fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified)

wiflow_std: min_feature_width (default 15) replaces the keypoints->stride
coupling — for_keypoints(17) now provably builds the trained [2,2,2,2]
graph and pools 15->17, matching the validated Python protocol (pinned by
tests); param_count() total on invalid configs; random_mask returns Result
and rejects non-finite/out-of-range ratios; trainer checkpoints switched
to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11).

ieee80211bf: SBP proxy now re-triggers instances and relays reports via
Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via
their existing path); missed_instances reset on success = consecutive
semantics; SessionTable gains a guarded SBP entry point + unknown-id drop
counter; initiator-role sessions reject inbound setup/SBP requests
(RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp
outside Idle return InvalidStateForCommand; SBP validation unified
through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping.
events.rs split out to honor the 500-line cap.

calibration/cli: enrollment geometry now actually reaches trained banks —
both production call sites attach .with_geometry; --geometry flag on
train-room and POST /enroll/geometry + train-body geometry on
calibrate-serve give production a recording surface; geometry-free banks
log the ADR-152 §2.1.2 note.

benchmarks: corruption masks committed as ground truth (unregenerable
after in-place cleaning; verified bit-identical regeneration from the
pristine copy) + generate_corruption_masks.py producer; _bench_common.py
dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20
re-verified equal to the last digit); remote scripts get the mmap patch;
tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer
executes (and overwrote) the export — artifact restored byte-exact.

Workspace: 2,963 tests passed, 0 failed; Python proof PASS.

Co-Authored-By: claude-flow <ruv@ruv.net>

benchmarks/wiflow-std/eval_ort_accuracy.py

@@ -17,41 +17,17 @@ import argparse
 import json
 import os
 import sys
-import time
-
-import numpy as np
-import torch
 
 HERE = os.path.dirname(os.path.abspath(__file__))
-RESULTS = os.path.join(HERE, "results")
 sys.path.insert(0, HERE)
 
+from _bench_common import RESULTS, evaluate  # noqa: E402
 from quantize_bench import build_test_subset  # noqa: E402  (sets up upstream imports)
 
-sys.path.insert(0, os.path.join(HERE, "upstream"))
-from utils.metrics import calculate_mpjpe, calculate_pck  # noqa: E402
-
 
 def evaluate_ort(sess, loader, label):
-    inp = sess.get_inputs()[0].name
-    totals = {0.2: 0.0, 0.5: 0.0}
-    total_mpe, n = 0.0, 0
-    t0 = time.time()
-    for batch_idx, (bx, by) in enumerate(loader):
-        out = torch.from_numpy(sess.run(None, {inp: bx.numpy()})[0])
-        pck = calculate_pck(out, by, thresholds=[0.2, 0.5])
-        mpe = calculate_mpjpe(out, by)
-        bs = by.size(0)
-        total_mpe += mpe * bs
-        for t in totals:
-            totals[t] += pck[t] * bs
-        n += bs
-        if batch_idx % 50 == 0:
-            print(f"  [{label}] batch {batch_idx}: n={n} "
-                  f"pck20={totals[0.2]/n:.4f} mpjpe={total_mpe/n:.4f} "
-                  f"({time.time()-t0:.0f}s)", flush=True)
-    return {"samples": n, "pck@20": totals[0.2] / n, "pck@50": totals[0.5] / n,
-            "mpjpe": total_mpe / n, "wall_seconds": time.time() - t0}
+    """ORT-session evaluation via the canonical _bench_common.evaluate loop."""
+    return evaluate(sess, loader, label=label)
 
 
 def main():