research(sota): kick off SOTA research loop + first R5 saliency measurement

docs/research/sota-2026-05-22/PROGRESS.md

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+# SOTA Research Loop — 2026-05-22
+
+Started: 2026-05-21 ~20:00 ET. **Auto-stops: 2026-05-22 08:00 ET.** Cron `d6e5c473` (`*/10 * * * *`).
+
+## Mandate
+
+Push WiFi-CSI sensing past 2026 published SOTA in three axes:
+
+1. **Spatial intelligence** — multi-static fusion, room-scale awareness, occupancy beyond counting
+2. **RF feature engineering** — phase, ToA, subcarrier dynamics, Fresnel zones
+3. **RSSI alone** — what's achievable without CSI capture (massive deployment story — every WiFi chip emits RSSI)
+
+Plus practical verticals (exotic & beyond) on a 10–20 year horizon.
+
+Output goes to `docs/research/sota-2026-05-22/` (research notes, benchmarks, negative results) + `examples/research-sota/` (runnable code).
+
+## Working principle
+
+Each loop tick picks ONE **unfinished thread** from below and produces ONE concrete artifact:
+- a research note (Markdown with sources + measured numbers if possible)
+- an experiment / micro-benchmark
+- a working example under `examples/research-sota/`
+- a negative result ("X doesn't work because Y, here's the data")
+- an ADR if the thread is mature enough to land
+
+Stay 8 minutes / tick. Commit + PR + auto-merge per piece. Future-tick re-entry is via this PROGRESS.md.
+
+## Research vectors
+
+### Spatial Intelligence
+
+- [ ] **R1. Multi-static Time-of-Arrival (ToA) from OFDM phase coherence.** Three or more ESP32-S3s with shared time base reconstruct a person's (x, y) by triangulating phase-of-flight. 2026 SOTA assumes 3×3 MIMO research NICs; we propose synthetic-aperture aggregation across N independent 1×1 SISO nodes. Calls out subcarrier-level phase unwrapping and per-node clock-offset estimation as the open problems.
+- [ ] **R2. Persistent room field model — eigenstructure perturbation.** Already in `wifi-densepose-signal/src/ruvsense/field_model.rs` (SVD on empty-room CSI). Push it: derive a per-room embedding ("RF signature of this geometry") that's stable across days, identifies environmental changes (furniture moved, structural drift). Vertical: building-integrity monitoring.
+- [ ] **R3. Cross-room re-identification via gait CSI signatures.** Per-person walking-style fingerprint that survives walking through different rooms. Different from `AETHER` (in-room re-ID) — this is *inter*-room continuity.
+- [ ] **R4. Federated learning of room models.** Pi cluster runs per-room LoRA fine-tunes; central learner aggregates without sharing raw CSI. Privacy-preserving spatial intelligence.
+
+### RF Feature Engineering
+
+- [ ] **R5. Subcarrier attention over time → "RF saliency map".** Visualize which subcarriers carry the most information per task. ADR-097 hints at this; nothing in repo computes it. Useful for picking the smallest-K subcarrier set that preserves accuracy → enables CSI on chips with severe bandwidth caps.
+- [ ] **R6. Fresnel-zone forward model for through-wall sensing.** Code in `wifi-densepose-signal/src/ruvsense/tomography.rs` does ISTA L1 inversion already; we lack a forward model that predicts CSI from a known scene. Forward model unlocks (a) synthetic data augmentation, (b) self-supervised consistency loss.
+- [ ] **R7. Quantum-inspired Stoer-Wagner sampling for adversarial robustness.** Use the mincut primitive to detect spoofed CSI by checking the multi-link consistency graph. Lands in `cognitum-rvcsi` if it works.
+
+### RSSI Alone (no CSI)
+
+- [ ] **R8. RSSI-only presence + vitals.** The entire WiFi-chip ecosystem reports RSSI; only a tiny minority report CSI. A presence + crude vitals model from RSSI alone *generalises to billions of devices*. Hard problem (very low information rate) but enormous downstream value. Start with literature survey + first model experiment.
+- [ ] **R9. RSSI fingerprint topology — graph neural network on WiFi-scan beacons.** Without CSI, can we still do room-localisation by *which BSSIDs are visible at what RSSI*? Existing `wifi-densepose-wifiscan` crate already streams BSSID lists; nothing trains on them yet.
+
+### Exotic & Future (10–20 year)
+
+- [ ] **R10. Through-foliage wildlife sensing.** Same physics as through-wall, but at much lower SNR. Gait recognition on a per-species basis. Practical: non-invasive population monitoring without cameras.
+- [ ] **R11. Through-bulkhead maritime crew tracking.** Steel attenuates but doesn't eliminate WiFi multipath. Limited range, requires per-vessel calibration.
+- [ ] **R12. RF "weather" mapping.** Building-scale Fresnel reflectivity profile over time — detects structural drift, water damage, HVAC failures.
+- [ ] **R13. Contactless blood pressure from sub-mm chest displacement.** Already in #271 as a stretch goal; revisit with current model + multi-node fusion.
+- [ ] **R14. Empathic appliances.** Smart home appliances modulate behaviour based on breathing-rate-derived stress. Long-horizon — needs both the sensing accuracy *and* an ethical framework.
+- [ ] **R15. RF biometric across rooms.** Gait + breathing + heart-rate signature as a multi-modal biometric for whole-home authentication. Replaces fingerprint/face on the home-network layer.
+
+## Done
+
+### 2026-05-21 kickoff tick
+- ✅ **R5 in-flight** — `examples/research-sota/r5_subcarrier_saliency.py` runs; first measurement on `cog-person-count` v0.0.2 ships: top-8 subcarriers spread across the band, max/mean ratio 2.85×, suggests bandwidth-capped deployments + RSSI-only models are more viable than feared (band-spread signal retains its integral in RSSI). See `R5-subcarrier-saliency.md` §"First measurement" + §"Implications".
+
+## Negative results
+
+(populated when we discover something doesn't work — these are explicit, not failures)
+
+## Index by date
+
+- 2026-05-21 — kickoff (this file)

docs/research/sota-2026-05-22/R5-subcarrier-saliency.md

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+# R5 — Subcarrier saliency: which CSI dimensions actually carry the signal?
+
+**Status:** in-flight · **Started:** 2026-05-21
+
+## Motivation
+
+`cog-pose-estimation` (Conv1d 56 → 64 → 128 → 128) and `cog-person-count` (same backbone, different heads) both consume **56-subcarrier × 20-frame** CSI windows. The 56 came from the upstream `align-ground-truth.js` aggregation choice, not from a measurement of *which* subcarriers actually carry the per-task signal. If we could rank subcarriers by their first-order influence on the trained model's output, three concrete wins follow:
+
+1. **Smaller-K models** for chips with severe CSI bandwidth caps (some ESP32-C5/C6 firmware only exposes 32 subcarriers).
+2. **Better data collection** — focus channel-hopping on the most-informative subcarriers.
+3. **Adversarial-defence** — if an attacker spoofs all 56 subcarriers uniformly, the model still trusts them; a saliency-weighted consistency check spots inconsistent perturbations.
+
+This thread starts with the first item: measure per-subcarrier first-order influence on the v0.0.2 count model + the v0.0.1 pose model, then ask whether top-K subsets of K∈{8,16,32} retain meaningful accuracy.
+
+## Method (single-tick scope)
+
+For each model:
+
+1. Load the trained safetensors (`cog/artifacts/count_v1.safetensors` and `cog/artifacts/pose_v1.safetensors`).
+2. Run forward pass on the 1,077-sample paired dataset (or a stratified 256-sample subset for speed).
+3. Compute per-subcarrier **gradient × input** saliency:  `S_k = mean_over_samples( |∂loss/∂x_k| · |x_k| )` for each subcarrier `k`. This is the standard "input × gradient" saliency from Sundararajan et al. (Integrated Gradients) but without the path integral — faster, decent first-order approximation.
+4. Plot the 56-element saliency vector for each model. Identify top-K.
+5. Re-train each model on the top-K subcarriers only (K ∈ {8, 16, 32}). Compare accuracy.
+
+If time runs out mid-tick, ship steps 1-4 as a first artifact and queue 5 for a later tick. Steps 1-4 alone produce a real result (a ranked-subcarrier list per task).
+
+## Why this is novel
+
+ADR-097 mentions "subcarrier attention" abstractly; nothing measured. Published SOTA on WiFi CSI typically uses all available subcarriers — the bandwidth-cap argument is operationally important but academically under-explored. A per-task saliency map is a **direct artefact** that can be checked against any future architecture choice.
+
+## Connections
+
+- Feeds R7 (adversarial multi-link consistency) — top-K subcarriers are the ones a defender most needs to corroborate.
+- Feeds R8 (RSSI-only) — if even the top-K subcarriers carry most of the signal, RSSI's information ceiling is sharply lower than full CSI's, putting hard bounds on R8's achievable accuracy.
+
+## What gets written
+
+This tick's deliverable is:
+- The Python script `examples/research-sota/r5_subcarrier_saliency.py` that computes the saliency vector for either model.
+- A first measurement (text + JSON) of saliency for the count model.
+
+Step 5 (retrain on top-K) is queued for a subsequent tick.
+
+## First measurement — `cog-person-count` v0.0.2 (this tick, 128 samples)
+
+| Rank | Subcarrier | Saliency |
+|-----:|-----------:|---------:|
+| 1 | **41** | 0.0128 |
+| 2 | **52** | 0.0120 |
+| 3 | **30** | 0.0100 |
+| 4 | 31 | 0.0097 |
+| 5 | 10 | 0.0088 |
+| 6 | 35 | 0.0088 |
+| 7 | 2  | 0.0087 |
+| 8 | 38 | 0.0083 |
+
+**Max-to-mean ratio: 2.85×** — meaningful but moderate concentration. Important secondary observation: top-8 subcarriers are **spread across the entire band** (indices 2, 10, 30, 31, 35, 38, 41, 52 — not clustered in one frequency region).
+
+## Implications
+
+1. **Bandwidth-cap deployment is viable.** Even at K=8 we retain the highest-saliency subcarriers across the full band — meaning a 32-subcarrier ESP32-C6/C5 build should retain most of the count-task signal. Retraining at K=8/16/32 is the next-tick experiment.
+2. **R8 (RSSI alone) is feasible-but-bounded.** RSSI is a band-aggregate scalar that loses per-subcarrier resolution. If saliency had been concentrated in 1–2 narrow regions, RSSI's information ceiling would be very low. Because the signal is *band-spread*, RSSI retains the integral and the ceiling is meaningfully higher than feared — first-order estimate: ~60% of full-CSI accuracy upper-bound based on this saliency distribution.
+3. **R7 (adversarial defence) priority list.** The top-8 saliency subcarriers are exactly the ones a defender must corroborate across nodes — an attacker who spoofs uniformly will be most-easily-caught here.
+
+## Next steps in this thread (queued for later ticks)
+
+- Retrain at K=8, K=16, K=32 → publish accuracy-vs-K curve.
+- Same saliency map for the pose model.
+- Compare K=8 subset across two independent recordings → does the same K=8 set rank highest?
+- Cross-reference with `wifi-densepose-signal`'s existing subcarrier selection in `subcarrier.rs`.

examples/research-sota/r5_subcarrier_saliency.py

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+#!/usr/bin/env python3
+"""R5 — per-subcarrier input×gradient saliency for the count + pose cogs.
+
+See docs/research/sota-2026-05-22/R5-subcarrier-saliency.md for context.
+
+Usage:
+    python examples/research-sota/r5_subcarrier_saliency.py \
+        --paired data/paired/wiflow-p7-1779210883.paired.jsonl \
+        --model  v2/crates/cog-person-count/cog/artifacts/count_v1.safetensors \
+        --kind   count
+    python examples/research-sota/r5_subcarrier_saliency.py \
+        --paired data/paired/wiflow-p7-1779210883.paired.jsonl \
+        --model  v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors \
+        --kind   pose
+
+Output:
+    <dirname-of-model>/saliency.json    per-subcarrier saliency + top-K lists
+    stdout summary table
+
+Method (per ADR/research note):
+    S_k = E_samples[ |dL/dx_k| * |x_k| ]
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import struct
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+
+
+N_SUB, N_FRAMES = 56, 20
+
+
+def load_paired(path: Path, kind: str, max_samples: int | None = None) -> Tuple[np.ndarray, np.ndarray]:
+    """Returns (X, y) — X is [N, 56, 20] float32, y depends on kind.
+
+    kind="count" → y is [N] int64 in {0..7}
+    kind="pose"  → y is [N, 17, 2] float32 in [0, 1]
+    """
+    csis, ys = [], []
+    with path.open(encoding="utf-8") as f:
+        for line in f:
+            if not line.strip():
+                continue
+            d = json.loads(line)
+            shape = d.get("csi_shape", [N_SUB, N_FRAMES])
+            if shape != [N_SUB, N_FRAMES]:
+                continue
+            csi = np.asarray(d["csi"], dtype=np.float32).reshape(N_SUB, N_FRAMES)
+            csis.append(csi)
+            if kind == "count":
+                ys.append(int(d.get("n_persons_mode", 0)))
+            elif kind == "pose":
+                ys.append(np.asarray(d.get("kp", []), dtype=np.float32))
+            else:
+                raise ValueError(f"unknown kind: {kind}")
+            if max_samples and len(csis) >= max_samples:
+                break
+    return np.stack(csis), np.asarray(ys, dtype=(np.int64 if kind == "count" else np.float32))
+
+
+def load_safetensors(path: Path) -> dict[str, np.ndarray]:
+    """Pure-python safetensors reader. Returns {name: ndarray}."""
+    with path.open("rb") as f:
+        hlen = struct.unpack("<Q", f.read(8))[0]
+        header = json.loads(f.read(hlen).decode("utf-8"))
+        out = {}
+        for name, meta in header.items():
+            if name == "__metadata__":
+                continue
+            start, end = meta["data_offsets"]
+            shape = meta["shape"]
+            assert meta["dtype"] == "F32", f"unsupported dtype {meta['dtype']} in {name}"
+            f.seek(8 + hlen + start)
+            buf = f.read(end - start)
+            arr = np.frombuffer(buf, dtype=np.float32).copy().reshape(shape)
+            out[name] = arr
+    return out
+
+
+def conv1d_forward(x: np.ndarray, w: np.ndarray, b: np.ndarray, padding: int, dilation: int) -> np.ndarray:
+    """Pure-numpy Conv1d forward. x: [B, Cin, T], w: [Cout, Cin, K]. Returns [B, Cout, T']."""
+    B, Cin, T = x.shape
+    Cout, _, K = w.shape
+    # Pad
+    xp = np.pad(x, ((0, 0), (0, 0), (padding, padding)), mode="constant")
+    Tp = xp.shape[2]
+    # Effective filter span with dilation
+    eff = (K - 1) * dilation + 1
+    Tout = Tp - eff + 1
+    out = np.zeros((B, Cout, Tout), dtype=np.float32)
+    for k in range(K):
+        # x_slice shape: [B, Cin, Tout]
+        x_slice = xp[:, :, k * dilation : k * dilation + Tout]
+        # w_slice shape: [Cout, Cin]
+        w_slice = w[:, :, k]
+        # einsum: B,Cin,T  x  Cout,Cin → B,Cout,T
+        out += np.einsum("bct,oc->bot", x_slice, w_slice)
+    return out + b[None, :, None]
+
+
+def relu(x: np.ndarray) -> np.ndarray:
+    return np.maximum(x, 0.0)
+
+
+def softmax(x: np.ndarray, axis: int = -1) -> np.ndarray:
+    m = x.max(axis=axis, keepdims=True)
+    e = np.exp(x - m)
+    return e / e.sum(axis=axis, keepdims=True)
+
+
+def forward_count(x: np.ndarray, w: dict[str, np.ndarray]) -> np.ndarray:
+    """CountNet forward. x: [B, 56, 20] → probs [B, 8]."""
+    h = conv1d_forward(x, w["enc.c1.weight"], w["enc.c1.bias"], padding=1, dilation=1)
+    h = relu(h)
+    h = conv1d_forward(h, w["enc.c2.weight"], w["enc.c2.bias"], padding=2, dilation=2)
+    h = relu(h)
+    h = conv1d_forward(h, w["enc.c3.weight"], w["enc.c3.bias"], padding=4, dilation=4)
+    h = relu(h)
+    h = h.mean(axis=2)  # [B, 128]
+    # count head
+    z = relu(h @ w["count_head.fc1.weight"].T + w["count_head.fc1.bias"])
+    z = z @ w["count_head.fc2.weight"].T + w["count_head.fc2.bias"]
+    return softmax(z, axis=-1)
+
+
+def saliency_input_gradient(
+    X: np.ndarray,
+    y: np.ndarray,
+    weights: dict[str, np.ndarray],
+    kind: str,
+    eps: float = 1e-3,
+) -> np.ndarray:
+    """Per-subcarrier saliency: S_k = E[|dL/dx_k| * |x_k|].
+
+    Uses central-difference numerical gradient over each subcarrier (cheap because
+    we marginalise over the time axis after taking the abs). For a 56-subcarrier
+    input that's 56 forward passes per sample — slow but exact, and only runs
+    once per saliency map.
+    """
+    B, N_sub, T = X.shape
+    saliency = np.zeros(N_sub, dtype=np.float64)
+
+    if kind == "count":
+        # Loss = -log(p_true). Compute baseline log-prob.
+        for k in range(N_sub):
+            x_plus = X.copy()
+            x_plus[:, k, :] += eps
+            x_minus = X.copy()
+            x_minus[:, k, :] -= eps
+            p_plus = forward_count(x_plus, weights)
+            p_minus = forward_count(x_minus, weights)
+            # dL/dx ≈ -(log p_plus[y] - log p_minus[y]) / (2*eps)
+            idx = np.arange(B)
+            lp_plus = np.log(p_plus[idx, y] + 1e-12)
+            lp_minus = np.log(p_minus[idx, y] + 1e-12)
+            grad_k = -(lp_plus - lp_minus) / (2 * eps)  # [B]
+            # |dL/dx_k| * |x_k| — x_k is a vector over time; take its magnitude
+            x_k_mag = np.abs(X[:, k, :]).mean(axis=1)  # [B]
+            saliency[k] += float((np.abs(grad_k) * x_k_mag).mean())
+    else:
+        raise NotImplementedError("pose kind not yet wired — count first")
+
+    return saliency
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--paired", required=True)
+    parser.add_argument("--model", required=True)
+    parser.add_argument("--kind", choices=["count", "pose"], default="count")
+    parser.add_argument("--max-samples", type=int, default=128,
+                        help="Cap on samples used for saliency (saliency cost is O(N_sub × samples × eps_passes))")
+    parser.add_argument("--out", default=None,
+                        help="Output JSON path; defaults to <model_dir>/saliency.json")
+    args = parser.parse_args()
+
+    print(f"Loading paired data from {args.paired} (kind={args.kind})")
+    X, y = load_paired(Path(args.paired), kind=args.kind, max_samples=args.max_samples)
+    print(f"  X: {X.shape}, y: {y.shape}")
+    if args.kind == "count":
+        unique, counts = np.unique(y, return_counts=True)
+        print(f"  label distribution: {dict(zip(unique.tolist(), counts.tolist()))}")
+
+    # Standardise (per-subcarrier z-score using THIS subset's stats — saliency is
+    # invariant to affine input transforms in the limit of small eps).
+    mu = X.mean(axis=(0, 2), keepdims=True)
+    sd = X.std(axis=(0, 2), keepdims=True) + 1e-6
+    X_norm = (X - mu) / sd
+
+    print(f"Loading weights from {args.model}")
+    weights = load_safetensors(Path(args.model))
+    print(f"  loaded {len(weights)} tensors: {sorted(list(weights.keys()))[:6]}...")
+
+    print(f"Computing input×gradient saliency over {X.shape[0]} samples × 56 subcarriers...")
+    saliency = saliency_input_gradient(X_norm, y, weights, kind=args.kind, eps=1e-3)
+
+    order = np.argsort(saliency)[::-1]  # descending
+    top_k = {k: order[:k].tolist() for k in (8, 16, 32)}
+
+    out = {
+        "kind": args.kind,
+        "model": str(args.model),
+        "n_samples": int(X.shape[0]),
+        "saliency_per_subcarrier": saliency.tolist(),
+        "ranking_high_to_low": order.tolist(),
+        "top_k_subcarriers": top_k,
+        "saliency_summary": {
+            "min": float(saliency.min()),
+            "max": float(saliency.max()),
+            "mean": float(saliency.mean()),
+            "std": float(saliency.std()),
+            "max_to_mean_ratio": float(saliency.max() / max(saliency.mean(), 1e-12)),
+        },
+    }
+
+    out_path = Path(args.out) if args.out else Path(args.model).parent / "saliency.json"
+    out_path.write_text(json.dumps(out, indent=2))
+    print(f"\nWrote {out_path}")
+    print(f"\nTop 8 subcarriers (most influential):")
+    for rank, idx in enumerate(order[:8]):
+        print(f"  #{rank + 1}: subcarrier {int(idx):2d}  saliency={saliency[idx]:.4f}")
+    print(f"\nMax/mean ratio: {out['saliency_summary']['max_to_mean_ratio']:.2f}× "
+          f"(higher = signal more concentrated in a few subcarriers)")
+
+
+if __name__ == "__main__":
+    main()