feat(adr-185): P4 benchmarks, examples, and README extras for the SOTA wheels

ADR-185 §4.2 pytest-benchmark micro-benchmarks + runnable examples +
README extras table for the aether/meridian/mat bindings.

- python/bench/test_bench_{aether,meridian,mat}.py — follow the existing
  test_bench_vitals.py pattern (skipped by default; --benchmark-only).
- python/examples/{reid_from_csi,cross_room_calibrate,mat_triage}.py —
  typed, runnable, mypy --strict clean.
- python/README.md — SOTA extras table + example links.

Measured on a RELEASE wheel (maturin develop --release --features sota),
reference machine per ADR-117 §10:
  AETHER embed()          mean ~150 us/window   (target <2 ms)   PASS
    batch scaling 1/8/64: 140 / 1091 / 8509 us  (linear, no O(n^2)) PASS
  MERIDIAN normalize()    mean ~2.2 us/frame     (target <200 us) PASS
  MERIDIAN encode()       mean ~6.9 us           (target <200 us) PASS
  MAT ingest+scan_once()  mean ~40 ms/256-frame  (< 500 ms interval) PASS

Acceptance self-verification (ADR-185 §6), all run just now:
  §6.1 default wheel 279 KB (<=5 MB); build_features has no p6-* feature  PASS
  §6.2 pytest tests/test_aether.py    9/9   PASS
  §6.3 pytest tests/test_meridian.py  13/13 PASS
  §6.4 pytest tests/test_mat.py       7/7   PASS
  §6.5 benchmarks meet all targets (above)                                PASS
  §6.6 parity harness: 3/3 SHA golden gates green (cargo test --features
       sota, 6/6); CI *wiring* as a release gate is out of python/ scope  PARTIAL
  §6.7 SOTA accuracy bars on labeled fixtures: NOT met (no labeled
       fixtures / trained models available; parity proves path-equality,
       not accuracy)                                                       OPEN
  §6.8 .pyi stubs present for all three; mypy --strict on the 3 examples   PASS
  §6.9 base wheel `import wifi_densepose.{aether,meridian,mat}` raises a
       clear ImportError naming the extra                                  PASS
  No regression: 76 pre-existing tests pass on the default wheel.

Status NOT flipped to Accepted: §6.7 (accuracy bars) is unmet, §6.6 CI
wiring is pending, and the per-extra wheel-size hoists (sensing-server /
train / mat leaf crates) remain follow-ups. docs/adr/ is owned by another
agent this session, so the ADR ledger edit is deferred to that owner.

python/examples/cross_room_calibrate.py

@@ -0,0 +1,45 @@
+"""MERIDIAN cross-room calibration (ADR-185 P2, `[meridian]` extra).
+
+Hardware-invariant CSI normalization, AP-geometry encoding, and few-shot
+rapid adaptation — the tch-free domain-generalization path.
+
+    pip install wifi-densepose[meridian]
+    python examples/cross_room_calibrate.py
+"""
+
+from __future__ import annotations
+
+import math
+
+from wifi_densepose.meridian import (
+    GeometryEncoder,
+    HardwareNormalizer,
+    HardwareType,
+    MeridianGeometryConfig,
+    RapidAdaptation,
+)
+
+
+def main() -> None:
+    # 1. Normalize a 64-subcarrier ESP32 frame to the canonical 56-tone grid.
+    norm = HardwareNormalizer()
+    amp = [10.0 + 0.05 * k for k in range(64)]
+    phase = [0.01 * k for k in range(64)]
+    frame = norm.normalize(amp, phase, HardwareType.detect(64))
+    print(f"canonical subcarriers: {len(frame.amplitude)} (hw={frame.hardware_type})")
+
+    # 2. Encode AP positions into a permutation-invariant geometry embedding.
+    enc = GeometryEncoder(MeridianGeometryConfig())
+    geometry = enc.encode([[0.0, 0.0, 2.5], [5.0, 0.0, 2.5], [0.0, 4.0, 2.5]])
+    print(f"geometry embedding dim: {len(geometry)}")
+
+    # 3. Few-shot rapid adaptation over a handful of unlabeled frames.
+    ra = RapidAdaptation(min_calibration_frames=10, lora_rank=4)
+    for i in range(12):
+        ra.push_frame([math.sin(0.1 * i + 0.05 * d) for d in range(16)])
+    result = ra.adapt()
+    print(f"adapted over {result.frames_used} frames, final_loss={result.final_loss:.4f}")
+
+
+if __name__ == "__main__":
+    main()

python/examples/mat_triage.py

@@ -0,0 +1,49 @@
+"""MAT disaster-survivor triage from CSI (ADR-185 P3, `[mat]` extra).
+
+Ingest a CSI stream, run one detection cycle, and list detected survivors
+by START triage class.
+
+    pip install wifi-densepose[mat]
+    python examples/mat_triage.py
+
+Note: the stream here is synthetic (breathing-modulated) — it demonstrates
+the API and pipeline, not validated detection accuracy on real rubble.
+"""
+
+from __future__ import annotations
+
+import math
+from collections.abc import Iterator
+
+from wifi_densepose.mat import DisasterConfig, DisasterResponse, DisasterType, ScanZone
+
+
+def breathing_stream(
+    frames: int = 256, subc: int = 56, fs: float = 20.0
+) -> Iterator[tuple[list[float], list[float]]]:
+    for t in range(frames):
+        tt = t / fs
+        breath = 2.0 * math.sin(2 * math.pi * 0.3 * tt)
+        amp = [10.0 + 0.05 * k + breath for k in range(subc)]
+        phase = [0.01 * k + 0.1 * math.sin(2 * math.pi * 0.3 * tt) for k in range(subc)]
+        yield amp, phase
+
+
+def main() -> None:
+    cfg = DisasterConfig(DisasterType.Earthquake, sensitivity=0.9, confidence_threshold=0.1)
+    resp = DisasterResponse(cfg)
+    resp.initialize_event(0.0, 0.0, "Collapsed Building A")
+    resp.add_zone(ScanZone.rectangle("North Wing", 0.0, 0.0, 50.0, 30.0))
+
+    for amp, phase in breathing_stream():
+        resp.push_csi_data(amp, phase)
+    resp.scan_once()
+
+    survivors = resp.survivors()
+    print(f"detected {len(survivors)} survivor(s)")
+    for s in survivors:
+        print(f"  {s.id[:8]}  triage={s.triage_status}  confidence={s.confidence:.3f}")
+
+
+if __name__ == "__main__":
+    main()

python/examples/reid_from_csi.py

@@ -0,0 +1,39 @@
+"""AETHER re-identification from CSI (ADR-185 P1, `[aether]` extra).
+
+Compute 128-dim contrastive embeddings for CSI windows and score them by
+cosine similarity — the primitive behind room fingerprinting and person
+re-identification.
+
+    pip install wifi-densepose[aether]
+    python examples/reid_from_csi.py
+"""
+
+from __future__ import annotations
+
+import math
+
+from wifi_densepose.aether import AetherConfig, EmbeddingExtractor, cosine_similarity
+
+
+def make_window(phase_shift: float, frames: int = 8, subc: int = 56) -> list[list[float]]:
+    """A synthetic CSI window; `phase_shift` stands in for a different scene."""
+    return [
+        [math.sin(0.1 * t + 0.03 * k + phase_shift) for k in range(subc)]
+        for t in range(frames)
+    ]
+
+
+def main() -> None:
+    ext = EmbeddingExtractor(n_subcarriers=56, config=AetherConfig())
+
+    same_a = ext.embed(make_window(0.0))
+    same_b = ext.embed(make_window(0.0))  # same scene
+    other = ext.embed(make_window(1.5))   # different scene
+
+    print(f"embedding dim:        {len(same_a)}")
+    print(f"same-scene similarity:  {cosine_similarity(same_a, same_b):.4f}")
+    print(f"cross-scene similarity: {cosine_similarity(same_a, other):.4f}")
+
+
+if __name__ == "__main__":
+    main()