docs: add RuvSense persistent field model, exotic tiers, and appliance categories

Expands the RuvSense architecture from pose estimation to spatial
intelligence platform with persistent electromagnetic world model.

Research (Part II added):
- 7 exotic capability tiers: field normal modes, RF tomography,
  intention lead signals, longitudinal biomechanics drift,
  cross-room continuity, invisible interaction layer, adversarial detection
- Signals-not-diagnoses framework with 3 monitoring levels
- 5 appliance product categories: Invisible Guardian, Spatial Digital Twin,
  Collective Behavior Engine, RF Interaction Surface, Pre-Incident Drift Monitor
- Regulatory classification (consumer wellness → clinical decision support)
- Extended acceptance tests: 7-day autonomous, 30-day appliance validation

ADR-030 (new):
- Persistent field model architecture with room eigenstructure
- Longitudinal drift detection via Welford statistics + HNSW memory
- All 5 ruvector crates mapped across 7 exotic tiers
- GOAP implementation priority: field modes → drift → tomography → intent
- Invisible Guardian recommended as first hardware SKU vertical

DDD model (extended):
- 3 new bounded contexts: Field Model, Longitudinal Monitoring, Spatial Identity
- Full aggregate roots, value objects, domain events for each context
- Extended context map showing all 6 bounded contexts
- Repository interfaces for field baselines, personal baselines, transitions
- Invariants enforcing signals-not-diagnoses boundary

https://claude.ai/code/session_01QTX772SDsGVSPnaphoNgNY

claude.md

@@ -21,33 +21,77 @@ All 5 ruvector crates integrated in workspace:
 - `ruvector-attention` → `model.rs` (apply_spatial_attention) + `bvp.rs`
 
 ### Architecture Decisions
-All ADRs in `docs/adr/` (ADR-001 through ADR-017). Key ones:
+28 ADRs in `docs/adr/` (ADR-001 through ADR-028). Key ones:
 - ADR-014: SOTA signal processing (Accepted)
 - ADR-015: MM-Fi + Wi-Pose training datasets (Accepted)
 - ADR-016: RuVector training pipeline integration (Accepted — complete)
 - ADR-017: RuVector signal + MAT integration (Proposed — next target)
+- ADR-024: Contrastive CSI embedding / AETHER (Accepted)
+- ADR-027: Cross-environment domain generalization / MERIDIAN (Accepted)
+- ADR-028: ESP32 capability audit + witness verification (Accepted)
 
 ### Build & Test Commands (this repo)
 ```bash
-# Rust — check training crate (no GPU needed)
+# Rust — full workspace tests (1,031 tests, ~2 min)
 cd rust-port/wifi-densepose-rs
+cargo test --workspace --no-default-features
+
+# Rust — single crate check (no GPU needed)
 cargo check -p wifi-densepose-train --no-default-features
 
-# Rust — run all tests
-cargo test -p wifi-densepose-train --no-default-features
-
-# Rust — full workspace check
-cargo check --workspace --no-default-features
-
-# Python — proof verification
+# Python — deterministic proof verification (SHA-256)
 python v1/data/proof/verify.py
 
 # Python — test suite
 cd v1 && python -m pytest tests/ -x -q
 ```
 
+### Validation & Witness Verification (ADR-028)
+
+**After any significant code change, run the full validation:**
+
+```bash
+# 1. Rust tests — must be 1,031+ passed, 0 failed
+cd rust-port/wifi-densepose-rs
+cargo test --workspace --no-default-features
+
+# 2. Python proof — must print VERDICT: PASS
+cd ../..
+python v1/data/proof/verify.py
+
+# 3. Generate witness bundle (includes both above + firmware hashes)
+bash scripts/generate-witness-bundle.sh
+
+# 4. Self-verify the bundle — must be 7/7 PASS
+cd dist/witness-bundle-ADR028-*/
+bash VERIFY.sh
+```
+
+**If the Python proof hash changes** (e.g., numpy/scipy version update):
+```bash
+# Regenerate the expected hash, then verify it passes
+python v1/data/proof/verify.py --generate-hash
+python v1/data/proof/verify.py
+```
+
+**Witness bundle contents** (`dist/witness-bundle-ADR028-<sha>.tar.gz`):
+- `WITNESS-LOG-028.md` — 33-row attestation matrix with evidence per capability
+- `ADR-028-esp32-capability-audit.md` — Full audit findings
+- `proof/verify.py` + `expected_features.sha256` — Deterministic pipeline proof
+- `test-results/rust-workspace-tests.log` — Full cargo test output
+- `firmware-manifest/source-hashes.txt` — SHA-256 of all 7 ESP32 firmware files
+- `crate-manifest/versions.txt` — All 15 crates with versions
+- `VERIFY.sh` — One-command self-verification for recipients
+
+**Key proof artifacts:**
+- `v1/data/proof/verify.py` — Trust Kill Switch: feeds reference signal through production pipeline, hashes output
+- `v1/data/proof/expected_features.sha256` — Published expected hash
+- `v1/data/proof/sample_csi_data.json` — 1,000 synthetic CSI frames (seed=42)
+- `docs/WITNESS-LOG-028.md` — 11-step reproducible verification procedure
+- `docs/adr/ADR-028-esp32-capability-audit.md` — Complete audit record
+
 ### Branch
-All development on: `claude/validate-code-quality-WNrNw`
+Default branch: `main`
 
 ---
 
@@ -93,14 +137,16 @@ All development on: `claude/validate-code-quality-WNrNw`
 
 Before merging any PR, verify each item applies and is addressed:
 
-1. **Tests pass** — `cargo test` (Rust) and `python -m pytest` (Python) green
-2. **README.md** — Update platform tables, crate descriptions, hardware tables, feature summaries if scope changed
-3. **CHANGELOG.md** — Add entry under `[Unreleased]` with what was added/fixed/changed
-4. **User guide** (`docs/user-guide.md`) — Update if new data sources, CLI flags, or setup steps were added
-5. **ADR index** — Update ADR count in README docs table if a new ADR was created
-6. **Docker Hub image** — Only rebuild if Dockerfile, dependencies, or runtime behavior changed (not needed for platform-gated code that doesn't affect the Linux container)
-7. **Crate publishing** — Only needed if a crate is published to crates.io and its public API changed (workspace-internal crates don't need publishing)
-8. **`.gitignore`** — Add any new build artifacts or binaries
+1. **Rust tests pass** — `cargo test --workspace --no-default-features` (1,031+ passed, 0 failed)
+2. **Python proof passes** — `python v1/data/proof/verify.py` (VERDICT: PASS)
+3. **README.md** — Update platform tables, crate descriptions, hardware tables, feature summaries if scope changed
+4. **CHANGELOG.md** — Add entry under `[Unreleased]` with what was added/fixed/changed
+5. **User guide** (`docs/user-guide.md`) — Update if new data sources, CLI flags, or setup steps were added
+6. **ADR index** — Update ADR count in README docs table if a new ADR was created
+7. **Witness bundle** — Regenerate if tests or proof hash changed: `bash scripts/generate-witness-bundle.sh`
+8. **Docker Hub image** — Only rebuild if Dockerfile, dependencies, or runtime behavior changed
+9. **Crate publishing** — Only needed if a crate is published to crates.io and its public API changed
+10. **`.gitignore`** — Add any new build artifacts or binaries
 
 ## Build & Test
 

docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md

@@ -0,0 +1,400 @@
+# ADR-029: Project RuvSense -- Sensing-First RF Mode for Multistatic WiFi DensePose
+
+| Field | Value |
+|-------|-------|
+| **Status** | Proposed |
+| **Date** | 2026-03-02 |
+| **Deciders** | ruv |
+| **Codename** | **RuvSense** -- RuVector-Enhanced Sensing for Multistatic Fidelity |
+| **Relates to** | ADR-012 (ESP32 Mesh), ADR-014 (SOTA Signal Processing), ADR-016 (RuVector Training), ADR-017 (RuVector Signal+MAT), ADR-018 (ESP32 Implementation), ADR-024 (AETHER Embeddings), ADR-026 (Survivor Track Lifecycle), ADR-027 (MERIDIAN Generalization) |
+
+---
+
+## 1. Context
+
+### 1.1 The Fidelity Gap
+
+Current WiFi-DensePose achieves functional pose estimation from a single ESP32 AP, but three fidelity metrics prevent production deployment:
+
+| Metric | Current (Single ESP32) | Required (Production) | Root Cause |
+|--------|------------------------|----------------------|------------|
+| Torso keypoint jitter | ~15cm RMS | <3cm RMS | Single viewpoint, 20 MHz bandwidth, no temporal smoothing |
+| Multi-person separation | Fails >2 people, frequent ID swaps | 4+ people, zero swaps over 10 min | Underdetermined with 1 TX-RX link; no person-specific features |
+| Small motion sensitivity | Gross movement only | Breathing at 3m, heartbeat at 1.5m | Insufficient phase sensitivity at 2.4 GHz; noise floor too high |
+| Update rate | ~10 Hz effective | 20 Hz | Single-channel serial CSI collection |
+| Temporal stability | Drifts within hours | Stable over days | No coherence gating; model absorbs environmental drift |
+
+### 1.2 The Insight: Sensing-First RF Mode on Existing Silicon
+
+You do not need to invent a new WiFi standard. The winning move is a **sensing-first RF mode** that rides on existing silicon (ESP32-S3), existing bands (2.4/5 GHz), and existing regulations (802.11n NDP frames). The fidelity improvement comes from three physical levers:
+
+1. **Bandwidth**: Channel-hopping across 2.4 GHz channels 1/6/11 triples effective bandwidth from 20 MHz to 60 MHz, 3x multipath separation
+2. **Carrier frequency**: Dual-band sensing (2.4 + 5 GHz) doubles phase sensitivity to small motion
+3. **Viewpoints**: Multistatic ESP32 mesh (4 nodes = 12 TX-RX links) provides 360-degree geometric diversity
+
+### 1.3 Acceptance Test
+
+**Two people in a room, 20 Hz update rate, stable tracks for 10 minutes with no identity swaps and low jitter in the torso keypoints.**
+
+Quantified:
+- Torso keypoint jitter < 30mm RMS (hips, shoulders, spine)
+- Zero identity swaps over 600 seconds (12,000 frames)
+- 20 Hz output rate (50 ms cycle time)
+- Breathing SNR > 10dB at 3m (validates small-motion sensitivity)
+
+---
+
+## 2. Decision
+
+### 2.1 Architecture Overview
+
+Implement RuvSense as a new bounded context within `wifi-densepose-signal`, consisting of 6 modules:
+
+```
+wifi-densepose-signal/src/ruvsense/
+├── mod.rs              // Module exports, RuvSense pipeline orchestrator
+├── multiband.rs        // Multi-band CSI frame fusion (§2.2)
+├── phase_align.rs      // Cross-channel phase alignment (§2.3)
+├── multistatic.rs      // Multi-node viewpoint fusion (§2.4)
+├── coherence.rs        // Coherence metric computation (§2.5)
+├── coherence_gate.rs   // Gated update policy (§2.6)
+└── pose_tracker.rs     // 17-keypoint Kalman tracker with re-ID (§2.7)
+```
+
+### 2.2 Channel-Hopping Firmware (ESP32-S3)
+
+Modify the ESP32 firmware (`firmware/esp32-csi-node/main/csi_collector.c`) to cycle through non-overlapping channels at configurable dwell times:
+
+```c
+// Channel hop table (populated from NVS at boot)
+static uint8_t s_hop_channels[6] = {1, 6, 11, 36, 40, 44};
+static uint8_t s_hop_count = 3;   // default: 2.4 GHz only
+static uint32_t s_dwell_ms = 50;  // 50ms per channel
+```
+
+At 100 Hz raw CSI rate with 50 ms dwell across 3 channels, each channel yields ~33 frames/second. The existing ADR-018 binary frame format already carries `channel_freq_mhz` at offset 8, so no wire format change is needed.
+
+**NDP frame injection:** `esp_wifi_80211_tx()` injects deterministic Null Data Packet frames (preamble-only, no payload, ~24 us airtime) at GPIO-triggered intervals. This is sensing-first: the primary RF emission purpose is CSI measurement, not data communication.
+
+### 2.3 Multi-Band Frame Fusion
+
+Aggregate per-channel CSI frames into a wideband virtual snapshot:
+
+```rust
+/// Fused multi-band CSI from one node at one time slot.
+pub struct MultiBandCsiFrame {
+    pub node_id: u8,
+    pub timestamp_us: u64,
+    /// One canonical-56 row per channel, ordered by center frequency.
+    pub channel_frames: Vec<CanonicalCsiFrame>,
+    /// Center frequencies (MHz) for each channel row.
+    pub frequencies_mhz: Vec<u32>,
+    /// Cross-channel coherence score (0.0-1.0).
+    pub coherence: f32,
+}
+```
+
+Cross-channel phase alignment uses `ruvector-solver::NeumannSolver` to solve for the channel-dependent phase rotation introduced by the ESP32 local oscillator during channel hops. The system:
+
+```
+[Φ₁, Φ₆, Φ₁₁] = [Φ_body + δ₁, Φ_body + δ₆, Φ_body + δ₁₁]
+```
+
+NeumannSolver fits the `δ` offsets from the static subcarrier components (which should have zero body-caused phase shift), then removes them.
+
+### 2.4 Multistatic Viewpoint Fusion
+
+With N ESP32 nodes, collect N `MultiBandCsiFrame` per time slot and fuse with geometric diversity:
+
+**TDMA Sensing Schedule (4 nodes):**
+
+| Slot | TX | RX₁ | RX₂ | RX₃ | Duration |
+|------|-----|-----|-----|-----|----------|
+| 0 | Node A | B | C | D | 4 ms |
+| 1 | Node B | A | C | D | 4 ms |
+| 2 | Node C | A | B | D | 4 ms |
+| 3 | Node D | A | B | C | 4 ms |
+| 4 | -- | Processing + fusion | | | 30 ms |
+| **Total** | | | | | **50 ms = 20 Hz** |
+
+Synchronization: GPIO pulse from aggregator node at cycle start. Clock drift at ±10ppm over 50 ms is ~0.5 us, well within the 1 ms guard interval.
+
+**Cross-node fusion** uses `ruvector-attn-mincut::attn_mincut` where time-frequency cells from different nodes attend to each other. Cells showing correlated motion energy across nodes (body reflection) are amplified; cells with single-node energy (local multipath artifact) are suppressed.
+
+**Multi-person separation** via `ruvector-mincut::DynamicMinCut`:
+
+1. Build cross-link temporal correlation graph (nodes = TX-RX links, edges = correlation coefficient)
+2. `DynamicMinCut` partitions into K clusters (one per detected person)
+3. Attention fusion (§5.3 of research doc) runs independently per cluster
+
+### 2.5 Coherence Metric
+
+Per-link coherence quantifies consistency with recent history:
+
+```rust
+pub fn coherence_score(
+    current: &[f32],
+    reference: &[f32],
+    variance: &[f32],
+) -> f32 {
+    current.iter().zip(reference.iter()).zip(variance.iter())
+        .map(|((&c, &r), &v)| {
+            let z = (c - r).abs() / v.sqrt().max(1e-6);
+            let weight = 1.0 / (v + 1e-6);
+            ((-0.5 * z * z).exp(), weight)
+        })
+        .fold((0.0, 0.0), |(sc, sw), (c, w)| (sc + c * w, sw + w))
+        .pipe(|(sc, sw)| sc / sw)
+}
+```
+
+The static/dynamic decomposition uses `ruvector-solver` to separate environmental drift (slow, global) from body motion (fast, subcarrier-specific).
+
+### 2.6 Coherence-Gated Update Policy
+
+```rust
+pub enum GateDecision {
+    /// Coherence > 0.85: Full Kalman measurement update
+    Accept(Pose),
+    /// 0.5 < coherence < 0.85: Kalman predict only (3x inflated noise)
+    PredictOnly,
+    /// Coherence < 0.5: Reject measurement entirely
+    Reject,
+    /// >10s continuous low coherence: Trigger SONA recalibration (ADR-005)
+    Recalibrate,
+}
+```
+
+When `Recalibrate` fires:
+1. Freeze output at last known good pose
+2. Collect 200 frames (10s) of unlabeled CSI
+3. Run AETHER contrastive TTT (ADR-024) to adapt encoder
+4. Update SONA LoRA weights (ADR-005), <1ms per update
+5. Resume sensing with adapted model
+
+### 2.7 Pose Tracker (17-Keypoint Kalman with Re-ID)
+
+Lift the Kalman + lifecycle + re-ID infrastructure from `wifi-densepose-mat/src/tracking/` (ADR-026) into the RuvSense bounded context, extended for 17-keypoint skeletons:
+
+| Parameter | Value | Rationale |
+|-----------|-------|-----------|
+| State dimension | 6 per keypoint (x,y,z,vx,vy,vz) | Constant-velocity model |
+| Process noise σ_a | 0.3 m/s² | Normal walking acceleration |
+| Measurement noise σ_obs | 0.08 m | Target <8cm RMS at torso |
+| Mahalanobis gate | χ²(3) = 9.0 | 3σ ellipsoid (same as ADR-026) |
+| Birth hits | 2 frames (100ms at 20Hz) | Reject single-frame noise |
+| Loss misses | 5 frames (250ms) | Brief occlusion tolerance |
+| Re-ID feature | AETHER 128-dim embedding | Body-shape discriminative (ADR-024) |
+| Re-ID window | 5 seconds | Sufficient for crossing recovery |
+
+**Track assignment** uses `ruvector-mincut`'s `DynamicPersonMatcher` (already integrated in `metrics.rs`, ADR-016) with joint position + embedding cost:
+
+```
+cost(track_i, det_j) = 0.6 * mahalanobis(track_i, det_j.position)
+                      + 0.4 * (1 - cosine_sim(track_i.embedding, det_j.embedding))
+```
+
+---
+
+## 3. GOAP Integration Plan (Goal-Oriented Action Planning)
+
+### 3.1 Action Dependency Graph
+
+```
+Phase 1: Foundation
+  Action 1: Channel-Hopping Firmware ──────────────────────┐
+      │                                                      │
+      v                                                      │
+  Action 2: Multi-Band Frame Fusion ──→ Action 6: Coherence │
+      │                                  Metric              │
+      v                                    │                 │
+  Action 3: Multistatic Mesh              v                 │
+      │                              Action 7: Coherence    │
+      v                                  Gate               │
+Phase 2: Tracking                         │                 │
+  Action 4: Pose Tracker ←────────────────┘                 │
+      │                                                      │
+      v                                                      │
+  Action 5: End-to-End Pipeline @ 20 Hz ←────────────────────┘
+      │
+      v
+Phase 4: Hardening
+  Action 8: AETHER Track Re-ID
+      │
+      v
+  Action 9: ADR-029 Documentation (this document)
+```
+
+### 3.2 Cost and RuVector Mapping
+
+| # | Action | Cost | Preconditions | RuVector Crates | Effects |
+|---|--------|------|---------------|-----------------|---------|
+| 1 | Channel-hopping firmware | 4/10 | ESP32 firmware exists | None (pure C) | `bandwidth_extended = true` |
+| 2 | Multi-band frame fusion | 5/10 | Action 1 | `solver`, `attention` | `fused_multi_band_frame = true` |
+| 3 | Multistatic mesh aggregation | 5/10 | Action 2 | `mincut`, `attn-mincut` | `multistatic_mesh = true` |
+| 4 | Pose tracker | 4/10 | Action 3, 7 | `mincut` | `pose_tracker = true` |
+| 5 | End-to-end pipeline | 6/10 | Actions 2-4 | `temporal-tensor`, `attention` | `20hz_update = true` |
+| 6 | Coherence metric | 3/10 | Action 2 | `solver` | `coherence_metric = true` |
+| 7 | Coherence gate | 3/10 | Action 6 | `attn-mincut` | `coherence_gating = true` |
+| 8 | AETHER re-ID | 4/10 | Actions 4, 7 | `attention` | `identity_stable = true` |
+| 9 | ADR documentation | 2/10 | All above | None | Decision documented |
+
+**Total cost: 36 units. Minimum viable path to acceptance test: Actions 1-5 + 6-7 = 30 units.**
+
+### 3.3 Latency Budget (50ms cycle)
+
+| Stage | Budget | Method |
+|-------|--------|--------|
+| UDP receive + parse | <1 ms | ADR-018 binary, 148 bytes, zero-alloc |
+| Multi-band fusion | ~2 ms | NeumannSolver on 2×2 phase alignment |
+| Multistatic fusion | ~3 ms | attn_mincut on 3-6 nodes × 64 velocity bins |
+| Model inference | ~30-40 ms | CsiToPoseTransformer (lightweight, no ResNet) |
+| Kalman update | <1 ms | 17 independent 6D filters, stack-allocated |
+| **Total** | **~37-47 ms** | **Fits in 50 ms** |
+
+---
+
+## 4. Hardware Bill of Materials
+
+| Component | Qty | Unit Cost | Purpose |
+|-----------|-----|-----------|---------|
+| ESP32-S3-DevKitC-1 | 4 | $10 | TX/RX sensing nodes |
+| ESP32-S3-DevKitC-1 | 1 | $10 | Aggregator (or x86/RPi host) |
+| External 5dBi antenna | 4-8 | $3 | Improved gain, directional coverage |
+| USB-C hub (4 port) | 1 | $15 | Power distribution |
+| Wall mount brackets | 4 | $2 | Ceiling/wall installation |
+| **Total** | | **$73-91** | Complete 4-node mesh |
+
+---
+
+## 5. RuVector v2.0.4 Integration Map
+
+All five published crates are exercised:
+
+| Crate | Actions | Integration Point | Algorithmic Advantage |
+|-------|---------|-------------------|----------------------|
+| `ruvector-solver` | 2, 6 | Phase alignment; coherence matrix decomposition | O(√n) Neumann convergence |
+| `ruvector-attention` | 2, 5, 8 | Cross-channel weighting; ring buffer; embedding similarity | Sublinear attention for small d |
+| `ruvector-mincut` | 3, 4 | Viewpoint diversity partitioning; track assignment | O(n^1.5 log n) dynamic updates |
+| `ruvector-attn-mincut` | 3, 7 | Cross-node spectrogram fusion; coherence gating | Attention + mincut in one pass |
+| `ruvector-temporal-tensor` | 5 | Compressed sensing window ring buffer | 50-75% memory reduction |
+
+---
+
+## 6. IEEE 802.11bf Alignment
+
+RuvSense's TDMA sensing schedule is forward-compatible with IEEE 802.11bf (WLAN Sensing, published 2024):
+
+| RuvSense Concept | 802.11bf Equivalent |
+|-----------------|---------------------|
+| TX slot | Sensing Initiator |
+| RX slot | Sensing Responder |
+| TDMA cycle | Sensing Measurement Instance |
+| NDP frame | Sensing NDP |
+| Aggregator | Sensing Session Owner |
+
+When commercial APs support 802.11bf, the ESP32 mesh can interoperate by translating SSP slots into 802.11bf Sensing Trigger frames.
+
+---
+
+## 7. Dependency Changes
+
+### Firmware (C)
+
+New files:
+- `firmware/esp32-csi-node/main/sensing_schedule.h`
+- `firmware/esp32-csi-node/main/sensing_schedule.c`
+
+Modified files:
+- `firmware/esp32-csi-node/main/csi_collector.c` (add channel hopping, link tagging)
+- `firmware/esp32-csi-node/main/main.c` (add GPIO sync, TDMA timer)
+
+### Rust
+
+New module: `crates/wifi-densepose-signal/src/ruvsense/` (6 files, ~1500 lines estimated)
+
+Modified files:
+- `crates/wifi-densepose-signal/src/lib.rs` (export `ruvsense` module)
+- `crates/wifi-densepose-signal/Cargo.toml` (no new deps; all ruvector crates already present per ADR-017)
+- `crates/wifi-densepose-sensing-server/src/main.rs` (wire RuvSense pipeline into WebSocket output)
+
+No new workspace dependencies. All ruvector crates are already in the workspace `Cargo.toml`.
+
+---
+
+## 8. Implementation Priority
+
+| Priority | Actions | Weeks | Milestone |
+|----------|---------|-------|-----------|
+| P0 | 1 (firmware) | 2 | Channel-hopping ESP32 prototype |
+| P0 | 2 (multi-band) | 2 | Wideband virtual frames |
+| P1 | 3 (multistatic) | 2 | Multi-node fusion |
+| P1 | 4 (tracker) | 1 | 17-keypoint Kalman |
+| P1 | 6, 7 (coherence) | 1 | Gated updates |
+| P2 | 5 (end-to-end) | 2 | 20 Hz pipeline |
+| P2 | 8 (AETHER re-ID) | 1 | Identity hardening |
+| P3 | 9 (docs) | 0.5 | This ADR finalized |
+| **Total** | | **~10 weeks** | **Acceptance test** |
+
+---
+
+## 9. Consequences
+
+### 9.1 Positive
+
+- **3x bandwidth improvement** without hardware changes (channel hopping on existing ESP32)
+- **12 independent viewpoints** from 4 commodity $10 nodes (C(4,2) × 2 links)
+- **20 Hz update rate** with Kalman-smoothed output for sub-30mm torso jitter
+- **Days-long stability** via coherence gating + SONA recalibration
+- **All five ruvector crates exercised** — consistent algorithmic foundation
+- **$73-91 total BOM** — accessible for research and production
+- **802.11bf forward-compatible** — investment protected as commercial sensing arrives
+- **Cognitum upgrade path** — same software stack, swap ESP32 for higher-bandwidth front end
+
+### 9.2 Negative
+
+- **4-node deployment** requires physical installation and calibration of node positions
+- **TDMA scheduling** reduces per-node CSI rate (each node only transmits 1/4 of the time)
+- **Channel hopping** introduces ~1-5ms gaps during `esp_wifi_set_channel()` transitions
+- **5 GHz CSI on ESP32-S3** may not be available (ESP32-C6 supports it natively)
+- **Coherence gate** may reject valid measurements during fast body motion (mitigation: gate only on static-subcarrier coherence)
+
+### 9.3 Risks
+
+| Risk | Probability | Impact | Mitigation |
+|------|-------------|--------|------------|
+| ESP32 channel hop causes CSI gaps | Medium | Reduced effective rate | Measure gap duration; increase dwell if >5ms |
+| 5 GHz CSI unavailable on S3 | High | Lose frequency diversity | Fallback: 3-channel 2.4 GHz still provides 3x BW; ESP32-C6 for dual-band |
+| Model inference >40ms | Medium | Miss 20 Hz target | Run model at 10 Hz; Kalman predict at 20 Hz interpolates |
+| Two-person separation fails at 3 nodes | Low | Identity swaps | AETHER re-ID recovers; increase to 4-6 nodes |
+| Coherence gate false-triggers | Low | Missed updates | Gate on environmental coherence only, not body-motion subcarriers |
+
+---
+
+## 10. Related ADRs
+
+| ADR | Relationship |
+|-----|-------------|
+| ADR-012 | **Extended**: RuvSense adds TDMA multistatic to single-AP mesh |
+| ADR-014 | **Used**: All 6 SOTA algorithms applied per-link |
+| ADR-016 | **Extended**: New ruvector integration points for multi-link fusion |
+| ADR-017 | **Extended**: Coherence gating adds temporal stability layer |
+| ADR-018 | **Modified**: Firmware gains channel hopping, TDMA schedule, HT40 |
+| ADR-022 | **Complementary**: RuvSense is the ESP32 equivalent of Windows multi-BSSID |
+| ADR-024 | **Used**: AETHER embeddings for person re-identification |
+| ADR-026 | **Reused**: Kalman + lifecycle infrastructure lifted to RuvSense |
+| ADR-027 | **Used**: GeometryEncoder, HardwareNormalizer, FiLM conditioning |
+
+---
+
+## 11. References
+
+1. IEEE 802.11bf-2024. "WLAN Sensing." IEEE Standards Association.
+2. Geng, J., Huang, D., De la Torre, F. (2023). "DensePose From WiFi." arXiv:2301.00250.
+3. Yan, K. et al. (2024). "Person-in-WiFi 3D." CVPR 2024, pp. 969-978.
+4. Chen, L. et al. (2026). "PerceptAlign: Geometry-Aware WiFi Sensing." arXiv:2601.12252.
+5. Kotaru, M. et al. (2015). "SpotFi: Decimeter Level Localization Using WiFi." SIGCOMM.
+6. Zheng, Y. et al. (2019). "Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi." MobiSys.
+7. Zeng, Y. et al. (2019). "FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing." MobiCom.
+8. AM-FM (2026). "A Foundation Model for Ambient Intelligence Through WiFi." arXiv:2602.11200.
+9. Espressif ESP-CSI. https://github.com/espressif/esp-csi

docs/adr/ADR-030-ruvsense-persistent-field-model.md

@@ -0,0 +1,364 @@
+# ADR-030: RuvSense Persistent Field Model — Longitudinal Drift Detection and Exotic Sensing Tiers
+
+| Field | Value |
+|-------|-------|
+| **Status** | Proposed |
+| **Date** | 2026-03-02 |
+| **Deciders** | ruv |
+| **Codename** | **RuvSense Field** — Persistent Electromagnetic World Model |
+| **Relates to** | ADR-029 (RuvSense Multistatic), ADR-005 (SONA Self-Learning), ADR-024 (AETHER Embeddings), ADR-016 (RuVector Integration), ADR-026 (Survivor Track Lifecycle), ADR-027 (MERIDIAN Generalization) |
+
+---
+
+## 1. Context
+
+### 1.1 Beyond Pose Estimation
+
+ADR-029 establishes RuvSense as a sensing-first multistatic mesh achieving 20 Hz DensePose with <30mm jitter. That treats WiFi as a **momentary pose estimator**. The next leap: treat the electromagnetic field as a **persistent world model** that remembers, predicts, and explains.
+
+The most exotic capabilities come from this shift in abstraction level:
+- The room is the model, not the person
+- People are structured perturbations to a baseline
+- Changes are deltas from a known state, not raw measurements
+- Time is a first-class dimension — the system remembers days, not frames
+
+### 1.2 The Seven Capability Tiers
+
+| Tier | Capability | Foundation |
+|------|-----------|-----------|
+| 1 | **Field Normal Modes** — Room electromagnetic eigenstructure | Baseline calibration + SVD |
+| 2 | **Coarse RF Tomography** — 3D occupancy volume from link attenuations | Sparse tomographic inversion |
+| 3 | **Intention Lead Signals** — Pre-movement prediction (200-500ms lead) | Temporal embedding trajectory analysis |
+| 4 | **Longitudinal Biomechanics Drift** — Personal baseline deviation over days | Welford statistics + HNSW memory |
+| 5 | **Cross-Room Continuity** — Identity persistence across spaces without optics | Environment fingerprinting + transition graph |
+| 6 | **Invisible Interaction Layer** — Multi-user gesture control through walls/darkness | Per-person CSI perturbation classification |
+| 7 | **Adversarial Detection** — Physically impossible signal identification | Multi-link consistency + field model constraints |
+
+### 1.3 Signals, Not Diagnoses
+
+RF sensing detects **biophysical proxies**, not medical conditions:
+
+| Detectable Signal | Not Detectable |
+|-------------------|---------------|
+| Breathing rate variability | COPD diagnosis |
+| Gait asymmetry shift (18% over 14 days) | Parkinson's disease |
+| Posture instability increase | Neurological condition |
+| Micro-tremor onset | Specific tremor etiology |
+| Activity level decline | Depression or pain diagnosis |
+
+The output is: "Your movement symmetry has shifted 18 percent over 14 days." That is actionable without being diagnostic. The evidence chain (stored embeddings, drift statistics, coherence scores) is fully traceable.
+
+### 1.4 Acceptance Tests
+
+**Tier 0 (ADR-029):** Two people, 20 Hz, 10 min stable tracks, zero ID swaps, <30mm torso jitter.
+
+**Tier 1-4 (this ADR):** Seven-day run, no manual tuning. System flags one real environmental change and one real human drift event, produces traceable explanation using stored embeddings plus graph constraints.
+
+**Tier 5-7 (appliance):** Thirty-day local run, no camera. Detects meaningful drift with <5% false alarm rate.
+
+---
+
+## 2. Decision
+
+### 2.1 Implement Field Normal Modes as the Foundation
+
+Add a `field_model` module to `wifi-densepose-signal/src/ruvsense/` that learns the room's electromagnetic baseline during unoccupied periods and decomposes all subsequent observations into environmental drift + body perturbation.
+
+```
+wifi-densepose-signal/src/ruvsense/
+├── mod.rs                // (existing, extend)
+├── field_model.rs        // NEW: Field normal mode computation + perturbation extraction
+├── tomography.rs         // NEW: Coarse RF tomography from link attenuations
+├── longitudinal.rs       // NEW: Personal baseline + drift detection
+├── intention.rs          // NEW: Pre-movement lead signal detector
+├── cross_room.rs         // NEW: Cross-room identity continuity
+├── gesture.rs            // NEW: Gesture classification from CSI perturbations
+├── adversarial.rs        // NEW: Physically impossible signal detection
+└── (existing files...)
+```
+
+### 2.2 Core Architecture: The Persistent Field Model
+
+```
+                    Time
+                     │
+                     ▼
+    ┌────────────────────────────────┐
+    │     Field Normal Modes (Tier 1) │
+    │     Room baseline + SVD modes   │
+    │     ruvector-solver             │
+    └────────────┬───────────────────┘
+                 │ Body perturbation (environmental drift removed)
+                 │
+         ┌───────┴───────┐
+         │               │
+         ▼               ▼
+    ┌──────────┐   ┌──────────────┐
+    │ Pose     │   │ RF Tomography│
+    │ (ADR-029)│   │ (Tier 2)     │
+    │ 20 Hz    │   │ Occupancy vol│
+    └────┬─────┘   └──────────────┘
+         │
+         ▼
+    ┌──────────────────────────────┐
+    │  AETHER Embedding (ADR-024)  │
+    │  128-dim contrastive vector  │
+    └────────────┬─────────────────┘
+                 │
+         ┌───────┼───────┐
+         │       │       │
+         ▼       ▼       ▼
+    ┌────────┐ ┌─────┐ ┌──────────┐
+    │Intention│ │Track│ │Cross-Room│
+    │Lead    │ │Re-ID│ │Continuity│
+    │(Tier 3)│ │     │ │(Tier 5)  │
+    └────────┘ └──┬──┘ └──────────┘
+                  │
+                  ▼
+    ┌──────────────────────────────┐
+    │  RuVector Longitudinal Memory │
+    │  HNSW + graph + Welford stats│
+    │  (Tier 4)                     │
+    └──────────────┬───────────────┘
+                   │
+           ┌───────┴───────┐
+           │               │
+           ▼               ▼
+    ┌──────────────┐ ┌──────────────┐
+    │ Drift Reports│ │ Adversarial  │
+    │ (Level 1-3)  │ │ Detection    │
+    │              │ │ (Tier 7)     │
+    └──────────────┘ └──────────────┘
+```
+
+### 2.3 Field Normal Modes (Tier 1)
+
+**What it is:** The room's electromagnetic eigenstructure — the stable propagation paths, reflection coefficients, and interference patterns when nobody is present.
+
+**How it works:**
+1. During quiet periods (empty room, overnight), collect 10 minutes of CSI across all links
+2. Compute per-link baseline (mean CSI vector)
+3. Compute environmental variation modes via SVD (temperature, humidity, time-of-day effects)
+4. Store top-K modes (K=3-5 typically captures >95% of environmental variance)
+5. At runtime: subtract baseline, project out environmental modes, keep body perturbation
+
+```rust
+pub struct FieldNormalMode {
+    pub baseline: Vec<Vec<Complex<f32>>>,      // [n_links × n_subcarriers]
+    pub environmental_modes: Vec<Vec<f32>>,    // [n_modes × n_subcarriers]
+    pub mode_energies: Vec<f32>,               // eigenvalues
+    pub calibrated_at: u64,
+    pub geometry_hash: u64,
+}
+```
+
+**RuVector integration:**
+- `ruvector-solver` → Low-rank SVD for mode extraction
+- `ruvector-temporal-tensor` → Compressed baseline history storage
+- `ruvector-attn-mincut` → Identify which subcarriers belong to which mode
+
+### 2.4 Longitudinal Drift Detection (Tier 4)
+
+**The defensible pipeline:**
+
+```
+RF → AETHER contrastive embedding
+   → RuVector longitudinal memory (HNSW + graph)
+     → Coherence-gated drift detection (Welford statistics)
+       → Risk flag with traceable evidence
+```
+
+**Three monitoring levels:**
+
+| Level | Signal Type | Example Output |
+|-------|------------|----------------|
+| **1: Physiological** | Raw biophysical metrics | "Breathing rate: 18.3 BPM today, 7-day avg: 16.1" |
+| **2: Drift** | Personal baseline deviation | "Gait symmetry shifted 18% over 14 days" |
+| **3: Risk correlation** | Pattern-matched concern | "Pattern consistent with increased fall risk" |
+
+**Storage model:**
+
+```rust
+pub struct PersonalBaseline {
+    pub person_id: PersonId,
+    pub gait_symmetry: WelfordStats,
+    pub stability_index: WelfordStats,
+    pub breathing_regularity: WelfordStats,
+    pub micro_tremor: WelfordStats,
+    pub activity_level: WelfordStats,
+    pub embedding_centroid: Vec<f32>,  // [128]
+    pub observation_days: u32,
+    pub updated_at: u64,
+}
+```
+
+**RuVector integration:**
+- `ruvector-temporal-tensor` → Compressed daily summaries (50-75% memory savings)
+- HNSW → Embedding similarity search across longitudinal record
+- `ruvector-attention` → Per-metric drift significance weighting
+- `ruvector-mincut` → Temporal segmentation (detect changepoints in metric series)
+
+### 2.5 Regulatory Classification
+
+| Classification | What You Claim | Regulatory Path |
+|---------------|---------------|-----------------|
+| **Consumer wellness** (recommended first) | Activity metrics, breathing rate, stability score | Self-certification, FCC Part 15 |
+| **Clinical decision support** (future) | Fall risk alert, respiratory pattern concern | FDA Class II 510(k) or De Novo |
+| **Regulated medical device** (requires clinical partner) | Diagnostic claims for specific conditions | FDA Class II/III + clinical trials |
+
+**Decision: Start as consumer wellness.** Build 12+ months of real-world longitudinal data. The dataset itself becomes the asset for future regulatory submissions.
+
+---
+
+## 3. Appliance Product Categories
+
+### 3.1 Invisible Guardian
+
+Wall-mounted wellness monitor for elderly care and independent living. No camera, no microphone, no reconstructable data. Stores embeddings and structural deltas only.
+
+| Spec | Value |
+|------|-------|
+| Nodes | 4 ESP32-S3 pucks per room |
+| Processing | Central hub (RPi 5 or x86) |
+| Power | PoE or USB-C |
+| Output | Risk flags, drift alerts, occupancy timeline |
+| BOM | $73-91 (ESP32 mesh) + $35-80 (hub) |
+| Validation | 30-day autonomous run, <5% false alarm rate |
+
+### 3.2 Spatial Digital Twin Node
+
+Live electromagnetic room model for smart buildings and workplace analytics.
+
+| Spec | Value |
+|------|-------|
+| Output | Occupancy heatmap, flow vectors, dwell time, anomaly events |
+| Integration | MQTT/REST API for BMS and CAFM |
+| Retention | 30-day rolling, GDPR-compliant |
+| Vertical | Smart buildings, retail, workspace optimization |
+
+### 3.3 RF Interaction Surface
+
+Multi-user gesture interface. No cameras. Works in darkness, smoke, through clothing.
+
+| Spec | Value |
+|------|-------|
+| Gestures | Wave, point, beckon, push, circle + custom |
+| Users | Up to 4 simultaneous |
+| Latency | <100ms gesture recognition |
+| Vertical | Smart home, hospitality, accessibility |
+
+### 3.4 Pre-Incident Drift Monitor
+
+Longitudinal biomechanics tracker for rehabilitation and occupational health.
+
+| Spec | Value |
+|------|-------|
+| Baseline | 7-day calibration per person |
+| Alert | Metric drift >2sigma for >3 days |
+| Evidence | Stored embedding trajectory + statistical report |
+| Vertical | Elderly care, rehab, occupational health |
+
+### 3.5 Vertical Recommendation for First Hardware SKU
+
+**Invisible Guardian** — the elderly care wellness monitor. Rationale:
+1. Largest addressable market with immediate revenue (aging population, care facility demand)
+2. Lowest regulatory bar (consumer wellness, no diagnostic claims)
+3. Privacy advantage over cameras is a selling point, not a limitation
+4. 30-day autonomous operation validates all tiers (field model, drift detection, coherence gating)
+5. $108-171 BOM allows $299-499 retail with healthy margins
+
+---
+
+## 4. RuVector Integration Map (Extended)
+
+All five crates are exercised across the exotic tiers:
+
+| Tier | Crate | API | Role |
+|------|-------|-----|------|
+| 1 (Field) | `ruvector-solver` | `NeumannSolver` + SVD | Environmental mode decomposition |
+| 1 (Field) | `ruvector-temporal-tensor` | `TemporalTensorCompressor` | Baseline history storage |
+| 1 (Field) | `ruvector-attn-mincut` | `attn_mincut` | Mode-subcarrier assignment |
+| 2 (Tomo) | `ruvector-solver` | `NeumannSolver` (L1) | Sparse tomographic inversion |
+| 3 (Intent) | `ruvector-attention` | `ScaledDotProductAttention` | Temporal trajectory weighting |
+| 3 (Intent) | `ruvector-temporal-tensor` | `CompressedCsiBuffer` | 2-second embedding history |
+| 4 (Drift) | `ruvector-temporal-tensor` | `TemporalTensorCompressor` | Daily summary compression |
+| 4 (Drift) | `ruvector-attention` | `ScaledDotProductAttention` | Metric drift significance |
+| 4 (Drift) | `ruvector-mincut` | `DynamicMinCut` | Temporal changepoint detection |
+| 5 (Cross-Room) | `ruvector-attention` | HNSW | Room and person fingerprint matching |
+| 5 (Cross-Room) | `ruvector-mincut` | `MinCutBuilder` | Transition graph partitioning |
+| 6 (Gesture) | `ruvector-attention` | `ScaledDotProductAttention` | Gesture template matching |
+| 7 (Adversarial) | `ruvector-solver` | `NeumannSolver` | Physical plausibility verification |
+| 7 (Adversarial) | `ruvector-attn-mincut` | `attn_mincut` | Multi-link consistency check |
+
+---
+
+## 5. Implementation Priority
+
+| Priority | Tier | Module | Weeks | Dependency |
+|----------|------|--------|-------|------------|
+| P0 | 1 | `field_model.rs` | 2 | ADR-029 multistatic mesh operational |
+| P0 | 4 | `longitudinal.rs` | 2 | Tier 1 baseline + AETHER embeddings |
+| P1 | 2 | `tomography.rs` | 1 | Tier 1 perturbation extraction |
+| P1 | 3 | `intention.rs` | 2 | Tier 1 + temporal embedding history |
+| P2 | 5 | `cross_room.rs` | 2 | Tier 4 person profiles + multi-room deployment |
+| P2 | 6 | `gesture.rs` | 1 | Tier 1 perturbation + per-person separation |
+| P3 | 7 | `adversarial.rs` | 1 | Tier 1 field model + multi-link consistency |
+
+**Total exotic tier: ~11 weeks after ADR-029 acceptance test passes.**
+
+---
+
+## 6. Consequences
+
+### 6.1 Positive
+
+- **Room becomes self-sensing**: Field normal modes provide a persistent baseline that explains change as structured deltas
+- **7-day autonomous operation**: Coherence gating + SONA adaptation + longitudinal memory eliminate manual tuning
+- **Privacy by design**: No images, no audio, no reconstructable data — only embeddings and statistical summaries
+- **Traceable evidence**: Every drift alert links to stored embeddings, timestamps, and graph constraints
+- **Multiple product categories**: Same software stack, different packaging — Guardian, Twin, Interaction, Drift Monitor
+- **Regulatory clarity**: Consumer wellness first, clinical decision support later with accumulated dataset
+- **Security primitive**: Coherence gating detects adversarial injection, not just quality issues
+
+### 6.2 Negative
+
+- **7-day calibration** required for personal baselines (system is less useful during initial period)
+- **Empty-room calibration** needed for field normal modes (may not always be available)
+- **Storage growth**: Longitudinal memory grows ~1 KB/person/day (manageable but non-zero)
+- **Statistical power**: Drift detection requires 14+ days of data for meaningful z-scores
+- **Multi-room**: Cross-room continuity requires hardware in all rooms (cost scales linearly)
+
+### 6.3 Risks
+
+| Risk | Probability | Impact | Mitigation |
+|------|-------------|--------|------------|
+| Field modes drift faster than expected | Medium | False perturbation detections | Reduce mode update interval from 24h to 4h |
+| Personal baselines too variable | Medium | High false alarm rate for drift | Widen sigma threshold from 2σ to 3σ; require 5+ days |
+| Cross-room matching fails for similar body types | Low | Identity confusion | Require temporal proximity (<60s) plus spatial adjacency |
+| Gesture recognition insufficient SNR | Medium | <80% accuracy | Restrict to near-field (<2m) initially |
+| Adversarial injection via coordinated WiFi injection | Very Low | Spoofed occupancy | Multi-link consistency check makes single-link spoofing detectable |
+
+---
+
+## 7. Related ADRs
+
+| ADR | Relationship |
+|-----|-------------|
+| ADR-029 | **Prerequisite**: Multistatic mesh is the sensing substrate for all exotic tiers |
+| ADR-005 (SONA) | **Extended**: SONA recalibration triggered by coherence gate → now also by drift events |
+| ADR-016 (RuVector) | **Extended**: All 5 crates exercised across 7 exotic tiers |
+| ADR-024 (AETHER) | **Critical dependency**: Embeddings are the representation for all longitudinal memory |
+| ADR-026 (Tracking) | **Extended**: Track lifecycle now spans days (not minutes) for drift detection |
+| ADR-027 (MERIDIAN) | **Used**: Room geometry encoding for field normal mode conditioning |
+
+---
+
+## 8. References
+
+1. IEEE 802.11bf-2024. "WLAN Sensing." IEEE Standards Association.
+2. FDA. "General Wellness: Policy for Low Risk Devices." Guidance Document, 2019.
+3. EU MDR 2017/745. "Medical Device Regulation." Official Journal of the European Union.
+4. Welford, B.P. (1962). "Note on a Method for Calculating Corrected Sums of Squares." Technometrics.
+5. Chen, L. et al. (2026). "PerceptAlign: Geometry-Aware WiFi Sensing." arXiv:2601.12252.
+6. AM-FM (2026). "A Foundation Model for Ambient Intelligence Through WiFi." arXiv:2602.11200.
+7. Geng, J. et al. (2023). "DensePose From WiFi." arXiv:2301.00250.