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feat(ruview-unified): unified RF spatial world model P1 (ADR-273..278)
One shared representation instead of another isolated RF classifier: new v2 leaf crate ruview-unified implementing all five ADR-273 pillars, plus six ADRs with measured, grade-labeled results. - Canonical RfTensor + fail-closed hardware adapter registry (802.11 CSI via wifi-densepose-core::CsiFrame, FMCW radar cubes, UWB CIR, 5G SRS); shared layout/gain/phase normalization proven by tests (ADR-274). - Universal RF foundation encoder: CFO-aligned, median-scaled tokenizer; masked-reconstruction pretraining with hand-derived backprop verified against central finite differences (max rel err 1.31e-5 over all 12 parameter groups); fusion contract z = Enc ⊙ σ(AgeEnc) + GeomEnc; task adapters under the 1% budget (129/268/387/2 params vs 40,856 backbone), enforced by test. - RF-aware Gaussian spatial memory: anisotropic primitives with per-band reflectivity, confidence-weighted fusion, decay, spatial-hash/semantic queries, closed-form Beer-Lambert channel gain (exact Friis on empty map), inverse gain updates (unseen 6.1 dB wall learned to <0.5 dB in 20 observations), task-gated scene graph (ADR-275). - Physics-guided synthetic RF worlds: image-method multipath (order ≤2), complex-permittivity Fresnel materials, emergent Doppler proven against the analytic phase rate, seeded ChaCha20 randomization of physics and hardware nuisances; byte-deterministic per seed (ADR-276). - Edge sensing control plane: 802.11bf/ETSI-ISAC-aligned purposes/zones, fail-closed authorization, double-gated identity, retention bounds; BoundedEvent-only trust boundary makes raw RF export unrepresentable (ADR-277). Radar inverse rendering stays a gated research program (ADR-278, no code by design). Anti-leakage acceptance pipeline (strict splits by room/day/person/ chipset/firmware/layout with independent disjointness verification): presence F1 1.00 on held-out rooms and held-out chipset, degradation 0.0, ECE 0.012, p95 latency 2.0 ms debug / 105 µs release — ALL SYNTHETIC until P2 real-data validation. Benchmarks + optimization pass: channel_gain 139→27 µs (O(1) in map size via segment-corridor AABB sweep), observe_link 305→74 µs, DFT twiddle plan 4.9x; hash/linear crossover (~4k Gaussians) reported honestly. Tests: ruview-unified 66 unit + 3 acceptance, 0 failed; workspace 3,771 passed 0 failed (--exclude wifi-densepose-desktop: GTK headers unavailable in this container). Python proof: VERDICT PASS. Also gitignore sensing-server test-run artifacts (incl. generated session-secret). Co-Authored-By: claude-flow <ruv@ruv.net> Claude-Session: https://claude.ai/code/session_01Q1R5zhz6sSfXGRXpgBwpFX
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# ADR-273: Unified RF Spatial World Model — one shared representation, not another isolated RF classifier
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| Field | Value |
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|-------|-------|
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| **Status** | Accepted — **P1 implemented** (new v2 workspace crate `ruview-unified`; 66 unit + 3 acceptance-pipeline tests, 0 failed; criterion benches) |
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| **Date** | 2026-07-26 |
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| **Deciders** | ruv |
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| **Codebase target** | `v2/crates/ruview-unified/` (new leaf crate; single internal dep on `wifi-densepose-core` for `CsiFrame`) |
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| **Sub-ADRs** | ADR-274 (universal RF encoder + adapter registry), ADR-275 (RF-aware Gaussian spatial memory), ADR-276 (physics-guided synthetic RF worlds), ADR-277 (edge sensing control plane), ADR-278 (radar inverse rendering research program) |
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| **Relates to** | ADR-152 (WiFi-Pose SOTA intake: geometry conditioning), ADR-153 (802.11bf protocol model), ADR-260/262 (RuField MFS + bridge), ADR-135/136 (calibration + canonical frame provenance), ADR-024 (AETHER), ADR-027 (MERIDIAN domain generalization) |
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| **Scope** | Decide the target architecture for RuView + RuVector sensing through 2026-H2: one persistent, queryable spatial world model that vision, WiFi CSI, cellular CFR/SRS, radar, geometry, semantics, uncertainty, and time all update — and the priority order for building it. |
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---
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## 0. PROOF discipline
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Every number in this ADR family is one of:
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- **MEASURED-SYNTHETIC** — produced by this repo's tests/benches on data from the ADR-276 physics generator. Reproducible: `cd v2 && cargo test -p ruview-unified` / `cargo bench -p ruview-unified`. **No claim of real-world accuracy is made or implied.**
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- **MEASURED-CODE** — a structural property of the implementation (parameter counts, gradient-check error, determinism), verified by a named test.
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- **EXTERNAL-UNVERIFIED** — a number reported by an external paper/preprint (WiFo-2, WiLHPE, RISE, DiffRadar, HybridSim, OAI SRS demo, …) that this repo has **not** reproduced. These motivated design choices; they are never presented as our results.
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## 1. Context
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Through mid-2026 the field moved decisively away from task-specific RF classifiers:
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1. **RF foundation models** (WiFo-2 scaling across 11.6 B CSI points/12 tasks; WiLLM's dataset adapters + shared self-supervised transformer; age-aware CSI fusion) — the architectural signal: *standardize heterogeneous CSI, pretrain with masked reconstruction, attach small task adapters* (all EXTERNAL-UNVERIFIED).
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2. **Gaussian fields as spatial memory** (EmbodiedSplat online semantic 3-D Gaussian mapping; TGSFormer bounded temporal Gaussian memory; July's physics-informed channel-gain mapping with incremental Gaussian insertion) — the missing bridge between RuView sensing and a queryable digital twin.
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3. **Synthetic RF worlds** (WaveVerse phase-coherent ray tracing; HybridSim's 92 % vs 54 % synthetic-to-real gap when *physics parameters*, not textures, are randomized) — the fastest path out of data scarcity.
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4. **Standards became actionable**: IEEE 802.11bf-2025 published (2025-09), 802.11bk (320 MHz positioning), ETSI ISAC architecture (2026-02) + security report (19 privacy/security issue classes), 3GPP Rel-20 sensing studies, OAI SRS xApp localization demo.
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5. **Generalization lessons**: PerceptAlign (condition on TX/RX geometry), RePos (factor root-relative pose from absolute localization), JITOMA (task-gated scene memory).
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RuView already has the ingredients (calibration ADR-151, canonical frames ADR-136, ruvsense multistatic stack, RuField bridge ADR-262) but they update **separate** state. The decision is to converge on **one shared representation with persistent scene memory**.
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## 2. Decision
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Build the unified model as five pillars in strict priority order (scored 35 % business value / 25 % readiness / 20 % defensibility / 20 % strategic learning):
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| # | Pillar | Score | Sub-ADR | P1 status |
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|---|--------|-------|---------|-----------|
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| 1 | Universal RF foundation encoder + hardware adapter registry | 4.7 | ADR-274 | **implemented** |
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| 2 | RF-aware Gaussian spatial memory | 4.5 | ADR-275 | **implemented** |
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| 3 | Age/geometry/uncertainty-aware inference (folded into the encoder contract) | 4.4 | ADR-274 §3 | **implemented** |
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| 4 | Physics-guided synthetic RF world generator | 4.1 | ADR-276 | **implemented** |
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| 5 | Edge sensing control plane (802.11bf / ETSI ISAC aligned) | 3.9* | ADR-277 | **implemented** (policy engine; O-RAN xApp is roadmap) |
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| 6 | Radar inverse rendering + differentiable RF SLAM | 3.6 | ADR-278 | research program (not implemented) |
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\* the 3.9-scored item is the O-RAN SRS xApp; its *policy plane* and its *SRS adapter seam* ship in P1 because they are cheap and gate everything else.
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The representation contract every pillar shares:
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```text
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z = Encoder(RF tokens) ⊙ σ(AgeEncoder(age)) + GeometryEncoder(sensor_pose)
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```
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served from one canonical tensor (`RfTensor`, ADR-274 §2) and persisted into one scene memory (`GaussianMap` + task-gated `SceneGraph`, ADR-275).
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## 3. Architecture (implemented, `v2/crates/ruview-unified/src/`)
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```text
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vendor captures ──▶ adapters.rs (WiFi CSI / FMCW cube / UWB CIR / 5G SRS)
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│ normalize: layout → gain → phase (ADR-274 §2.3)
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▼
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tensor.rs RfTensor (links × 56 bins × 8 snapshots, complex)
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│
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tokenizer.rs amplitude/delay/Doppler/phase/age/geometry/
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│ clock/uncertainty tokens (CFO-aligned,
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│ median-scale-normalized)
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▼
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encoder.rs + pretrain.rs masked-reconstruction pretraining,
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│ exact hand-derived backprop (gradient-checked)
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▼
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┌── heads.rs ≤1 % task adapters (presence/activity/localization/anomaly)
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│
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├── gaussian/ RF-aware Gaussian memory: fusion, decay, channel-gain
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│ queries, inverse updates, task-gated scene graph
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│
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└── policy.rs purposes/zones/retention/identity gating; BoundedEvent
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is the only exportable type (raw RF unrepresentable)
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```
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`synth/` (ADR-276) generates the labeled physics worlds that train and gate all of it; `eval.rs` implements the anti-leakage protocol below.
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## 4. The non-negotiable evaluation protocol (anti-leakage)
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The biggest failure mode in this field is **domain leakage disguised as accuracy**: random frame splits let a model recognize the room, session, person, device, or trajectory. Bigger models make it worse. Therefore:
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- **No result counts unless the test set holds out complete** rooms, days, people, chipsets, firmware versions, and antenna layouts. `eval::StrictSplit` constructs such splits and `verify()` independently proves disjointness (`eval.rs`; test `verify_catches_a_manufactured_leak`).
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- Track **relative degradation** known→unknown (`relative_degradation`, gate < 20 %), **calibration** (`expected_calibration_error`), and **abstention quality** (`selective_metrics` — an uncertain result must become *no decision*, not a confident guess).
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- Every synthetic number is labeled SYNTHETIC in test output and in these ADRs.
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## 5. Acceptance gates — P1 (synthetic analogue) results
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The ADR's acceptance test (frozen shared encoder, adapters < 1 % of backbone, unseen rooms/chipsets/layouts) is implemented end-to-end in `tests/e2e_acceptance.rs`. **MEASURED-SYNTHETIC** results on the ADR-276 generator (8 rooms × 20 windows × 3 links, seed 273273):
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| Gate (ADR target) | P1 synthetic result | Verdict |
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|---|---|---|
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| Presence F1 ≥ 0.90, unseen rooms | **1.0000** (rooms 6–7 held out of pretraining *and* head training) | pass |
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| Presence F1 ≥ 0.90, unseen chipset | **1.0000** (`chip-2` held out; per-room random gain/phase/CFO/noise) | pass |
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| Cross-environment degradation < 20 % | **0.0000** | pass |
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| Adapter budget < 1 % of backbone | presence 129 / activity 268 / localization 387 / anomaly 2 params vs 40,856-param backbone (< 408) | pass (MEASURED-CODE) |
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| Edge latency p95 < 50 ms | **2.0 ms** debug profile (tokenize+encode); 105 µs encode / 67 µs tokenize release (criterion) | pass |
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| Held-out ECE | **0.0122**; abstention risk monotone in threshold | pass |
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| Raw RF never crosses the trust boundary | structural: only `policy::BoundedEvent` exports (no tensor-carrying variant exists) | pass |
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| Every output carries uncertainty, provenance, model version, purpose | enforced at `BoundedEvent::new` (construction fails otherwise) | pass |
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**Honest reading**: a synthetic world where presence ⇔ a moving scatterer is *separable by construction*; F1 = 1.0 here validates the **pipeline and the anti-leakage machinery**, not real-world performance. The real-data gate (5 unseen rooms, 2 unseen chipsets, 2 unseen layouts, measured CSI) is P2 and remains open.
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## 6. Consequences
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- RuView gains a single, tested substrate that all future sensing work (vision fusion, SRS xApp, radar) updates instead of forking.
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- The synthetic-first discipline means every accuracy claim is grade-labeled; publishing an unlabeled number is now a process violation.
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- The Gaussian memory becomes the integration point for RuVector (vector retrieval → graph constraints → geometric verification; the LLM plans the query, the renderer verifies the answer).
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- Cost: a new crate to maintain (~4.6 k lines incl. tests); mitigations: zero heavy deps, deterministic tests, files < 500 lines each.
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## 7. Roadmap after P1
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| Phase | Content | Gate |
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|-------|---------|------|
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| P2 | Replay real `.csi.jsonl` (rvCSI / ADR-262 corpus) through the WiFi adapter; calibrate the anomaly head on real empty-room captures | strict-split F1/ECE on measured data, reported with degradation vs synthetic |
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| P3 | Wire `GaussianMap` into `wifi-densepose-sensing-server` behind the ADR-277 boundary; RuVector embedding of Gaussian clusters | live map consistency + bounded-event-only egress audit |
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| P4 | OAI SRS xApp feeding `CellularSrsAdapter` (the adapter + registry seam already exists) | 0.5 m p90 localization under *non-random* splits |
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| P5 | ADR-278 radar inverse rendering reproduction (RISE first) |
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# ADR-274: Universal RF foundation encoder + hardware adapter registry
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| Field | Value |
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|-------|-------|
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| **Status** | Accepted — **P1 implemented** (`ruview-unified`: `tensor.rs`, `adapters.rs`, `tokenizer.rs`, `encoder.rs`, `pretrain.rs`, `heads.rs`, `eval.rs`) |
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| **Date** | 2026-07-26 |
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| **Parent** | ADR-273 |
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| **Relates to** | ADR-136 (`CanonicalFrame` provenance — the WiFi adapter consumes `wifi-densepose-core::CsiFrame` directly), ADR-152 §2 (geometry conditioning intake), ADR-016/017 (ruvector integration points) |
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## 0. PROOF discipline
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Grades as in ADR-273 §0. Every number below is MEASURED-CODE or MEASURED-SYNTHETIC unless marked EXTERNAL-UNVERIFIED.
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## 1. Context
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WiFo-2 and WiLLM (EXTERNAL-UNVERIFIED) demonstrated that heterogeneous CSI standardization + masked-reconstruction pretraining + small task adapters beats per-task models, and the age-aware CSI line showed a cheap win from encoding sample freshness multiplicatively. RuView has four incompatible capture families today (802.11 CSI, FMCW radar cubes, UWB CIR, and — via O-RAN — 5G SRS). Each previously implied its own model.
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## 2. Decision — canonical tensor + adapter registry
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### 2.1 Canonical tensor
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All modalities normalize to `RfTensor` (`tensor.rs`): complex `(links × 56 bins × 8 snapshots)` plus carrier/bandwidth, per-link `LinkGeometry`, `sample_age_s`, `clock_quality ∈ [0,1]`, `uncertainty ∈ [0,1]`, `device_id`, and a `CalibrationMeta` contract. 56 bins = usable 20 MHz 802.11n subcarriers (and the existing 114→56 interpolation in `wifi-densepose-train`), so the most common source resamples trivially.
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**Boundary rule**: `RfTensor::new` is the only constructor and validates every field (finite samples, geometry/link arity, ranges). Downstream code assumes validity. Tests: `tensor.rs::tests` (4).
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### 2.2 Normalization pipeline (every adapter, 3 stages)
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1. **Layout** — vendor shape → `(links, bins, snapshots)`; FMCW gets a fast-time DFT to range bins; SRS gets comb de-interleaving; then linear complex resampling to canonical dims.
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2. **Amplitude** — per-link division by median amplitude (chipset gain invariance; offset recorded in `CalibrationMeta.gain_offset_db`).
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3. **Phase** — per (link, snapshot), remove constant offset + least-squares linear ramp across bins (CFO residual + sampling-time offset), with unwrapping. Skipped for delay-domain modalities (radar range profiles, UWB taps) where a detrend would erase ToF structure.
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Measured (test `wifi_adapter_normalizes_shape_gain_and_phase`): a synthetic capture with per-link gains ×3.7/×7.4 and phase ramp `0.9 + 0.11·bin` comes out with median amplitude 1.0 ± 1e-9 and residual phase < 1e-4 rad (the ~7 µrad residue is second-order chord-vs-arc error from complex resampling). The radar adapter localizes a fast-time beat tone to the analytically expected canonical range bin (`radar_adapter_localizes_beat_tone_to_range_bin`).
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### 2.3 Registry
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`AdapterRegistry` maps hardware id → `dyn RfAdapter`, **fail-closed** (unknown hardware is an error; wrong modality is a typed `ModalityMismatch`). Reference adapters ship for `esp32s3-csi`, `mr60bha2` (FMCW), `dw3000` (UWB), `oai-srs-xapp` (5G SRS) — the last being the ADR-273 P4 seam.
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## 3. Decision — encoder, fusion contract, adapters
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### 3.1 Tokenizer
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One token per (link, 8-bin subcarrier group); 24 features: log-amplitudes, delay-spectrum DFT (4), Doppler DFT bins 1–4 (log-compressed `ln(1+100·mag)`), temporal amplitude deviation (`ln(1+20·std)`), phase velocity, sample age, link distance/height/azimuth, clock quality, uncertainty (`tokenizer.rs`, layout table on `RfToken`).
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Two hardware-invariance steps precede feature extraction, and both were *forced by measurement*, not aesthetics (see §5 evidence trail):
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- **window-median amplitude normalization** — raw Friis-scale features (~1e-3) left every head unable to learn;
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- **CFO alignment** — per link, each snapshot is de-rotated by `arg Σ_b H[b,s]·H̄[b,0]`; carrier-frequency-offset drift is a *common* rotation and cancels, while a moving scatterer's frequency-selective perturbation survives (test `motion_raises_doppler_and_variance_features` uses a bin-dependent perturbation precisely so alignment cannot cancel it).
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### 3.2 Encoder + pretraining
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Pure-Rust, exactly differentiable (`encoder.rs`):
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```text
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h_i = tanh(W1·x_i + b1) token embedding
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c = mean_i h_i permutation-invariant pool
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m = tanh(W2·c + b2); g = tanh(W2b·m + b2b)
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gate = σ(age_w·age + age_b) multiplicative freshness gate
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z = g ⊙ gate + Wg·geo + bg ← the ADR-273 fusion contract, verbatim
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```
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Masked-reconstruction pretraining (`pretrain.rs`): mask 25 % of tokens, reconstruct each from `[z ; sinusoidal-position]` via a linear head discarded at deployment; SGD.
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**Proof of the backward pass** (MEASURED-CODE, `gradients_match_finite_differences`): analytic gradients of **all 12 parameter groups** vs central finite differences — 174 sampled parameters, max relative error **1.31e-5**, with the absolute floor at central-difference roundoff (≈5e-11). Training halves masked loss and beats the constant-predictor variance baseline (`0.2757 → 0.0966` vs baseline `0.1550`; `pretraining_reduces_masked_loss_and_beats_mean_baseline`). Same seed ⇒ bit-identical weights (`training_is_deterministic`).
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Backbone at deployment config (d_model 128): **40,856 parameters** (hand-count asserted in `param_count_matches_hand_computation`).
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### 3.3 Two representation views (the PerceptAlign lesson, applied)
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- `encode()` → full `z` (geometry-conditioned) — for localization/channel-prediction heads where sensor pose is signal.
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- `encode_content()` → `[g ⊙ gate ; mean token features]` — for environment-invariant heads (presence/activity/anomaly). The additive `Wg·geo` term is a **room-specific offset a linear adapter would memorize** — measured: with it, held-out-room presence F1 was 0.00 while training F1 fit; without it plus the pooled-statistics skip connection, held-out F1 is 1.00 (SYNTHETIC, ADR-273 §5).
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### 3.4 Task adapters, ≤ 1 % budget
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`heads.rs`: presence (logistic, 129 params), activity (rank-2 LoRA-style factorized softmax, 268), localization (linear ℝ³, 387), anomaly (2 calibration statistics on reconstruction error). All < 408 = 1 % of the 40,856-param backbone, asserted in `every_head_fits_the_one_percent_budget_at_deployment_config`. Convex heads train full-batch (deterministic); tests show they fit separable/multiclass toys to ≥ 95 %.
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### 3.5 Anti-leakage evaluation (ADR-273 §4)
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`eval.rs`: `PartitionKey` (room/day/person/chipset/firmware/layout), `StrictSplit::holdout` + independent `verify()`, ECE, coverage/selective-risk, degradation ratio, F1. Six unit tests including a manufactured-leak detection test.
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## 4. Alternatives considered
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- **Candle/ONNX backbone now** — rejected for P1: the deliverable is a *proven contract* (gradient-checked fusion formula, budget enforcement, leakage protocol); porting to `wifi-densepose-nn` backends is mechanical once real-data P2 justifies scale.
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- **Per-modality encoders with late fusion** — rejected: reproduces the isolated-classifier status quo ADR-273 exists to end.
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- **Full transformer attention** — deferred: mean-pool + 2 mixing layers passed every P1 gate; attention is a P2 measurement question, not a default.
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## 5. Evidence trail (what the measurements changed)
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P1 development falsified two comfortable assumptions, recorded here because the *fixes are the ADR*:
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1. Raw-scale tokens: presence head stuck at F1 0.47 even on training rooms → window-median normalization + CFO alignment (train F1 → 0.76).
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2. Geometry-additive `z` for invariant tasks: held-out-room F1 0.00 → content view + pooled-statistic skip (held-out F1 → 1.00) — i.e. *the leak the eval protocol was designed to catch, caught in our own architecture first*.
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## 6. Consequences
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One encoder now serves presence, activity, localization, respiration-class, channel prediction, and anomaly through < 1 % adapters; new hardware lands as an adapter, not a model. Cost: the pure-Rust trainer is CPU-bound (fine at 40 k params; a P2 scale-up moves to `wifi-densepose-nn`).
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# ADR-275: RF-aware Gaussian spatial memory — the persistent scene representation
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| Field | Value |
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|-------|-------|
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| **Status** | Accepted — **P1 implemented** (`ruview-unified/src/gaussian/`: `primitive.rs`, `map.rs`, `gain.rs`, `graph.rs`; 16 unit tests, criterion benches) |
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| **Date** | 2026-07-26 |
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| **Parent** | ADR-273 |
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| **Relates to** | ADR-030 (persistent field model — superseded in direction by this), ADR-134 (CIR/ISTA), ADR-147 (OccWorld priors), ADR-261 (RuVector graph-ANN — the retrieval layer this memory will index into) |
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## 0. PROOF discipline
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Grades per ADR-273 §0. The July 2026 external motivators (EmbodiedSplat ~5 fps online semantic Gaussian mapping, ~67× memory efficiency; TGSFormer bounded temporal Gaussian memory; physics-informed channel-gain mapping with incremental Gaussian insertion; JITOMA task-gated activation) are EXTERNAL-UNVERIFIED throughout.
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## 1. Context
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RuView's spatial state is currently scattered (pose tracker state, field-model eigenstructure, worldgraph tracks). Vision-side SOTA converged on Gaussian fields as the common continuous scene memory, and — the July signal that matters here — the representation crossed into RF: propagation geometry, opacity, attenuation, and scattering as Gaussian primitives, updated *incrementally* when the environment changes. That is exactly the bridge from RuView sensing to a queryable digital twin: one store that answers both geometric questions ("what is near the sofa") and RF questions ("which object caused the channel anomaly", "where did multipath change").
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## 2. Decision — the primitive
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`RfGaussian` (`primitive.rs`) carries all six ADR-273 attribute groups:
|
||||
|
||||
1. **Geometry**: position, per-axis scale (σ), unit-quaternion orientation → anisotropic metric `Σ⁻¹ = R·diag(1/σ²)·Rᵀ`.
|
||||
2. **Semantics**: 16-d embedding (RuVector-alignable).
|
||||
3. **RF response**: reflectivity `[4 bands × 4 incident-angle bins]` (2.4/5/6/60 GHz), plus `occupancy` = peak extinction coefficient (nepers/m) used by the gain model.
|
||||
4. **Motion**: signed Doppler m/s + `{Static, Slow, Fast}` class.
|
||||
5. **Trust/lifecycle**: confidence ∈ [0,1], timestamp, decay τ, `Provenance {device, model_version, synthetic}`.
|
||||
6. **Links**: typed references into the scene graph / RuVector entities.
|
||||
|
||||
Validated constructor (quaternion normalized, ranges checked); anisotropy and rotation are proven behaviorally (thin axis decays ≥ 80× faster at 0.3 m — the analytic ratio is 86; a 90° quaternion rotates the metric with it).
|
||||
|
||||
## 3. Decision — the map
|
||||
|
||||
`GaussianMap` (`map.rs`): spatial-hash grid (1 m default pitch) over a flat store.
|
||||
|
||||
- **Fusion, not accumulation**: an insert within Mahalanobis² 9 of a same-entity-kind Gaussian merges — confidence-weighted position/scale/occupancy/semantics/reflectivity/Doppler, noisy-OR confidence (`c₁+c₂−c₁c₂`), newest provenance wins, links union. Test: two 0.5-confidence observations 0.1 m apart fuse to one Gaussian at the weighted midpoint with confidence 0.75.
|
||||
- **Decay**: exponential confidence decay per Gaussian τ; prune below 0.02; deterministic (replay test).
|
||||
- **Queries**: radius (hash + linear reference impl, equivalence-tested on 100-Gaussian grids), kNN (expanding ring), semantic cosine top-k, and the segment-corridor query below.
|
||||
|
||||
## 4. Decision — channel gain as a first-class query + inverse update
|
||||
|
||||
`gain.rs` implements the RF query surface:
|
||||
|
||||
```text
|
||||
H(tx,rx,f) = (λ/4πd)·e^{-j2πd/λ} · exp(−Σ_g occ_g·I_g)
|
||||
```
|
||||
|
||||
with `I_g` the **closed-form** line integral of each Gaussian's density along the TX→RX segment (1-D Gaussian integral via erf; derivation in the module doc).
|
||||
|
||||
**Exactness anchors (MEASURED-CODE):**
|
||||
|
||||
- Empty map ⇒ **exact Friis** amplitude (< 1e-15) and propagation phase (`empty_map_returns_exact_friis`).
|
||||
- Closed-form line integral matches 1 mm trapezoid quadrature through a rotated anisotropic Gaussian to < 1e-6 (`line_integral_matches_numeric_quadrature`).
|
||||
- On-path absorber attenuates strictly monotonically in occupancy; a 10σ off-path absorber changes LoS gain < 1e-6 dB.
|
||||
|
||||
**Inverse update** (`observe_link`) — the incremental-mapping move: measured link amplitude → target optical depth `τ* = ln(friis/measured)`; a projected-gradient step distributes the residual over intersected Gaussians proportional to their path integrals (exact Newton along the link at lr = 1), clamped at occupancy ≥ 0; if nothing intersects and attenuation is demanded, a compact absorber is spawned at the midpoint sized to close the residual. **Measured**: from an empty map, 20 observations of a link with an unseen 0.7-neper (≈6.1 dB) obstruction converge to < 0.06 neper residual and < 0.5 dB prediction error (`inverse_update_learns_a_wall_from_link_residuals`).
|
||||
|
||||
## 5. Decision — task-gated scene graph
|
||||
|
||||
`graph.rs`: sparse typed nodes (`Object/Room/PersonClass/Device/Event` — person *classes* only; identity lives behind ADR-277's double gate) and relations (`Contains/Near/CausedBy/ObservedBy`). The only sanctioned read is `activate(relevant_kinds, seeds, max_nodes)` — bounded BFS that reports truncation instead of silently scanning (the JITOMA lesson). Tests: an "which object caused the anomaly" activation pulls exactly {event, object, room} and gates out devices/person-classes; the node budget is enforced and truncation is flagged.
|
||||
|
||||
## 6. Performance (criterion, release, this machine)
|
||||
|
||||
| Benchmark | Result | Note |
|
||||
|---|---|---|
|
||||
| `channel_gain`, 1 k Gaussians | **26.9 µs** | was 139 µs with the midpoint-ball candidate query |
|
||||
| `channel_gain`, 16 k Gaussians | **27.7 µs** | ~O(1) in map size after the corridor rewrite |
|
||||
| segment corridor query, hash vs linear | 24 µs vs 6 µs (1 k) / 24 µs vs **163 µs** (16 k) | crossover ≈ 4 k Gaussians — reported honestly; both paths kept + equivalence-tested |
|
||||
| radius query, hash vs linear | 4.3 µs vs 101 µs @ 16 k (23×) | hash loses at 1 k (4.0 vs 1.9 µs) — small maps are brute-force territory |
|
||||
| `observe_link` inverse update | **74 µs** | was 305 µs pre-optimization |
|
||||
| map insert+fuse (64 Gaussians, in observe bench setup) | included above | |
|
||||
|
||||
The optimization pass replaced a midpoint-ball candidate search (`(2·(L/2+3)+1)³ ≈ 9,300` cell lookups on a 14 m link) with an AABB sweep prefiltered by cell-centre-to-segment distance (bound `margin + √3/2·cell`), after a first corridor attempt (per-sample cube inserts into a BTreeSet) measured *worse* (1.2 ms) and was discarded — kept in this record as the honest negative result.
|
||||
|
||||
## 7. Consequences
|
||||
|
||||
- The map answers "where is a person likely", "where did multipath change", and "which object caused a channel anomaly" (gain residual → `CausedBy` edge) from one store.
|
||||
- RuVector integration (ADR-261) becomes: vector search retrieves candidate Gaussians/nodes → graph traversal enforces relations → the gain model *verifies* answers against geometry. The LLM plans the query; it never invents the spatial answer.
|
||||
- Not yet done (P3): live wiring into `wifi-densepose-sensing-server`, visual/depth Gaussian ingestion, and RuVector index sync.
|
||||
@@ -0,0 +1,68 @@
|
||||
# ADR-276: Physics-guided synthetic RF world generator — randomize physics, not textures
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Accepted — **P1 implemented** (`ruview-unified/src/synth/`: `room.rs`, `raytrace.rs`, `generator.rs`; 10 unit tests + the ADR-273 acceptance pipeline consumes it end-to-end) |
|
||||
| **Date** | 2026-07-26 |
|
||||
| **Parent** | ADR-273 |
|
||||
| **Relates to** | ADR-015 (MM-Fi/Wi-Pose datasets), ADR-089 (nvsim — the determinism pattern this follows), ADR-135 (empty-room baselines the generator can emulate) |
|
||||
|
||||
## 0. PROOF discipline
|
||||
|
||||
Grades per ADR-273 §0. WaveVerse (released simulator, phase-coherent ray tracing) and HybridSim (92.07 % vs 54.22 % synthetic-only→real activity recognition when physics is modeled explicitly) are EXTERNAL-UNVERIFIED motivators. Every output of this generator is stamped `RfModality::Synthetic` and every number derived from it is labeled SYNTHETIC — that stamp survives into `Provenance.synthetic` at the ADR-277 export boundary.
|
||||
|
||||
## 1. Context
|
||||
|
||||
RuView's scarcest resource is labeled, *diverse* RF data: rooms, materials, antenna placements, people, chipsets. The 2026 evidence says synthetic RF transfers **when the physics is explicit and the randomization hits physical parameters** (permittivity, geometry, kinematics, hardware nuisances) rather than cosmetic noise. A physics generator also gives the ADR-273 acceptance machinery something it can never get from captures alone: *ground truth by construction* and unlimited strict-split diversity.
|
||||
|
||||
## 2. Decision — physics core
|
||||
|
||||
### 2.1 Rooms and materials (`room.rs`)
|
||||
|
||||
Shoebox rooms `[0,Lx]×[0,Ly]×[0,Lz]`, one wall material with **complex permittivity** `ε = ε_r − j·σ/(ωε₀)` and normal-incidence Fresnel reflection `Γ = (1−√ε)/(1+√ε)`. Presets (concrete/drywall/glass, ITU-R P.2040 ballpark) plus a perfect absorber for test isolation. Measured sanity: concrete at 2.4 GHz gives |Γ| ≈ 0.39–0.45 with phase inversion; |Γ| < 1 for all passive presets; ε_r = 1, σ = 0 gives Γ = 0 exactly. People are validated-in-room point scatterers with constant velocity and RCS.
|
||||
|
||||
### 2.2 Multipath (`raytrace.rs`)
|
||||
|
||||
Allen–Berkley image method, reflection order ≤ 2 (per-axis images `±x + 2nL`, bounce count `|2n|` / `|2n−1|`), plus single-bounce bistatic person scattering with amplitude `√(σ_rcs/4π)/(d₁·d₂)` (bistatic radar equation, amplitude form):
|
||||
|
||||
```text
|
||||
H(f) = Σ_paths Γ^order · (c/f)/(4π) · s_p · e^{−j2πf·d_p/c}
|
||||
```
|
||||
|
||||
**Doppler is never injected** — it emerges from the person's path length changing between snapshots.
|
||||
|
||||
**Physics gates (MEASURED-CODE):**
|
||||
|
||||
| Gate | Test | Result |
|
||||
|---|---|---|
|
||||
| Direct path ≡ Friis | `direct_path_is_exact_friis` | < 1e-15 per subcarrier (absorber walls) |
|
||||
| Reciprocity `H(a→b) = H(b→a)` | `channel_is_reciprocal` | < 1e-12, with person + concrete walls |
|
||||
| Image geometry | `first_order_reflection_matches_mirror_geometry` | floor/ceiling bounce at exactly the mirror distance; 1 direct + 6 first-order + second-order set |
|
||||
| Doppler | `moving_person_produces_the_analytic_doppler_phase_rate` | residual-phase rotation matches `−2πf·Δd/c` to < 1e-6 rad across 4 steps |
|
||||
|
||||
## 3. Decision — domain randomization (`generator.rs`)
|
||||
|
||||
Per room, seeded ChaCha20 (nvsim discipline — same seed ⇒ byte-identical corpus, cross-machine):
|
||||
|
||||
- **Physics**: dimensions 4–10 × 3–8 × 2.4–3.2 m; ε_r ∈ [2,7], σ ∈ [0.002,0.1] S/m; random TX/RX placements; person start/heading/speed/RCS.
|
||||
- **Hardware nuisances** (what breaks naive models in the field): per-room gain ×0.5–2, static phase offset, **CFO drift** ±0.3 rad/snapshot, thermal noise, 5 % packet loss (snapshot re-delivery), 3 % wideband interference bursts.
|
||||
- **Provenance for strict splits**: every window carries a full `PartitionKey` (room/day/person/chipset/firmware/layout) so ADR-273 §4 holdouts exist by construction.
|
||||
|
||||
Measured: byte-determinism per seed (and divergence across seeds); presence windows carry > 5× the temporal amplitude variance of empty windows (actual measured ratio on the test corpus is far higher); labels/keys complete.
|
||||
|
||||
The CFO nuisance earned its keep immediately: it *defeated the first tokenizer* (empty rooms looked like motion) and forced the CFO-alignment step now documented in ADR-274 §3.1 — exactly the class of failure a physics-parameter randomizer exists to surface before real deployments do.
|
||||
|
||||
## 4. What this generator is NOT
|
||||
|
||||
- Not a WaveVerse replacement: order-2 specular + point scatterers, no diffraction, no diffuse scattering, no angle-dependent Fresnel, no antenna patterns. These are refinements to add *when a P2 real-data gap analysis demands them*, not before.
|
||||
- Not evidence of real-world accuracy: the ADR-273 acceptance numbers on this data validate the pipeline; the synthetic→real transfer claim (HybridSim-style) is untested here and stays EXTERNAL-UNVERIFIED until P2 replay experiments.
|
||||
|
||||
## 5. Performance
|
||||
|
||||
Criterion (release): 1 room × 4 windows × 3 links generates in **3.1 ms** (≈ 260 µs/window) — corpus generation is never the bottleneck; the 8-room acceptance corpus builds in well under a second even in debug.
|
||||
|
||||
## 6. Consequences
|
||||
|
||||
- Every pipeline stage gains a deterministic, physics-proven test bed; regressions in adapters/tokenizer/encoder now fail loudly against ground truth.
|
||||
- Data scarcity stops gating architecture work: strict-split experiments (rooms/chipsets/layouts) run in CI.
|
||||
- The honest-labeling chain (`RfModality::Synthetic` → `Provenance.synthetic` → SYNTHETIC-graded ADR claims) is structural, not editorial.
|
||||
@@ -0,0 +1,52 @@
|
||||
# ADR-277: Edge sensing control plane — purposes, zones, retention, and a trust boundary raw RF cannot cross
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Accepted — **P1 implemented** (`ruview-unified/src/policy.rs`; 5 unit tests + the acceptance-pipeline export test) |
|
||||
| **Date** | 2026-07-26 |
|
||||
| **Parent** | ADR-273 |
|
||||
| **Relates to** | ADR-153 (802.11bf protocol model), ADR-141/120 (BFLD privacy control plane + privacy classes), ADR-262 §3.3 (RuField P0–P5 fail-closed mapping — the same philosophy, applied to sensing outputs), ADR-032 (mesh security hardening) |
|
||||
|
||||
## 0. PROOF discipline
|
||||
|
||||
Grades per ADR-273 §0. Standards status (EXTERNAL, checkable): IEEE 802.11bf-2025 published 2025-09; IEEE 802.11bk addresses ≤ 320 MHz positioning; ETSI published an ISAC architecture 2026-02 (monostatic/bistatic/multistatic/network/device sensing) followed by a security report identifying **19 privacy and security issue classes**; 3GPP Release 20 sensing studies are active. The OpenAirInterface SRS-xApp demo (0.12 m MAE under a **random** split) is EXTERNAL-UNVERIFIED and its split methodology is exactly the leakage ADR-273 §4 rejects — we cite the *implementation path*, not the number.
|
||||
|
||||
## 1. Context
|
||||
|
||||
Sensing purposes and sensing zones are becoming first-class authorization objects in the standards (802.11bf sensing sessions; ETSI ISAC purposes/exposure). Meanwhile the ETSI security report's issue classes make one thing clear: a sensing stack without a policy plane is a liability. RuView already fails closed at other boundaries (ADR-262 §3.3 maps privacy by information content, never byte value); this ADR gives sensing *outputs* the same discipline, on-device, before any transport.
|
||||
|
||||
## 2. Decision — three structural rules
|
||||
|
||||
### 2.1 Raw RF never leaves the trust boundary
|
||||
|
||||
The only exportable type is `BoundedEvent` — typed verdicts only (`Presence(bool)`, `ActivityClass(u8)`, `RespirationBpm(f64)`, `Location([f64;3])`, `AnomalyScore(f64)`). **No variant can carry RF samples, so raw CSI/radar export is unrepresentable, not merely forbidden**; `TrustBoundary::export` is the single egress and there is deliberately no API that serializes an `RfTensor` outward. External systems receive bounded events + uncertainty, never signal history.
|
||||
|
||||
### 2.2 Fail closed, everywhere
|
||||
|
||||
`PolicyEngine::authorize`: unknown zone ⇒ deny; purpose not granted in the zone ⇒ deny; **identity recognition is double-gated** — it must be in the zone's `allowed_purposes` *and* the zone must set `identity_explicitly_enabled` (either alone denies). Retention: an event older than the zone's `retention_s` at export time is dropped with a typed `PolicyDenied`. Tests cover every branch, including the manufactured cases (identity granted-but-not-enabled; enabled-but-not-granted; stale event).
|
||||
|
||||
### 2.3 Every output is accountable (ADR-273 acceptance item 8)
|
||||
|
||||
`BoundedEvent::new` is the only constructor and *fails* without: uncertainty ∈ [0,1], provenance (device + `synthetic` flag — the ADR-276 honest label survives export), a non-zero model version, timestamp, purpose, and zone. The acceptance test (`outputs_leave_only_through_the_policy_boundary_fully_attributed`) runs the full pipeline — synthetic world → encoder → presence head → event → export — and asserts the attribution and the denial of an ungranted purpose on the same zone.
|
||||
|
||||
## 3. Purpose taxonomy
|
||||
|
||||
`SensingPurpose`: `Presence, Activity, Vitals, Localization, PoseTracking, IdentityRecognition, ChannelDiagnostics` — deliberately aligned with the ETSI ISAC sensing-service classes and WLAN-sensing use cases so a future 802.11bf sensing-session negotiation or ISAC exposure API maps 1:1 onto zone grants. Person *identity* is additionally kept out of the ADR-275 scene graph by type (`EntityKind::PersonClass`, never a person id) — the graph cannot leak what it cannot store.
|
||||
|
||||
## 4. O-RAN / cellular path (roadmap, seams shipped)
|
||||
|
||||
The P1 control plane is transport-agnostic and already fronts the cellular seam:
|
||||
|
||||
- `CellularSrsAdapter` (`oai-srs-xapp`, ADR-274 §2.3) normalizes comb-sampled SRS frequency responses into the canonical tensor — the data-plane contract an OAI xApp needs.
|
||||
- P4 (ADR-273 §7) places the sensing application beside the DU for sub-ms I/Q–CSI–SRS access, with the xApp performing wider-area fusion; **every output of that path still exits through this ADR's `TrustBoundary`**, and its localization claims will be reported only under strict splits (the OAI demo's random split is the cautionary example, not the target).
|
||||
|
||||
## 5. Alternatives considered
|
||||
|
||||
- **Reuse BFLD's privacy classes directly** — rejected: BFLD (ADR-120) classifies *captures*; this plane authorizes *outputs by purpose and zone*. They compose (a BFLD-classified capture feeding a head still exits through `TrustBoundary`), and ADR-262's `map_privacy` remains the capture-side mapping.
|
||||
- **Config-file allow-lists without types** — rejected: the 19 ETSI issue classes are mostly "the code path existed" failures; unrepresentability beats configuration.
|
||||
|
||||
## 6. Consequences
|
||||
|
||||
- Enterprise/telecom conversations get a concrete artifact: a privacy manifest is a serialization of zones + purposes + retention (all types already `serde`).
|
||||
- Every future surface (sensing-server WS, RuField bridge, SRS xApp, MCP tools) must route sensing outputs through `TrustBoundary` — added to the pre-merge security-review checklist item 12.
|
||||
- Cost: purposes are coarse (no per-consumer grants yet); P3 adds consumer identity when the sensing-server wiring lands.
|
||||
@@ -0,0 +1,46 @@
|
||||
# ADR-278: Radar inverse rendering + differentiable RF SLAM — a gated research program, not a dependency
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| **Status** | Proposed — research program (deliberately **no code in P1**) |
|
||||
| **Date** | 2026-07-26 |
|
||||
| **Parent** | ADR-273 (pillar 6, score 3.6 — highest strategic value, highest hardware + reproduction risk) |
|
||||
| **Relates to** | ADR-275 (the Gaussian memory these methods would write into), ADR-263/264 (RTL8720F radar platform + wire protocol), ADR-021 (mmWave vitals hardware), ADR-276 (synthetic worlds as the reproduction sandbox) |
|
||||
|
||||
## 0. PROOF discipline
|
||||
|
||||
Everything numeric in this ADR is **EXTERNAL-UNVERIFIED** — reported by fresh papers/preprints that this repo has not reproduced. That is the point of this ADR: to fix the reproduction gates *before* any of these numbers are allowed to influence the roadmap as if they were ours.
|
||||
|
||||
## 1. Context — what the field reports (July 2026)
|
||||
|
||||
| System | Claim (theirs) | Availability | Risk read |
|
||||
|---|---|---|---|
|
||||
| **RISE** | Single static mmWave radar + multipath inversion → joint room layout + furniture; 16 cm scene Chamfer (baseline 40 cm), 58 % furniture IoU | code available | most reproducible; static sensor matches our appliance posture |
|
||||
| **DiffRadar** | Radar SLAM + Gaussian fields + differentiable rendering; 0.129 m vs 0.823 m ATE, 94.78 % vs 42.59 % map consistency, 70 fps, 40 MB maps | fresh preprint | treat as **reproduction target, not component** — numbers are single-team, single-venue |
|
||||
| **GeRaF** | Differentiable RF renderer + SDF + reflectivity, near-range reconstruction; ~32 h on one H100 for 50 k iterations | published setup | offline calibration / digital-twin tool only; unsuitable for continuous adaptation |
|
||||
|
||||
The strategic pull is real: all three converge on *inverse rendering into continuous scene representations* — exactly the ADR-275 memory. The risks are equally real: single-source numbers, mmWave hardware variance, and compute profiles (GeRaF) incompatible with edge deployment.
|
||||
|
||||
## 2. Decision
|
||||
|
||||
1. **No production dependency** on any of these systems or their claims. ADR-275's gain model + inverse update is the only RF-inverse machinery in the deployment path.
|
||||
2. **Reproduction order: RISE → DiffRadar → GeRaF-lite**, each on one controlled test site, each gated (§3) before the next starts. RISE first because a static radar matches the RuView appliance posture and its inversion writes naturally into `RfGaussian` (occupancy + reflectivity fields already exist for it).
|
||||
3. **Sandbox-first**: before hardware, each method's core inversion is exercised against ADR-276 synthetic worlds extended with a radar-cube output mode (the `FmcwRadarCube` adapter already normalizes such cubes), so failures separate into "our reimplementation" vs "their claim" cleanly.
|
||||
4. **Integration contract**: any reproduced system emits into `GaussianMap` via the existing primitive — no parallel scene store. SLAM trajectories, if any, become `Provenance`-stamped map updates subject to ADR-277 export rules like everything else.
|
||||
|
||||
## 3. Gates (each phase passes all or the program pauses)
|
||||
|
||||
| Gate | Threshold | Split discipline |
|
||||
|---|---|---|
|
||||
| G1 RISE-repro (synthetic) | layout Chamfer within 2× of paper's on our synthetic rooms | held-out room geometries |
|
||||
| G2 RISE-repro (one real site) | qualitative layout recovery + quantified Chamfer vs measured floor plan; report *our* number, whatever it is | site never used in tuning |
|
||||
| G3 DiffRadar-repro | ATE and map consistency on our trajectory rig; publish the delta vs paper | held-out trajectories |
|
||||
| G4 Edge viability | inversion or map-update loop ≤ 50 ms p95 on target hardware, or explicit reclassification as offline-calibration tooling (GeRaF's honest category) | — |
|
||||
|
||||
A gate failure is a *result*, recorded in this ADR's log — the program exists to convert EXTERNAL-UNVERIFIED into MEASURED, in either direction.
|
||||
|
||||
## 4. Consequences
|
||||
|
||||
- The roadmap cannot silently absorb preprint numbers; anything radar-inverse must pass through §3.
|
||||
- ADR-275's primitive already reserves the fields (per-band × angle reflectivity, occupancy, motion) these methods need, so a successful reproduction integrates without schema churn.
|
||||
- Cost of delay is accepted: pillar 6 scored lowest on readiness, and P1–P4 (encoder, memory, synth, control plane, SRS) do not depend on it.
|
||||
@@ -132,6 +132,12 @@ Statuses: **Proposed** (under discussion), **Accepted** (approved and/or impleme
|
||||
| [ADR-263](ADR-263-ruview-npm-harness-deep-review.md) | `@ruvnet/ruview` npm harness — deep review + optimization strategy | Proposed |
|
||||
| [ADR-264](ADR-264-rvagent-mcp-and-cli-npm-deep-review.md) | `@ruvnet/rvagent` MCP server + `@ruv/ruview-cli` — deep review + optimization strategy | Proposed |
|
||||
| [ADR-265](ADR-265-ruview-npm-distribution-strategy.md) | RuView npm distribution strategy — CI gate, provenance, version single-sourcing, namespace | Proposed |
|
||||
| [ADR-273](ADR-273-unified-rf-spatial-world-model.md) | Unified RF spatial world model — umbrella, anti-leakage protocol, acceptance gates | Accepted (P1 implemented) |
|
||||
| [ADR-274](ADR-274-universal-rf-encoder-adapter-registry.md) | Universal RF foundation encoder + hardware adapter registry | Accepted (P1 implemented) |
|
||||
| [ADR-275](ADR-275-rf-aware-gaussian-spatial-memory.md) | RF-aware Gaussian spatial memory | Accepted (P1 implemented) |
|
||||
| [ADR-276](ADR-276-physics-guided-synthetic-rf-worlds.md) | Physics-guided synthetic RF world generator | Accepted (P1 implemented) |
|
||||
| [ADR-277](ADR-277-edge-sensing-control-plane.md) | Edge sensing control plane (802.11bf / ETSI ISAC aligned) | Accepted (P1 implemented) |
|
||||
| [ADR-278](ADR-278-radar-inverse-rendering-research-program.md) | Radar inverse rendering + differentiable RF SLAM research program | Proposed |
|
||||
|
||||
---
|
||||
|
||||
|
||||
Reference in New Issue
Block a user