinstance(row, dict):
+                    continue
+                key = str(row.get("key") or "").strip()
+                ids = [str(x) for x in (row.get("node_ids") or []) if str(x)]
+                pivots = [str(x) for x in (row.get("pivots") or []) if str(x)]
+                if not key or len(ids) < 2:
+                    continue
+                if pivots:
+                    any_pivots = True
+                groups.append({"key": key, "node_ids": ids, "pivots": pivots})
+                pairs.append((key, ids))
+            if groups:
+                if not any_pivots:
+                    groups = groups_from_membership(pairs)
+                st0 = st0.copy()
+                st0.meta = dict(st0.meta or {})
+                st0.meta["compose_views"] = {
+                    **(st0.meta.get("compose_views") or {}),
+                    "rigid_groups": groups,
+                }
+
+    # --- local actions: keep current coordinates, do not rebuild skeleton ---
+    if action_key == "fix_overlaps":
+        op = fix_overlaps_local(st0, base_params)
+        st, fix_meta = _ensure_zero_overlap(op.state, base_params)
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={"op": op.params, "ensure": fix_meta, "hotspots": len(hotspot_scopes(st0))},
+        )
+
+    if action_key == "untangle":
+        knobs = untangle_params_from_overrides(params)
+        op = untangle_crossings(st0, base_params, **knobs)
+        # Origin shift only — soft_nn_scale can inflate an already-good human
+        # layout; local jumps are capped inside untangle_crossings.
+        st = normalize_origin(op.state, base_params).state
+        # Overlap crush must not erase untangle gains.
+        from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings as _cx
+
+        x_before_fix = _cx(st.positions, st.links)
+        st2, fix_meta = _ensure_zero_overlap(st, base_params)
+        x_after_fix = _cx(st2.positions, st2.links)
+        if x_after_fix <= x_before_fix + 2:
+            st = st2
+        else:
+            fix_meta = {
+                **fix_meta,
+                "reverted": True,
+                "crossings_before": x_before_fix,
+                "crossings_after": x_after_fix,
+                "reason": "overlap_fix_raised_crossings",
+            }
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={"op": op.params, "note": op.note, "meta": st.meta.get("untangle"), "ensure": fix_meta},
+        )
+
+    if action_key == "straighten_channels":
+        knobs = straighten_params_from_overrides(params)
+        op = straighten_channels_greedy(st0, base_params, **knobs)
+        st, fix_meta = _ensure_zero_overlap(op.state, base_params)
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={
+                "op": op.params,
+                "note": op.note,
+                "meta": st.meta.get("straighten_channels"),
+                "ensure": fix_meta,
+            },
+        )
+
+    if action_key == "layout_dual_unit":
+        knobs = dual_unit_params_from_overrides(params)
+        op = layout_dual_unit(
+            st0,
+            base_params,
+            unit_id=knobs.get("unit_id"),
+            portal_a=knobs.get("portal_a"),
+            portal_b=knobs.get("portal_b"),
+            require_zero_cross=bool(knobs.get("require_zero_cross", False)),
+        )
+        accepted = bool(op.params.get("accepted", False))
+        # Always return best-effort dual geometry (even if accepted=False).
+        # Do NOT run global overlap crush — it reintroduces crossings.
+        st = op.state
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={
+                "op": op.params,
+                "note": op.note,
+                "accepted": accepted,
+                "meta": st.meta.get("layout_dual_unit"),
+                "ensure": {
+                    "ran": False,
+                    "reason": "dual_unit_skips_global_overlap_crush",
+                },
+            },
+        )
+
+    if action_key == "orbit_sweep":
+        knobs = orbit_params_from_overrides(params)
+        do_round = bool(knobs.get("round"))
+        do_until = bool(knobs.get("until_limit"))
+        node_id = str(knobs.get("node_id") or "").strip()
+        frozen = knobs.get("frozen_ids")
+        protect = knobs.get("protect_rigid", "off")
+        if do_until:
+            # until_limit wins over round/node_id (single-point loop to stall).
+            op = orbit_sweep_until_limit(
+                st0,
+                params=base_params,
+                max_degree=int(knobs.get("max_degree") or 14),
+                max_jump=knobs.get("max_jump"),
+                angle_step=knobs.get("angle_step"),
+                nn_floor=float(knobs.get("nn_floor") or 36.0),
+                min_angle_sep=float(knobs.get("min_angle_sep") or 35.0),
+                protect_rigid=protect,
+                frozen_ids=frozen,
+                freeze_layers=knobs.get("freeze_layers"),
+                freeze_levels=knobs.get("freeze_levels"),
+                y_min=knobs.get("y_min"),
+                y_max=knobs.get("y_max"),
+                objective=str(knobs.get("objective") or "crossing"),
+                max_moves=int(knobs.get("max_moves") or 40),
+                stall_limit=int(knobs.get("stall_limit") or 12),
+                max_stretch=float(knobs.get("max_stretch") or 32.0),
+                min_delta=int(knobs.get("min_delta") or 1),
+                scan_cap=int(knobs.get("scan_cap") or 32),
+                top_k=int(knobs.get("top_k") or 8),
+                prefer_low_degree=bool(knobs.get("prefer_low_degree", True)),
+                cand_cap=int(knobs.get("cand_cap") or 360),
+                bundle=bool(knobs.get("bundle", True)),
+                bundle_max=int(knobs.get("bundle_max") or 10),
+            )
+            st = normalize_origin(op.state, base_params).state
+            fin = score_state(st)
+            return _pack_result(
+                st,
+                fin,
+                action=action_key,
+                recipe=None,
+                recipe_id=None,
+                preset=preset,
+                params=base_params,
+                tune=False,
+                tried=None,
+                local={
+                    "op": op.params,
+                    "note": op.note,
+                    "meta": st.meta.get("orbit_sweep"),
+                    "until_limit": True,
+                },
+            )
+        if do_round:
+            op = orbit_sweep_round(
+                st0,
+                params=base_params,
+                top_n=int(knobs.get("top_n") or 12),
+                max_degree=int(knobs.get("max_degree") or 9),
+                max_jump=knobs.get("max_jump"),
+                angle_step=knobs.get("angle_step"),
+                nn_floor=float(knobs.get("nn_floor") or 36.0),
+                min_angle_sep=float(knobs.get("min_angle_sep") or 35.0),
+                protect_rigid=protect,
+                frozen_ids=frozen,
+                freeze_layers=knobs.get("freeze_layers"),
+                freeze_levels=knobs.get("freeze_levels"),
+                focus_ids=knobs.get("focus_ids"),
+                y_min=knobs.get("y_min"),
+                y_max=knobs.get("y_max"),
+                objective=str(knobs.get("objective") or "crossing"),
+            )
+            st = normalize_origin(op.state, base_params).state
+            fin = score_state(st)
+            return _pack_result(
+                st,
+                fin,
+                action=action_key,
+                recipe=None,
+                recipe_id=None,
+                preset=preset,
+                params=base_params,
+                tune=False,
+                tried=None,
+                local={
+                    "op": op.params,
+                    "note": op.note,
+                    "meta": st.meta.get("orbit_sweep"),
+                    "round": True,
+                },
+            )
+        if not node_id:
+            raise ValueError(
+                "orbit_sweep_requires_node_id_or_round_or_until_limit:"
+                "params.node_id=… or params.round=true or params.until_limit=true"
+            )
+        sweep = orbit_sweep_node(
+            st0,
+            node_id,
+            params=base_params,
+            max_jump=knobs.get("max_jump"),
+            angle_step=knobs.get("angle_step"),
+            nn_floor=float(knobs.get("nn_floor") or 36.0),
+            min_angle_sep=float(knobs.get("min_angle_sep") or 35.0),
+            cand_cap=int(knobs.get("cand_cap") or 280),
+            protect_rigid=protect,
+            frozen_ids=frozen,
+            freeze_layers=knobs.get("freeze_layers"),
+            freeze_levels=knobs.get("freeze_levels"),
+            top_k=int(knobs.get("top_k") or 3),
+            y_min=knobs.get("y_min"),
+            y_max=knobs.get("y_max"),
+            objective=knobs.get("objective", "crossing"),
+        )
+        if not sweep.get("ok"):
+            fin = score_state(st0)
+            return _pack_result(
+                st0,
+                fin,
+                action=action_key,
+                recipe=None,
+                recipe_id=None,
+                preset=preset,
+                params=base_params,
+                tune=False,
+                tried=None,
+                local={"op": sweep, "note": f"orbit_sweep:{sweep.get('error')}"},
+            )
+        # Suggest-only keeps coords. Apply path sets params.pick (HTTP injects
+        # pick=1 on mode=apply) so the chosen candidate is in positions.
+        apply_pick = knobs.get("pick")
+        if apply_pick is not None:
+            op = apply_orbit_pick(st0, sweep, pick=int(apply_pick or 1))
+            st = normalize_origin(op.state, base_params).state
+            fin = score_state(st)
+            return _pack_result(
+                st,
+                fin,
+                action=action_key,
+                recipe=None,
+                recipe_id=None,
+                preset=preset,
+                params=base_params,
+                tune=False,
+                tried=None,
+                local={
+                    "op": op.params,
+                    "note": op.note,
+                    "sweep": sweep,
+                    "pick": int(apply_pick or 1),
+                },
+            )
+        fin = score_state(st0)
+        return _pack_result(
+            st0,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={
+                "op": {
+                    "node_id": sweep.get("node_id"),
+                    "crossings_before": sweep.get("crossings_before"),
+                    "candidates": sweep.get("candidates"),
+                    "sampled": sweep.get("sampled"),
+                    "improving_n": sweep.get("improving_n"),
+                },
+                "note": (
+                    f"orbit_sweep top3 improving={sweep.get('improving_n')} "
+                    f"sampled={sweep.get('sampled')}"
+                ),
+                "sweep": sweep,
+                "hint": sweep.get("hint"),
+            },
+        )
+
+    if action_key == "clear_edge_hits":
+        knobs = clear_edge_params_from_overrides(params)
+        preserve = bool(knobs.get("preserve_axis"))
+        op = clear_edge_hits(
+            st0,
+            base_params,
+            top_n=int(knobs.get("top_n") or 12),
+            thr=float(knobs.get("thr") or 40.0),
+            margin=float(knobs.get("margin") or 20.0),
+            max_moves=int(knobs.get("max_moves") or 24),
+            preserve_axis=preserve,
+            pitch=knobs.get("pitch"),
+            side=knobs.get("side"),
+            rounds=int(knobs.get("rounds") or (6 if preserve else 1)),
+            frozen_ids=knobs.get("frozen_ids") or set(),
+            max_eject_degree=int(knobs.get("max_eject_degree") or 5),
+        )
+        st = normalize_origin(op.state, base_params).state
+        from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings as _cx
+
+        x0 = _cx(st.positions, st.links)
+        st2, fix_meta = _ensure_zero_overlap(st, base_params)
+        x1 = _cx(st2.positions, st2.links)
+        if x1 <= x0 + 2:
+            st = st2
+        else:
+            fix_meta = {"ran": False, "reason": "overlap_fix_would_raise_crossings"}
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={
+                "op": op.params,
+                "note": op.note,
+                "meta": st.meta.get("clear_edge_hits"),
+                "ensure": fix_meta,
+            },
+        )
+
+    if action_key == "compact_bbox":
+        knobs = compact_bbox_params_from_overrides(params)
+        op = compact_bbox(
+            st0,
+            base_params,
+            frozen_ids=knobs.get("frozen_ids") or set(),
+            portal_ids=knobs.get("portal_ids") or [],
+            min_scale=float(knobs.get("min_scale") or 0.72),
+            step=float(knobs.get("step") or 0.03),
+            max_clearance_slack=int(knobs.get("max_clearance_slack") or 80),
+            outlier_only=bool(knobs.get("outlier_only", True)),
+        )
+        st = normalize_origin(op.state, base_params).state
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={"op": op.params, "note": op.note, "meta": st.meta.get("compact_bbox")},
+        )
+
+    if action_key == "pull_far_chains":
+        knobs = pull_far_chains_params_from_overrides(params)
+        op = pull_far_chains(
+            st0,
+            base_params,
+            frozen_ids=knobs.get("frozen_ids") or set(),
+            portal_ids=knobs.get("portal_ids") or [],
+            max_chains=int(knobs.get("max_chains") or 16),
+            min_tip_radius=float(knobs.get("min_tip_radius") or 1800.0),
+            scales=knobs.get("scales") or (0.92, 0.88, 0.84, 0.80, 0.75),
+            max_clearance_slack=int(knobs.get("max_clearance_slack") or 40),
+            pull_isolates=bool(knobs.get("pull_isolates", True)),
+        )
+        st = normalize_origin(op.state, base_params).state
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={
+                "op": op.params,
+                "note": op.note,
+                "meta": st.meta.get("pull_far_chains"),
+            },
+        )
+
+    if action_key == "level_bands":
+        knobs = level_bands_params_from_overrides(params)
+        op = apply_level_bands(st0, base_params, **knobs)
+        # Soft unstick only — hard crush fights band geometry and inflates crossings.
+        st = op.state
+        ov = overlapping_nodes(st)
+        fix_meta: dict[str, Any] = {"ran": False, "overlaps_before": len(ov)}
+        if ov:
+            st2 = fix_overlaps_local(st, base_params).state
+            st = st2
+            fix_meta = {
+                "ran": True,
+                "overlaps_before": len(ov),
+                "overlaps_after": len(overlapping_nodes(st)),
+                "mode": "local_only",
+            }
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={
+                "op": op.params,
+                "note": op.note,
+                "meta": st.meta.get("level_bands"),
+                "ensure": fix_meta,
+            },
+        )
+
+    if action_key == "polish_crossings":
+        knobs = press_params_from_overrides(params)
+        # Omit knobs so polish can auto-scale budgets on large E (MCP timeout).
+        # Internal polish still calls straighten / press_hot_edges /
+        # press_crossers / untangle.
+        op = polish_crossings(
+            st0,
+            base_params,
+            portal_ids=knobs.get("portal_ids"),
+            straighten=knobs.get("straighten"),
+            preserve_dual_eye=knobs.get("preserve_dual_eye"),
+            max_degree=int(knobs.get("max_degree") or 9),
+            untangle_rounds=knobs.get("untangle_rounds"),
+            top_n=knobs.get("top_n"),
+            max_moves=knobs.get("max_moves"),
+            max_sweeps=knobs.get("max_sweeps"),
+        )
+        st = normalize_origin(op.state, base_params).state
+        # Overlap crush must not erase crossing gains.
+        from netx_api.mcp_remote.topology.layout_metrics import count_edge_crossings as _cx
+
+        x0 = _cx(st.positions, st.links)
+        st2, fix_meta = _ensure_zero_overlap(st, base_params)
+        x1 = _cx(st2.positions, st2.links)
+        if x1 <= x0 + 2:
+            st = st2
+        else:
+            fix_meta = {
+                **fix_meta,
+                "reverted": True,
+                "reason": "overlap_fix_raised_crossings",
+            }
+        fin = score_state(st)
+        return _pack_result(
+            st,
+            fin,
+            action=action_key,
+            recipe=None,
+            recipe_id=None,
+            preset=preset,
+            params=base_params,
+            tune=False,
+            tried=None,
+            local={
+                "op": op.params,
+                "note": op.note,
+                "meta": st.meta.get(action_key),
+                "ensure": fix_meta,
+            },
+        )
+
+    # --- full layout recipe ---
+    recipe_id = resolve_recipe(recipe)
+    tried: list[dict[str, Any]] = []
+    best_st = None
+    best_fin: dict[str, Any] | None = None
+    best_params = base_params
+    combos = _tune_grid(base_params) if tune else [base_params]
+
+    for i, p in enumerate(combos):
+        st, _trace, fin = run_recipe(st0, recipe_id, p)
+        st, fix_meta = _ensure_zero_overlap(st, p)
+        fin = score_state(st)
+        row = {
+            "i": i,
+            "total": (fin.get("score") or {}).get("total"),
+            "overlaps": fin.get("footprint_overlap_pairs"),
+            "crossings": fin.get("edge_crossings"),
+            "nn_p50": fin.get("nn_p50"),
+            "util": fin.get("space_utilization"),
+            "fix": fix_meta,
+            "headline": ((fin.get("report") or {}).get("verdict") or {}).get("headline"),
+        }
+        tried.append(row)
+        if best_fin is None or _rank_key(fin) < _rank_key(best_fin):
+            best_st, best_fin, best_params = st, fin, p
+
+    assert best_st is not None and best_fin is not None
+    return _pack_result(
+        best_st,
+        best_fin,
+        action=action_key,
+        recipe=recipe if recipe in RECIPE_ALIASES else recipe_id,
+        recipe_id=recipe_id,
+        preset=preset,
+        params=best_params,
+        tune=bool(tune),
+        tried=tried if tune else None,
+        local={"auto_fix_overlaps": True},
+    )
+
+
+def list_layout_catalog() -> dict[str, Any]:
+    from netx_api.mcp_remote.topology import NETX_MCP_REV, __version__
+
+    return {
+        "version": __version__,
+        "rev": NETX_MCP_REV,
+        "actions": {
+            "layout": "全图配方（骨架）；块内 pack，结束时局部解叠",
+            "fix_overlaps": "只拉开当前重叠点（+1 跳邻居），不重排全图",
+            "straighten_channels": (
+                "拉直 deg≤2 通道（弦/横/纵）；仅全局交叉下降才接受该通道"
+                "（params: step/min_len）"
+            ),
+            "layout_dual_unit": (
+                "双门户单元美化（多走廊→平行 H/V 道；链→拉直）；"
+                "默认允许残留交叉；params.require_zero_cross=true 时要求为 0（params: unit_id）"
+            ),
+            "untangle": (
+                "贪心挪低度数点降交叉；默认 protect_rigid=portals "
+                "只冻共享门户，走廊/触手可动（all=全冻，off=不冻）；"
+                "可用 focus_ids / source_view_ids"
+            ),
+            "polish_crossings": (
+                "一键压交叉：straighten→press_hot_edges→press_crossers→"
+                "untangle(portals)；优先用 source_view_ids 冻门户 "
+                "（勿写临时 py）"
+            ),
+            "clear_edge_hits": (
+                "把贴在非关联边上的网元沿垂直方向弹开（直角偏好 H/V）；"
+                "门控：不增交叉、不增重叠（preserve_axis 亦不放宽交叉）。"
+                "眼 sink 须 portal_ids；params: top_n/thr/margin/max_moves/max_eject_degree"
+            ),
+            "compact_bbox": (
+                "眼图安全收 bbox：相对门户中心；默认 farthest-K（outlier_only）；"
+                "门控：交叉不升、overlaps=0、贴边不可大幅恶化。"
+                "params: portal_ids/min_scale/step"
+            ),
+            "pull_far_chains": (
+                "眼图安全收远场：deg≤2 走廊/孤立点/远叶相对门户中点缩放；"
+                "门控：交叉不升、overlaps=0、贴边松弛有限。"
+                "params: portal_ids/max_chains/min_tip_radius/scales"
+            ),
+            "align_reference": (
+                "【非主路径】仅同网同成员画布 A/B 调试："
+                "把参考画布几何映射到当前画布（共享 fabric_node_id）。"
+                "日常无范本、跨网人工图 → 禁止当交付手段。"
+                "params: reference_view_id|source_view_id, portal_ids, mode=similarity|adopt"
+            ),
+            "level_bands": (
+                "按 fabric level/layer 水平分层（external→core→agg→access）；"
+                "params: y0/band_gap/preserve_x/pitch；分层场景先于 polish"
+            ),
+            "orbit_sweep": (
+                "压交叉：以网元为圆心不定长扫角；"
+                "preview+node_id / apply+pick；"
+                "until_limit=true 单点循环到 stall（默认冻 portals、objective=crossing|total）；"
+                "round=true 一批 top_n 自动 pick#1（眼 sink 禁用）；"
+                "单点/round 默认 protect_rigid=off；bundle 默认开"
+            ),
+            "job_status": (
+                "轮询后台 job：params.job_id；返回 progress.phase/pct、elapsed_ms、"
+                "heartbeat_age_ms、stale（软警告，不改状态）"
+            ),
+            "job_cancel": (
+                "协作式取消后台 job：params.job_id；下一检查点退出；"
+                "若已 PATCH 则保留写入并回报 applied"
+            ),
+            "move_nodes": (
+                "成员迁移（双向）：params.fabric_node_ids 从 source_view_id→view_id；"
+                "默认 remove_from_source；对调两 view 即回迁；"
+                "copy_positions|park；mode=preview|apply"
+            ),
+        },
+        "recipes": {
+            "rings": "环+链花瓣；core_bar 时梁优先（多 CN 不塞进 AN 列）",
+            "corridor": "Tutte 走廊骨架，交叉常更少，易偏空——稳基线",
+            "compact": "走廊 + 分块 pack（禁止全局压扁）——稳基线",
+            "unstick": "corridor 后再强解重叠",
+        },
+        "presets": {
+            "loose": "偏疏、交叉友好",
+            "balanced": "默认折中",
+            "dense": "偏紧、抬 util",
+        },
+        "modes": {
+            "preview": "只算分+坐标，不写库",
+            "apply": "PATCH 到 view_id（有残留重叠则拒绝落笔）",
+        },
+        "workflow": (
+            "主路径：analyze(structure) → sinkTopologyDualUnits(max_units=1) → "
+            "suggestSinkHubs/move_nodes(park) → "
+            "orbit_sweep(until_limit crossing→total) → "
+            "clear_edge_hits → pull_far_chains → compact_bbox → 手拖。"
+            "默认无范本；align_reference 仅同网调试，禁止当跨网交付。"
+            "眼 sink 禁 polish/fix_overlaps/untangle/round。"
+        ),
+        "eye_polish_plateau": (
+            "算法到头：overlaps=0 + until_limit stall + "
+            "pull/compact/clear moved≈0 → 手拖或改初布；勿指望金标对齐。"
+        ),
+    }

netx_api/mcp_remote/topology/layout_topology_quality.py

@@ -0,0 +1,334 @@
+"""Mid-tier topology geometry QA: chain cohesion + min-ring integrity.
+
+- chain（直链成一体）: deg≤2 corridors should lay nearly collinear as one unit.
+- rings（最小环不被穿）: short chordless cycles should not be crossed by foreign edges.
+"""
+
+from __future__ import annotations
+
+import math
+from collections import defaultdict, deque
+from typing import Any
+
+from netx_api.mcp_remote.topology.layout_metrics import segments_properly_intersect
+
+# Turn from 180° beyond this → kink (degrees).
+CHAIN_KINK_DEG = 40.0
+# Min nodes on a corridor to score as a chain (hub + ≥2 hops, or ≥3 deg≤2).
+CHAIN_MIN_NODES = 3
+# Chordless cycles longer than this are ignored (metro min-rings are short).
+RING_MAX_LEN = 8
+
+
+def _adj(links: list[tuple[str, str]]) -> dict[str, set[str]]:
+    g: dict[str, set[str]] = defaultdict(set)
+    for a, b in links:
+        if a == b:
+            continue
+        g[a].add(b)
+        g[b].add(a)
+    return g
+
+
+def _edge_key(a: str, b: str) -> tuple[str, str]:
+    return (a, b) if a < b else (b, a)
+
+
+def _walk_path(start: str, nodes: set[str], adj: dict[str, set[str]]) -> list[str]:
+    ordered = [start]
+    seen = {start}
+    prev: str | None = None
+    cur = start
+    while True:
+        nbs = [v for v in adj.get(cur, ()) if v in nodes and v not in seen]
+        if not nbs:
+            break
+        # Prefer continuing along the unique unused neighbor.
+        nxt = nbs[0] if len(nbs) == 1 else sorted(nbs)[0]
+        if prev in nbs and len(nbs) > 1:
+            nbs = [v for v in nbs if v != prev]
+            nxt = nbs[0]
+        prev, cur = cur, nxt
+        ordered.append(cur)
+        seen.add(cur)
+    return ordered
+
+
+def extract_chain_paths(adj: dict[str, set[str]]) -> list[list[str]]:
+    """Maximal deg≤2 corridors, optionally extended by one hub endpoint each side."""
+    if not adj:
+        return []
+    deg = {u: len(vs) for u, vs in adj.items()}
+    low = {u for u, d in deg.items() if 0 < d <= 2}
+    if not low:
+        return []
+
+    seen: set[str] = set()
+    chains: list[list[str]] = []
+    for seed in sorted(low):
+        if seed in seen:
+            continue
+        q = deque([seed])
+        seen.add(seed)
+        comp: set[str] = set()
+        while q:
+            u = q.popleft()
+            comp.add(u)
+            for v in adj.get(u, ()):
+                if v in low and v not in seen:
+                    seen.add(v)
+                    q.append(v)
+
+        # Cycle of only low-deg nodes → not a chain corridor.
+        sub_deg = {u: sum(1 for v in adj.get(u, ()) if v in comp) for u in comp}
+        ends = [u for u in comp if sub_deg[u] <= 1]
+        if len(ends) == 0 and len(comp) >= 3:
+            continue
+
+        start = sorted(ends)[0] if ends else sorted(comp)[0]
+        ordered = _walk_path(start, comp, adj)
+        if len(ordered) < len(comp):
+            # branched low-deg blob: take longest path approximation via ends
+            best = ordered
+            for e in ends:
+                p = _walk_path(e, comp, adj)
+                if len(p) > len(best):
+                    best = p
+            ordered = best
+
+        # Extend with unique high-deg portals (hub ends).
+        def _portal(end: str) -> str | None:
+            outs = [v for v in adj.get(end, ()) if v not in comp]
+            return outs[0] if len(outs) == 1 else None
+
+        if ordered:
+            left = _portal(ordered[0])
+            right = _portal(ordered[-1])
+            if left:
+                ordered = [left] + ordered
+            if right and right != left:
+                ordered = ordered + [right]
+
+        if len(ordered) >= CHAIN_MIN_NODES:
+            chains.append(ordered)
+    return chains
+
+
+def _chain_straightness(path: list[str], pos: dict[str, tuple[float, float]]) -> float:
+    pts = [pos[n] for n in path if n in pos]
+    if len(pts) < 2:
+        return 1.0
+    chord = math.hypot(pts[-1][0] - pts[0][0], pts[-1][1] - pts[0][1])
+    plen = 0.0
+    for i in range(len(pts) - 1):
+        plen += math.hypot(pts[i + 1][0] - pts[i][0], pts[i + 1][1] - pts[i][1])
+    if plen < 1e-9:
+        return 1.0
+    return max(0.0, min(1.0, chord / plen))
+
+
+def _chain_kinks(path: list[str], pos: dict[str, tuple[float, float]]) -> int:
+    pts = [pos[n] for n in path if n in pos]
+    kinks = 0
+    for i in range(1, len(pts) - 1):
+        ax, ay = pts[i][0] - pts[i - 1][0], pts[i][1] - pts[i - 1][1]
+        bx, by = pts[i + 1][0] - pts[i][0], pts[i + 1][1] - pts[i][1]
+        la, lb = math.hypot(ax, ay), math.hypot(bx, by)
+        if la < 1e-9 or lb < 1e-9:
+            continue
+        cos = max(-1.0, min(1.0, (ax * bx + ay * by) / (la * lb)))
+        turn = abs(math.degrees(math.acos(cos)) - 0.0)  # 0 = straight continuation
+        # acos of dot gives angle between directions; 0° = collinear same way.
+        # kink if direction changes more than CHAIN_KINK_DEG from straight (0°).
+        if turn > CHAIN_KINK_DEG:
+            kinks += 1
+    return kinks
+
+
+def compute_chain_cohesion(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+) -> dict[str, Any]:
+    """直链成一体：corridor straightness / kink rate → score∈[0,1]."""
+    adj = _adj(links)
+    chains = extract_chain_paths(adj)
+    if not chains:
+        return {
+            "chain_count": 0,
+            "chain_nodes": 0,
+            "straightness_p50": None,
+            "kink_count": 0,
+            "kink_frac": 0.0,
+            "score": 1.0,
+            "tip": "无 deg≤2 走廊可评；不扣分。",
+        }
+
+    straight: list[float] = []
+    weights: list[float] = []
+    kink_total = 0
+    kink_slots = 0
+    node_union: set[str] = set()
+    for path in chains:
+        node_union.update(path)
+        s = _chain_straightness(path, pos)
+        straight.append(s)
+        weights.append(max(1, len(path) - 1))
+        k = _chain_kinks(path, pos)
+        kink_total += k
+        kink_slots += max(0, len([n for n in path if n in pos]) - 2)
+
+    # weighted mean straightness
+    wsum = sum(weights) or 1.0
+    mean_s = sum(s * w for s, w in zip(straight, weights)) / wsum
+    sorted_s = sorted(straight)
+    p50 = sorted_s[len(sorted_s) // 2]
+    kink_frac = (kink_total / kink_slots) if kink_slots else 0.0
+    # Mid-tier score: mostly straightness, penalize kinks.
+    score = max(0.0, min(1.0, 0.75 * mean_s + 0.25 * (1.0 - kink_frac)))
+
+    return {
+        "chain_count": len(chains),
+        "chain_nodes": len(node_union),
+        "straightness_p50": round(p50, 4),
+        "straightness_mean": round(mean_s, 4),
+        "kink_count": kink_total,
+        "kink_frac": round(kink_frac, 4),
+        "score": round(score, 4),
+        "tip": (
+            "deg≤2 走廊应近似共线成一体（chord/path≈1、少折角）；"
+            f"折角阈值 {CHAIN_KINK_DEG:.0f}°。"
+        ),
+    }
+
+
+def _is_chordless(cycle: list[str], adj: dict[str, set[str]]) -> bool:
+    n = len(cycle)
+    idx = {u: i for i, u in enumerate(cycle)}
+    for i, u in enumerate(cycle):
+        for v in adj.get(u, ()):
+            j = idx.get(v)
+            if j is None:
+                continue
+            dist = min((j - i) % n, (i - j) % n)
+            if dist > 1:
+                return False
+    return True
+
+
+def find_short_chordless_cycles(
+    adj: dict[str, set[str]],
+    *,
+    max_len: int = RING_MAX_LEN,
+    max_cycles: int = 120,
+) -> list[list[str]]:
+    """Enumerate chordless cycles of length 3..max_len (canonicalized).
+
+    Caps at ``max_cycles`` so giant star canvases stay interactive.
+    """
+    if not adj:
+        return []
+    cycles: list[list[str]] = []
+    seen: set[frozenset[str]] = set()
+    nodes = sorted(adj.keys())
+    for start in nodes:
+        if len(cycles) >= max_cycles:
+            break
+        # path grows; first neighbor ordered to cut duplicates
+        for first in sorted(adj.get(start, ())):
+            if first <= start:
+                continue
+            if len(cycles) >= max_cycles:
+                break
+            stack: list[tuple[str, list[str]]] = [(first, [start, first])]
+            while stack:
+                if len(cycles) >= max_cycles:
+                    break
+                u, path = stack.pop()
+                if len(path) > max_len:
+                    continue
+                for v in adj.get(u, ()):
+                    if v == start and len(path) >= 3:
+                        key = frozenset(path)
+                        if key in seen:
+                            continue
+                        if _is_chordless(path, adj):
+                            seen.add(key)
+                            cycles.append(list(path))
+                            if len(cycles) >= max_cycles:
+                                break
+                        continue
+                    if v in path or v < start:
+                        continue
+                    if len(path) + 1 > max_len:
+                        continue
+                    stack.append((v, path + [v]))
+    return cycles
+
+
+def compute_ring_integrity(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+) -> dict[str, Any]:
+    """最小环不被穿：foreign edges crossing a short cycle's boundary."""
+    adj = _adj(links)
+    cycles = find_short_chordless_cycles(adj, max_len=RING_MAX_LEN)
+    if not cycles:
+        return {
+            "ring_count": 0,
+            "rings_pierced": 0,
+            "pierce_crossings": 0,
+            "score": 1.0,
+            "tip": "无长度≤8 的弦无关短环；不扣分。",
+        }
+
+    pierced = 0
+    pierce_crossings = 0
+    for cyc in cycles:
+        cyc_edges = {
+            _edge_key(cyc[i], cyc[(i + 1) % len(cyc)]) for i in range(len(cyc))
+        }
+        hit = False
+        for a, b in links:
+            ek = _edge_key(a, b)
+            if ek in cyc_edges:
+                continue
+            if a not in pos or b not in pos:
+                continue
+            pa, pb = pos[a], pos[b]
+            for u, v in cyc_edges:
+                if u not in pos or v not in pos:
+                    continue
+                if segments_properly_intersect(pa, pb, pos[u], pos[v]):
+                    pierce_crossings += 1
+                    hit = True
+                    break
+            # continue scanning to count pierce_crossings
+        if hit:
+            pierced += 1
+
+    n = len(cycles)
+    # Soften: many pierce events on one ring still one pierced ring;
+    # also decay by pierce density.
+    pierce_frac = pierced / n
+    dens = pierce_crossings / max(1, n)
+    score = max(0.0, min(1.0, 1.0 - 0.7 * pierce_frac - 0.3 * min(1.0, dens / 3.0)))
+
+    return {
+        "ring_count": n,
+        "rings_pierced": pierced,
+        "pierce_crossings": pierce_crossings,
+        "score": round(score, 4),
+        "tip": (
+            f"弦无关短环（3–{RING_MAX_LEN}）边界不应被环外边穿越；"
+            "rings_pierced / pierce_crossings 越低越好。"
+        ),
+    }
+
+
+def compute_topology_quality(
+    pos: dict[str, tuple[float, float]],
+    links: list[tuple[str, str]],
+) -> dict[str, Any]:
+    chain = compute_chain_cohesion(pos, links)
+    rings = compute_ring_integrity(pos, links)
+    return {"chains": chain, "rings": rings}