Merge e47c48e4e3 into 69e61e3437

Per the CLAUDE.md pre-merge checklist (item 5, "Add entry under
[Unreleased]"), several recently-merged PRs landed without CHANGELOG
entries. Backfilling the user/operator-facing ones — most importantly the
MAT triage safety fix:

- #926 (Security/safety): survivor with a heartbeat never triaged Deceased
- #918: per-node HA devices report each node's own presence/motion
- #919: actionable --model load diagnostic (refs #894)
- #920: --export-rvf no longer silently produces a placeholder model
- #929 (Security): bearer scheme matched case-insensitively (RFC 6750)

CI-internal fixes (#925 rust-cache, #930 SAST) are intentionally omitted —
they don't change product behavior. Docs-only.

.github/workflows/ci.yml

@@ -108,16 +108,18 @@ jobs:
     - name: Install Rust toolchain
       uses: dtolnay/rust-toolchain@stable
 
-    - name: Cache cargo
-      uses: actions/cache@v4
+    # Swatinem/rust-cache replaces a naive `actions/cache` of the whole
+    # `v2/target`. That manual cache of a 38-crate target dir (multi-GB) was an
+    # intermittent failure source — several CI runs this cycle died at the
+    # cache/setup step (after toolchain install, before "Run Rust tests"),
+    # needing a rerun. rust-cache is purpose-built for Rust: it caches the
+    # registry + git + a pruned target, evicts stale deps, and restores far more
+    # reliably (and faster) on large workspaces. `workspaces: v2` points it at
+    # the v2/ cargo workspace (keys on v2/Cargo.lock, caches v2/target).
+    - name: Cache cargo (Swatinem/rust-cache)
+      uses: Swatinem/rust-cache@v2
       with:
-        path: |
-          ~/.cargo/registry
-          ~/.cargo/git
-          v2/target
-        key: ${{ runner.os }}-cargo-${{ hashFiles('v2/Cargo.lock') }}
-        restore-keys: |
-          ${{ runner.os }}-cargo-
+        workspaces: v2
 
     - name: Run Rust tests
       working-directory: v2
@@ -265,23 +267,45 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements.txt
-        pip install locust
+        pip install pytest   # the perf suite is pytest, not locust
 
-    - name: Start application
-      working-directory: archive/v1
-      run: |
-        uvicorn src.api.main:app --host 0.0.0.0 --port 8000 &
-        sleep 10
+    # No "Start application" step: the gated test (test_frame_budget.py) drives
+    # the CSIProcessor pipeline in-process and makes no HTTP calls, so the old
+    # uvicorn server + `sleep 10` were dead weight — they only existed for the
+    # now-excluded api_throughput/inference_speed tests, and on every run dumped
+    # ~50 misleading "router requires hardware setup" ERROR lines for a server
+    # no test touched. MOCK_POSE_DATA is server-only and unused here.
 
     - name: Run performance tests
+      working-directory: archive/v1
       run: |
-        locust -f tests/performance/locustfile.py --headless --users 50 --spawn-rate 5 --run-time 60s --host http://localhost:8000
+        # Gate only on the genuine, deterministic perf guard:
+        # test_frame_budget.py times the *real* CSIProcessor pipeline against
+        # the ADR 50 ms per-frame budget (single-frame, p95 over 100 frames,
+        # +Doppler) — a true regression signal.
+        #
+        # test_api_throughput.py / test_inference_speed.py are excluded: every
+        # test there is a TDD red-phase stub (suffix `_should_fail_initially`)
+        # that times a *mock that sleeps* — meaningless as a perf signal, with
+        # machine-dependent wall-clock asserts (e.g. `actual_rps >= 40`,
+        # `batch_time < individual_time`) that are inherently flaky on shared
+        # CI runners, plus a cross-class fixture-scope bug. Forcing them green
+        # would be manufacturing a false signal; they stay in-repo for local
+        # TDD but do not gate CI until the underlying features are implemented.
+        #
+        # `python -m pytest` (not the bare `pytest` script) puts the cwd
+        # (archive/v1) on sys.path so `from src.core...` resolves — the bare
+        # script omits cwd and raises ModuleNotFoundError: No module named 'src'.
+        # -o addopts="" drops the root pyproject's --cov/--cov-fail-under=100.
+        python -m pytest tests/performance/test_frame_budget.py \
+          -o addopts="" -v --junitxml=perf-junit.xml
 
     - name: Upload performance results
+      if: always()
       uses: actions/upload-artifact@v4
       with:
         name: performance-results
-        path: locust_report.html
+        path: archive/v1/perf-junit.xml
 
   # Docker Build and Test
   # NOTE: the canonical Docker build for the sensing-server is now
@@ -367,6 +391,8 @@ jobs:
     runs-on: ubuntu-latest
     needs: [docker-build]
     if: github.ref == 'refs/heads/main'
+    permissions:
+      contents: write   # gh-pages deploy needs write (GITHUB_TOKEN is read-only by default -> 403)
     steps:
     - name: Checkout code
       uses: actions/checkout@v4
@@ -384,6 +410,8 @@ jobs:
 
     - name: Generate OpenAPI spec
       working-directory: archive/v1
+      env:
+        MOCK_POSE_DATA: "true"   # no CSI hardware in CI
       run: |
         python -c "
         from src.api.main import app
@@ -394,6 +422,7 @@ jobs:
 
     - name: Deploy to GitHub Pages
       uses: peaceiris/actions-gh-pages@v4
+      continue-on-error: true   # openapi generation above is the real validation; deploy is best-effort (Pages may be disabled)
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
         publish_dir: ./docs

.github/workflows/security-scan.yml

@@ -46,7 +46,10 @@ jobs:
 
     - name: Run Bandit security scan
       run: |
-        bandit -r src/ -f sarif -o bandit-results.sarif
+        # The Python codebase lives under archive/v1/src (it moved there when
+        # the runtime was rewritten in Rust). Scanning `src/` matched nothing,
+        # so this SAST step was a silent no-op.
+        bandit -r archive/v1/src/ -f sarif -o bandit-results.sarif
       continue-on-error: true
 
     - name: Upload Bandit results to GitHub Security
@@ -57,22 +60,20 @@ jobs:
         sarif_file: bandit-results.sarif
         category: bandit
 
-    - name: Run Semgrep security scan
-      continue-on-error: true
-      uses: returntocorp/semgrep-action@v1
-      with:
-        config: >-
-          p/security-audit
-          p/secrets
-          p/python
-          p/docker
-          p/kubernetes
-      env:
-        SEMGREP_APP_TOKEN: ${{ secrets.SEMGREP_APP_TOKEN }}
-        
-    - name: Generate Semgrep SARIF
+    # Removed the deprecated `returntocorp/semgrep-action@v1` step: it was
+    # redundant (the pip `semgrep --sarif` below is what feeds GitHub Security;
+    # the action only pushed to the Semgrep cloud app via SEMGREP_APP_TOKEN) and
+    # it pulled `returntocorp/semgrep-agent:v1` from Docker Hub on every run,
+    # which intermittently timed out and turned this check red. The pip semgrep
+    # (installed above) needs no Docker pull. The action's `p/docker` +
+    # `p/kubernetes` rulesets are folded into the command below so coverage is
+    # preserved.
+    - name: Run Semgrep + generate SARIF
       run: |
-        semgrep --config=p/security-audit --config=p/secrets --config=p/python --sarif --output=semgrep.sarif src/
+        semgrep \
+          --config=p/security-audit --config=p/secrets --config=p/python \
+          --config=p/docker --config=p/kubernetes \
+          --sarif --output=semgrep.sarif archive/v1/src/
       continue-on-error: true
 
     - name: Upload Semgrep results to GitHub Security

CHANGELOG.md

@@ -12,6 +12,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - **MQTT multi-node deployments now create one Home-Assistant device per node — closes #898.** After the #872 MQTT wiring landed, the JSON→`VitalsSnapshot` bridge hard-coded a single `node_id` (the MQTT client id) and the publisher used a single `OwnedDiscoveryBuilder`, so every physical node collapsed into one device (`identifiers:["wifi_densepose_wifi-densepose-1"]`), contradicting the "one device per node" docs. The bridge now emits one snapshot per node in the sensing update's `nodes[]` (each with its own `node_id` + RSSI, falling back to a single aggregate snapshot for wifi/simulate sources), and the publisher derives a per-node builder (`OwnedDiscoveryBuilder::for_node`) that publishes discovery + availability lazily on first sight of each `node_id` and routes state to per-node topics — yielding N distinct HA devices with per-node availability/LWT. Unit-tested (distinct nodes → distinct `wifi_densepose_<node>` identifiers); 71 MQTT tests pass.
 - **Person count no longer pinned to 1 — addresses #803.** The aggregate occupancy reported by the sensing server was derived from `smoothed_person_score`, an EMA-smoothed *activity* score (amplitude variance / motion / spectral energy). That score saturates near a single occupant — one moving person maxes it out — so it cannot discriminate occupancy *count* and stayed clamped at 1 across S3/C6 and the Python/Docker/Rust servers. Meanwhile the count-aware per-node estimates the ESP32 paths already compute (firmware `n_persons`, and the DynamicMinCut `corr_persons`) were stashed in `NodeState::prev_person_count` and then **discarded** by the aggregator (same dead-wiring class as #872). The aggregator now takes `max(activity_count, node_max)` via a unit-tested `aggregate_person_count` helper, so a node positively estimating 2–3 occupants is surfaced instead of overwritten. The fix can only ever *raise* the count when a node reports more people, so the single-occupant case is provably never inflated (regression-guarded by test). **Second half:** the pure-CSI per-node path itself clamped its own estimate — the DynamicMinCut occupancy (`estimate_persons_from_correlation`, 0–3) was mapped to a score via `corr_persons / 3.0`, putting 2 people at 0.667, *just under* the 0.70 up-threshold of `score_to_person_count`, so the per-node count never climbed past 1 (so `node_max` was also stuck at 1 for CSI-only nodes). Replaced it with a threshold-aligned `corr_persons_to_score` mapping (1→0.40, 2→0.74, 3→0.96) whose steady state round-trips back to the same count through the EMA + hysteresis, while still gating transient noise. A convergence test replays the exact EMA loop to prove min-cut=2 now reports 2 (and documents that the old `/3.0` mapping reported 1). Full multi-person accuracy still depends on the underlying estimator quality; this removes the two server-side clamps that masked it. 586 sensing-server tests pass.
 - **MQTT publisher now actually runs (`--mqtt`) — closes #872.** The `--mqtt*` flags were defined only in `cli::Args` (dead code, referenced nowhere) while the binary parses a *separate* `main::Args` with no mqtt fields, and `main.rs` never started the `mqtt::` publisher — so MQTT/Home-Assistant integration was completely unwired (`--mqtt` errored as an unexpected argument, and even with the Docker image's `--features mqtt` build the publisher never ran). Earlier attempts chased a Docker *rebuild*; the real cause was disconnected *code*. Extracted the flags into a shared `cli::MqttArgs` (`#[command(flatten)]` into both structs), spawn the publisher on `--mqtt`, and bridge the JSON sensing broadcast into the typed `VitalsSnapshot` stream with a defensive `serde_json::Value` mapping. Verified end-to-end against `mosquitto`: 20 HA auto-discovery entities + live state (presence/person-count/…). 577 (default) / 580 (`--features mqtt`) tests pass.
+- **Mass Casualty triage never reports a survivor with a heartbeat as Deceased (safety) — PR #926.** Both triage paths in `wifi-densepose-mat` — `TriageCalculator::calculate` (`combine_assessments(Absent, None) ⇒ Deceased`) and the detection path `EnsembleClassifier::determine_triage` (`!has_breathing && !has_movement ⇒ Deceased`) — ignored the `heartbeat` field. A survivor with a detectable **pulse** but no sensed breathing/movement (respiratory arrest — the most time-critical *savable* state, Immediate/Red) was therefore reported **Deceased (Black)** and deprioritized for rescue. The domain path was in fact only reachable *because* a heartbeat made `has_vitals()` true, so every "Deceased" was a live person. Both paths now escalate to **Immediate** when a heartbeat is present; total absence of breathing, movement *and* heartbeat is unchanged (domain → `Unknown`, ensemble → `Deceased`). 2 safety regression tests; full MAT suite (177) green.
+- **Per-node Home-Assistant devices now report each node's *own* presence/motion — PR #918.** After the one-device-per-node fan-out landed, the MQTT bridge still applied the *room-level aggregate* `classification` to every node, so in a multi-node deployment a node watching an empty corner inherited another node's "present" (and `motion_level: "absent"` was mis-mapped to full motion). Each node in the broadcast `nodes[]` already carries its own `classification`; the bridge now reads it per node (extracted into a testable `vitals_snapshots_from_sensing_json`), keeping vitals + person count room-level. 4 unit tests.
+- **`--model` gives an actionable diagnostic instead of a cryptic magic error — PR #919 (refs #894).** Passing a HuggingFace `ruvnet/wifi-densepose-pretrained` file (`model.safetensors` / `model-q4.bin` / `model.rvf.jsonl`) to `--model` produced `invalid magic at offset 0: … got 0x77455735`, then a silent fall back to heuristics. The load-failure path now detects the format (safetensors / quantized blob / JSONL manifest) and explains that those files are a different format **and** encoder architecture than the RVF binary container the progressive loader expects, pointing to #894. Pure `diagnose_model_load_error` + 4 tests.
+- **`--export-rvf` no longer silently produces a placeholder model — PR #920.** The `--export-rvf` handler ran *before* `--train`/`--pretrain` and unconditionally wrote placeholder sine-wave weights, so the documented `--train … --export-rvf <path>` workflow short-circuited to a fake model and never trained (while printing "exported successfully"). It now emits the placeholder **container-format demo** only standalone (with a clear warning), and falls through to real training when `--train`/`--pretrain` is set; docs point to `--save-rvf` for the real model. 3 guard tests.
 
 ### Added
 - **WiFi-CSI pose: efficiency frontier + per-room calibration service** (ADR-150 §3.2–3.6). Two beyond-SOTA results on the MM-Fi benchmark, plus the deployment mechanism that resolves real-world generalization:
@@ -33,6 +37,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Security
 - **ESP32 OTA upload now fails closed when no PSK is provisioned** (#596 audit finding — critical, **breaking change for unprovisioned nodes**). `ota_check_auth()` previously returned `true` when `s_ota_psk[0] == '\0'`, so a freshly-flashed node would accept attacker-controlled firmware over plain HTTP on port 8032 from any host on the WiFi. No Secure Boot V2, no signed-image verification — a single LAN call could brick or backdoor a node. The fix rejects every OTA upload until a PSK is written to NVS (the OTA HTTP server still starts so operators can run `provision.py --ota-psk <hex>` over USB-CDC without reflashing). **Operators affected**: any deployment that relied on the unauthenticated OTA endpoint working out of the box now needs to provision a PSK before subsequent OTA pushes will succeed. Boot-time `ESP_LOGW` makes the new posture visible.
+- **Bearer-token auth accepts the scheme case-insensitively (RFC 6750) — PR #929.** `require_bearer` parsed the `Authorization` header with a case-sensitive `strip_prefix("Bearer ")`, so a *correct* `RUVIEW_API_TOKEN` sent as `Authorization: bearer <token>` (or `BEARER`, or with extra whitespace) was rejected with a confusing 401 — needless friction when enabling auth. The scheme is now matched with `eq_ignore_ascii_case` (per RFC 6750 §2.1 / RFC 7235 §2.1); the token compare is unchanged — still exact and constant-time (`ct_eq`) — so a wrong token or a non-Bearer scheme (`Basic …`) still returns 401. Audited the surrounding code while here: `ct_eq` correctly rejects length mismatch (no prefix-auth bypass) and the middleware fails closed. New `accepts_case_insensitive_bearer_scheme` test.
 - **Path-traversal vulnerabilities patched in five sensing-server endpoints** (closes #615 — critical). New `wifi_densepose_sensing_server::path_safety::safe_id()` enforces `[A-Za-z0-9._-]` only (no leading `.`, max 64 chars) before any user-controlled identifier reaches a `format!()` building a filesystem path. Applied at:
   - `POST /api/v1/recording/start` (`recording.rs` — `session_name`)
   - `GET /api/v1/recording/download/:id` (`recording.rs` — `id`)
@@ -430,7 +435,7 @@ Model release (no new firmware binary). Firmware remains at v0.6.0-esp32.
 - Security fix merged via PR #310.
 
 ### Performance
-- Presence detection: 100% accuracy on 60,630 overnight samples.
+- Presence detection: 100% accuracy on 60,630 overnight samples. *(Retracted — that recording was single-class (one sleeping person, 6,062/6,063 frames "present"), so a constant "yes" scores ~99.98%. Superseded by the honest 82.3% held-out temporal-triplet metric; see [#882](https://github.com/ruvnet/RuView/issues/882). Kept here as the in-place public record.)*
 - Inference: 0.008 ms per sample, 164K embeddings/sec.
 - Contrastive self-supervised training: 51.6% improvement over baseline.
 

archive/v1/src/services/pose_service.py

@@ -107,16 +107,25 @@ class PoseService:
     async def _initialize_models(self):
         """Initialize neural network models."""
         try:
-            # Initialize DensePose model
+            # Initialize DensePose model. DensePoseHead requires a config
+            # dict — input_channels matches the modality translator's output
+            # (256), with the standard DensePose 24 body parts and 2 (U,V)
+            # coordinates. (Previously called with no args → TypeError at
+            # startup, which broke the API service.)
+            densepose_config = {
+                'input_channels': 256,
+                'num_body_parts': 24,
+                'num_uv_coordinates': 2,
+            }
             if self.settings.pose_model_path:
-                self.densepose_model = DensePoseHead()
+                self.densepose_model = DensePoseHead(densepose_config)
                 # Load model weights if path is provided
                 # model_state = torch.load(self.settings.pose_model_path)
                 # self.densepose_model.load_state_dict(model_state)
                 self.logger.info("DensePose model loaded")
             else:
                 self.logger.warning("No pose model path provided, using default model")
-                self.densepose_model = DensePoseHead()
+                self.densepose_model = DensePoseHead(densepose_config)
             
             # Initialize modality translation
             config = {

docs/readme-details.md

@@ -122,7 +122,7 @@ node scripts/benchmark-ruvllm.js --model models/csi-ruvllm       # benchmark
 
 | What we measured | Result | Why it matters |
 |-----------------|--------|---------------|
-| **Presence detection** | **100% accuracy** | Never misses a person, never false alarms |
+| **CSI embedding quality** | **82.3% held-out temporal-triplet** | Honest label-free metric on the last 20% by time (v1's "100% presence" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) |
 | **Inference speed** | **0.008 ms** per embedding | 125,000x faster than real-time |
 | **Throughput** | **164,183 embeddings/sec** | One Mac Mini handles 1,600+ ESP32 nodes |
 | **Contrastive learning** | **51.6% improvement** | Strong pattern learning from real overnight data |
@@ -233,7 +233,7 @@ python firmware/esp32-csi-node/provision.py --port COM9 --hop-channels "1,6,11"
 | **kNN similarity search** | "Find the 10 most similar states to right now" — anomaly detection, fingerprinting | Cognitum Seed |
 | **Witness chain** | SHA-256 tamper-evident audit trail for every measurement (1,747 entries validated) | Cognitum Seed |
 | **Camera-free pose training** | 17 COCO keypoints from 10 sensor signals — PIR, RSSI triangulation, subcarrier asymmetry, vibration, BME280 | 2x ESP32 + Seed |
-| **Pre-trained model** | 82.8 KB (8 KB at 4-bit quantization), 100% presence accuracy, 0 skeleton violations | Download from release |
+| **Pre-trained model** | 82.8 KB (8 KB at 4-bit quantization), 82.3% held-out temporal-triplet accuracy (v1's "100% presence" was single-class — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) | Download from release |
 | **Sub-ms inference** | 0.012 ms latency, 171,472 embeddings/sec on M4 Pro | Any machine with Node.js |
 | **SONA adaptation** | Adapts to new rooms in <1ms without retraining | ruvllm runtime |
 | **LoRA room adapters** | Per-node fine-tuning with 2,048 parameters per adapter | Automatic |
@@ -262,7 +262,7 @@ node scripts/benchmark-ruvllm.js --model models/csi-ruvllm
 
 | What we measured | Result | Why it matters |
 |-----------------|--------|---------------|
-| **Presence detection** | **100% accuracy** | Never misses a person, never false alarms |
+| **CSI embedding quality** | **82.3% held-out temporal-triplet** | Honest label-free metric (v1's "100% presence" was single-class — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) |
 | **Person counting** | **24/24 correct** (MinCut) | Fixed the #1 user-reported issue |
 | **Inference speed** | **0.012 ms** per embedding | 83,000x faster than real-time |
 | **Throughput** | **171,472 embeddings/sec** | One Mac Mini handles 1,700+ ESP32 nodes |

docs/user-guide.md

@@ -1048,7 +1048,7 @@ The Rust sensing server binary accepts the following flags:
 | `--dataset` | (none) | Path to dataset directory (MM-Fi or Wi-Pose) |
 | `--dataset-type` | `mmfi` | Dataset format: `mmfi` or `wipose` |
 | `--epochs` | `100` | Training epochs |
-| `--export-rvf` | (none) | Export RVF model container and exit |
+| `--export-rvf` | (none) | Export a **placeholder** RVF container-format demo and exit — **not a trained model**. For a real model use `--train` (+ `--save-rvf`) or download a pretrained encoder. |
 | `--save-rvf` | (none) | Save model state to RVF on shutdown |
 | `--model` | (none) | Load a trained `.rvf` model for inference |
 | `--load-rvf` | (none) | Load model config from RVF container |
@@ -1119,7 +1119,7 @@ What it ships (and what it does not):
 
 | Capability | Status |
 |------------|--------|
-| Presence detection (occupied / empty) | ✅ Trained head — 100% accuracy on validation |
+| Presence detection (occupied / empty) | ✅ Trained head — v2 encoder reports 82.3% held-out temporal-triplet acc (v1's "100% on validation" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) |
 | 128-dim CSI embeddings (re-ID, similarity, downstream training) | ✅ Trained encoder |
 | Single-person breathing / heart-rate | ⚠️ Server still uses heuristic DSP — model does not replace this yet |
 | 17-keypoint full-body pose | 🔬 No keypoint weights shipped yet — pose pipeline runs but without a learned head |
@@ -1359,7 +1359,7 @@ docker run --rm \
   -v $(pwd)/output:/output \
   --entrypoint /app/sensing-server \
   ruvnet/wifi-densepose:latest \
-  --train --dataset /data --epochs 100 --export-rvf /output/model.rvf
+  --train --dataset /data --epochs 100 --save-rvf /output/model.rvf
 ```
 
 The pipeline runs 10 phases:
@@ -1824,7 +1824,7 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/pre
 #   model.safetensors    — 48 KB contrastive encoder
 #   model-q4.bin         — 8 KB quantized (recommended)
 #   model-q2.bin         — 4 KB ultra-compact (ESP32 edge)
-#   presence-head.json   — presence detection head (100% accuracy)
+#   presence-head.json   — presence detection head (v2 encoder: 82.3% held-out triplet acc)
 #   node-1.json          — LoRA adapter for room 1
 #   node-2.json          — LoRA adapter for room 2
 ```
@@ -1833,7 +1833,7 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/pre
 
 The pre-trained encoder converts 8-dim CSI feature vectors into 128-dim embeddings. These embeddings power all 17 sensing applications:
 
-- **Presence detection** — 100% accuracy, never misses, never false alarms
+- **Presence detection** — v2 encoder: 82.3% held-out temporal-triplet accuracy (v1's "100%" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882))
 - **Environment fingerprinting** — kNN search finds "states like this one"
 - **Anomaly detection** — embeddings that don't match known clusters = anomaly
 - **Activity classification** — different activities cluster in embedding space

firmware/esp32-csi-node/release_bins/c6-adr110/SHA256SUMS.txt

@@ -1,4 +1,4 @@
-889715e9d698ad78f9978ad8b93b6af24a726b0494247201c8f0d920d9fc80ca *firmware/esp32-csi-node/release_bins/c6-adr110/bootloader.bin
-d8539e47c6f10a3344679118619e3fe01cfd66eb560ea8883268ca7c9a12efa4 *firmware/esp32-csi-node/release_bins/c6-adr110/esp32-csi-node.bin
+b0fb1f217a39c80bc95b5eb8208a0b8572ae64efa0f6d580b76caff4affe0f4d *firmware/esp32-csi-node/release_bins/c6-adr110/bootloader.bin
+4764c5b20a353895f70122816adc98f861ec20e9a8ea9b344dc0648b6341073c *firmware/esp32-csi-node/release_bins/c6-adr110/esp32-csi-node.bin
 7d2c7ac4888bfd75cd5f56e8d61f69595121183afc81556c876732fd3782c62f *firmware/esp32-csi-node/release_bins/c6-adr110/ota_data_initial.bin
 4c2cc4ffd52641e23b779bd57b3908014083ac3c1aab395756478c89e70d81f0 *firmware/esp32-csi-node/release_bins/c6-adr110/partition-table.bin

firmware/esp32-csi-node/release_bins/s3-adr110/SHA256SUMS.txt

@@ -1,3 +1,3 @@
-3c4905dd202ccabf4230cbabcc9320f250a60b1a7254eff7424780201bcb2072 *firmware/esp32-csi-node/release_bins/s3-adr110/bootloader.bin
-7a8bf9582c9031fed32f1ada44f5c41dd99bd07fadff8e5c86e07aa0f343e847 *firmware/esp32-csi-node/release_bins/s3-adr110/esp32-csi-node.bin
+b973d7eda65affb746adcfa63ceb18f779f206d240b76f01b8c9ae7485455660 *firmware/esp32-csi-node/release_bins/s3-adr110/bootloader.bin
+e21ef94aba779d534dc048c1b9da731c81e5dbe09d0645cfd70a05ad3642d3e9 *firmware/esp32-csi-node/release_bins/s3-adr110/esp32-csi-node.bin
 67222c257c0477501fd4002275638dc4262b34eb68235b8289fb1337054d322b *firmware/esp32-csi-node/release_bins/s3-adr110/partition-table.bin

firmware/esp32-csi-node/release_bins/version.txt

@@ -1,3 +1,4 @@
-0.6.6
-git-sha: cbcb389cb (pre-commit)
-built: 2026-05-21
+0.6.7
+git-sha: 8703ade9b
+built: 2026-06-02
+note: RuView#893 — display-less boards capture DATA frames (CSI yield 0pps fix); hardware-verified on ESP32-C6 (0->27 pps)

requirements.txt

@@ -36,3 +36,4 @@ scikit-learn>=1.2.0
 
 # Monitoring dependencies
 prometheus-client>=0.16.0
+psutil>=5.9.0  # system metrics — imported by health.py / metrics.py / status.py / monitoring.py

v2/crates/wifi-densepose-mat/src/detection/ensemble.rs

@@ -172,6 +172,14 @@ impl EnsembleClassifier {
         let has_movement = reading.movement.movement_type != MovementType::None;
 
         if !has_breathing && !has_movement {
+            // SAFETY: a detectable heartbeat means the survivor is ALIVE. No
+            // sensed breathing/movement *with* a pulse is respiratory arrest —
+            // the most time-critical savable state (Immediate), never Deceased.
+            // Only the total absence of breathing, movement AND heartbeat is
+            // reported Deceased.
+            if reading.heartbeat.is_some() {
+                return TriageStatus::Immediate;
+            }
             return TriageStatus::Deceased;
         }
 
@@ -295,6 +303,27 @@ mod tests {
         assert_eq!(result.recommended_triage, TriageStatus::Deceased);
     }
 
+    /// SAFETY regression: heartbeat present but no sensed breathing/movement is
+    /// respiratory arrest — Immediate, never Deceased. Only the *total* absence
+    /// of breathing, movement AND heartbeat (the test above) is Deceased.
+    #[test]
+    fn test_heartbeat_with_no_breathing_or_movement_is_immediate() {
+        // breathing: None, heartbeat: Some(72 bpm), movement: None
+        let reading = make_reading(None, Some(72.0), MovementType::None);
+
+        let classifier = EnsembleClassifier::new(EnsembleConfig {
+            min_ensemble_confidence: 0.0,
+            ..EnsembleConfig::default()
+        });
+
+        let result = classifier.classify(&reading);
+        assert_eq!(
+            result.recommended_triage,
+            TriageStatus::Immediate,
+            "a survivor with a pulse must never be triaged Deceased"
+        );
+    }
+
     #[test]
     fn test_ensemble_confidence_weighting() {
         let classifier = EnsembleClassifier::new(EnsembleConfig {

v2/crates/wifi-densepose-mat/src/domain/triage.rs

@@ -104,7 +104,20 @@ impl TriageCalculator {
         let movement_status = Self::assess_movement(vitals);
 
         // Step 4: Combine assessments
-        Self::combine_assessments(breathing_status, movement_status)
+        let status = Self::combine_assessments(breathing_status, movement_status);
+
+        // Step 5: SAFETY OVERRIDE — a detectable heartbeat means the survivor is
+        // ALIVE. `combine_assessments` only sees breathing + movement, so a
+        // person with a pulse but no *sensed* breathing/movement (respiratory
+        // arrest, or breathing too shallow for CSI to pick up) would otherwise
+        // be reported Deceased and deprioritized for rescue. No breathing + a
+        // pulse is the most time-critical *savable* state, so escalate to
+        // Immediate rather than ever calling a survivor with a heartbeat dead.
+        if status == TriageStatus::Deceased && vitals.heartbeat.is_some() {
+            return TriageStatus::Immediate;
+        }
+
+        status
     }
 
     /// Assess breathing status
@@ -217,7 +230,9 @@ enum MovementAssessment {
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::domain::{BreathingPattern, ConfidenceScore, MovementProfile};
+    use crate::domain::{
+        BreathingPattern, ConfidenceScore, HeartbeatSignature, MovementProfile, SignalStrength,
+    };
     use chrono::Utc;
 
     fn create_vitals(
@@ -233,6 +248,29 @@ mod tests {
         }
     }
 
+    /// SAFETY regression: a survivor with a detectable heartbeat but no sensed
+    /// breathing or movement is in respiratory arrest — Immediate (Red), and
+    /// must NEVER be reported Deceased. (Before the fix, `combine_assessments`
+    /// ignored heartbeat and returned Deceased; that path was in fact only
+    /// reachable *because* a heartbeat made `has_vitals()` true.)
+    #[test]
+    fn heartbeat_with_no_breathing_or_movement_is_immediate_not_deceased() {
+        let vitals = VitalSignsReading {
+            breathing: None,
+            heartbeat: Some(HeartbeatSignature {
+                rate_bpm: 72.0,
+                variability: 0.1,
+                strength: SignalStrength::Moderate,
+            }),
+            movement: MovementProfile::default(),
+            timestamp: Utc::now(),
+            confidence: ConfidenceScore::new(0.8),
+        };
+        let status = TriageCalculator::calculate(&vitals);
+        assert_eq!(status, TriageStatus::Immediate, "pulse present ⇒ alive");
+        assert_ne!(status, TriageStatus::Deceased);
+    }
+
     #[test]
     fn test_no_vitals_is_unknown() {
         let vitals = create_vitals(None, MovementProfile::default());

v2/crates/wifi-densepose-sensing-server/src/bearer_auth.rs

@@ -100,7 +100,17 @@ pub async fn require_bearer(
         .headers()
         .get(AUTHORIZATION)
         .and_then(|v| v.to_str().ok())
-        .and_then(|s| s.strip_prefix("Bearer "));
+        // RFC 6750 §2.1 / RFC 7235 §2.1: the auth-scheme ("Bearer") is
+        // case-insensitive. Match it as such (and tolerate extra leading
+        // whitespace before the token) so a correct token isn't rejected
+        // just because a client sent `bearer`/`BEARER`. The token compare
+        // below stays exact + constant-time.
+        .and_then(|s| {
+            let (scheme, token) = s.split_once(' ')?;
+            scheme
+                .eq_ignore_ascii_case("Bearer")
+                .then(|| token.trim_start())
+        });
     let ok = supplied
         .map(|s| ct_eq(s.as_bytes(), expected.as_bytes()))
         .unwrap_or(false);
@@ -185,6 +195,31 @@ mod tests {
         );
     }
 
+    #[tokio::test]
+    async fn accepts_case_insensitive_bearer_scheme() {
+        // RFC 6750 §2.1 / RFC 7235 §2.1: the auth-scheme is case-insensitive.
+        // A correct token must authenticate regardless of scheme casing or
+        // extra whitespace; a wrong token must still be rejected.
+        async fn req_status(auth_value: &str) -> StatusCode {
+            let r = wrap(AuthState::from_token("s3cr3t"));
+            let mut req = Request::builder()
+                .method("GET")
+                .uri("/api/v1/info")
+                .body(Body::empty())
+                .unwrap();
+            req.headers_mut()
+                .insert(AUTHORIZATION, auth_value.parse().unwrap());
+            r.oneshot(req).await.unwrap().status()
+        }
+        assert_eq!(req_status("Bearer s3cr3t").await, StatusCode::OK);
+        assert_eq!(req_status("bearer s3cr3t").await, StatusCode::OK);
+        assert_eq!(req_status("BEARER s3cr3t").await, StatusCode::OK);
+        assert_eq!(req_status("Bearer  s3cr3t").await, StatusCode::OK); // extra space
+        // Scheme leniency must NOT weaken the token check.
+        assert_eq!(req_status("bearer nope").await, StatusCode::UNAUTHORIZED);
+        assert_eq!(req_status("Basic s3cr3t").await, StatusCode::UNAUTHORIZED);
+    }
+
     #[tokio::test]
     async fn enabled_blocks_api_with_wrong_bearer() {
         let r = wrap(AuthState::from_token("s3cr3t"));

v2/crates/wifi-densepose-sensing-server/src/main.rs

@@ -5476,6 +5476,159 @@ async fn broadcast_tick_task(state: SharedState, tick_ms: u64) {
     }
 }
 
+/// Map one sensing-broadcast JSON document into the `VitalsSnapshot`(s) to
+/// publish over MQTT (issues #872/#898).
+///
+/// Multi-node sources carry a `nodes` array where **each node has its own
+/// `classification`** (`motion_level`, `presence`, `confidence`) and RSSI — so
+/// each node must surface its *own* presence/motion, not the room-level
+/// aggregate. Previously the bridge applied the aggregate `classification` to
+/// every per-node Home-Assistant device, so a node in an empty corner inherited
+/// another node's "present" (and `motion_level: "absent"` was mis-mapped to full
+/// motion). Vitals (breathing / heart rate) and the person count are room-level
+/// and shared across the per-node devices. Falls back to a single aggregate
+/// snapshot when there is no per-node data (e.g. wifi / simulate sources).
+#[cfg(feature = "mqtt")]
+fn vitals_snapshots_from_sensing_json(
+    v: &serde_json::Value,
+    base_id: &str,
+) -> Vec<wifi_densepose_sensing_server::mqtt::state::VitalsSnapshot> {
+    use wifi_densepose_sensing_server::mqtt::state::VitalsSnapshot;
+
+    // motion_level string -> motion scalar. "absent"/"none"/"still"/"idle"/""
+    // are non-moving; anything else (walking, …) is motion. `fallback` is used
+    // when the field is absent so a partial per-node payload defers to the
+    // room aggregate rather than silently reading 0.
+    fn motion_of(level: Option<&str>, fallback: f64) -> f64 {
+        match level {
+            Some("none") | Some("still") | Some("idle") | Some("absent") | Some("") => 0.0,
+            Some(_) => 1.0,
+            None => fallback,
+        }
+    }
+
+    let ts = (v["timestamp"].as_f64().unwrap_or(0.0) * 1000.0) as i64;
+    let vit = &v["vital_signs"];
+    let breathing = vit["breathing_rate_bpm"].as_f64();
+    let hr = vit["heart_rate_bpm"].as_f64();
+    let n_persons = v["persons"]
+        .as_array()
+        .map(|a| a.len() as u32)
+        .or_else(|| v["estimated_persons"].as_u64().map(|x| x as u32))
+        .unwrap_or(0);
+
+    // Room-level aggregate: the no-nodes fallback, and the per-node default for
+    // any field a node omits.
+    let acls = &v["classification"];
+    let agg_presence = acls["presence"].as_bool().unwrap_or(false);
+    let agg_motion = motion_of(acls["motion_level"].as_str(), 0.0);
+    let agg_conf = acls["confidence"].as_f64().unwrap_or(0.0);
+
+    let mk = |node_id: String, presence: bool, motion: f64, conf: f64, rssi: Option<f64>| {
+        VitalsSnapshot {
+            node_id,
+            timestamp_ms: ts,
+            presence,
+            motion,
+            presence_score: if presence { conf.max(0.0) } else { 0.0 },
+            breathing_rate_bpm: breathing,
+            heartrate_bpm: hr,
+            n_persons,
+            rssi_dbm: rssi,
+            vital_confidence: conf,
+            ..Default::default()
+        }
+    };
+
+    match v["nodes"].as_array() {
+        Some(arr) if !arr.is_empty() => arr
+            .iter()
+            .map(|node| {
+                let n = node["node_id"].as_u64().unwrap_or(0);
+                // Each node carries its OWN classification — use it, deferring to
+                // the room aggregate only for fields the node omits.
+                let ncls = &node["classification"];
+                let presence = ncls["presence"].as_bool().unwrap_or(agg_presence);
+                let motion = motion_of(ncls["motion_level"].as_str(), agg_motion);
+                let conf = ncls["confidence"].as_f64().unwrap_or(agg_conf);
+                mk(
+                    format!("{base_id}-node{n}"),
+                    presence,
+                    motion,
+                    conf,
+                    node["rssi_dbm"].as_f64(),
+                )
+            })
+            .collect(),
+        _ => vec![mk(
+            base_id.to_string(),
+            agg_presence,
+            agg_motion,
+            agg_conf,
+            v["nodes"][0]["rssi_dbm"].as_f64(),
+        )],
+    }
+}
+
+/// Turn a `ProgressiveLoader::new` failure into an actionable diagnostic (#894).
+///
+/// The published HuggingFace `ruvnet/wifi-densepose-pretrained` files
+/// (`model.safetensors`, `model-q{2,4,8}.bin`, `model.rvf.jsonl`) are a
+/// different *format* — and a different encoder architecture — than the RVF
+/// binary container the `--model` progressive loader expects (`RVFS` magic
+/// `0x52564653`). Feeding one to `--model` produced a bare
+/// "invalid magic at offset 0 …" that left users stuck. Detect the common
+/// cases and explain plainly what's loadable instead.
+fn diagnose_model_load_error(path: &std::path::Path, data: &[u8], err: &str) -> String {
+    let name = path
+        .file_name()
+        .and_then(|n| n.to_str())
+        .unwrap_or("")
+        .to_ascii_lowercase();
+    let ext = path
+        .extension()
+        .and_then(|e| e.to_str())
+        .unwrap_or("")
+        .to_ascii_lowercase();
+
+    // safetensors: 8-byte LE header length, then a JSON object starting with '{'.
+    let looks_safetensors = ext == "safetensors" || (data.len() > 9 && data[8] == b'{');
+    // JSONL manifest: starts with '{' (or the well-known suffix).
+    let looks_jsonl =
+        ext == "jsonl" || name.ends_with(".rvf.jsonl") || data.first() == Some(&b'{');
+    // Quantized weight blob shipped on HF (model-q2/q4/q8.bin).
+    let looks_quant_bin = ext == "bin" || name.contains("-q");
+
+    let kind = if looks_safetensors {
+        "a safetensors weight file"
+    } else if looks_jsonl {
+        "a JSONL manifest, not the binary container"
+    } else if looks_quant_bin {
+        "a quantized weight blob (e.g. HuggingFace model-q4.bin)"
+    } else {
+        "not an RVF binary container"
+    };
+
+    format!(
+        "model `{}` could not be loaded: it is {kind}. The --model flag expects an \
+         RVF binary container (`RVFS` magic 0x52564653) produced by the \
+         wifi-densepose-train pipeline. The HuggingFace ruvnet/wifi-densepose-pretrained \
+         files are a different format and encoder architecture, so they do not load \
+         here directly (issue #894). Continuing with signal heuristics. (loader: {err})",
+        path.display()
+    )
+}
+
+/// Whether `--export-rvf` should emit the placeholder container-format demo.
+///
+/// It must only do so **standalone**. Combined with `--train`/`--pretrain` the
+/// real model is produced by the training pipeline, so short-circuiting here
+/// would silently skip training and write placeholder weights — the #894 bug
+/// where the documented `--train … --export-rvf` workflow produced a fake model.
+fn export_emits_placeholder_demo(export_set: bool, train: bool, pretrain: bool) -> bool {
+    export_set && !train && !pretrain
+}
+
 // ── Main ─────────────────────────────────────────────────────────────────────
 
 /// If `--ui-path` points nowhere (wrong cwd), try common repo layouts relative to cwd.
@@ -5519,9 +5672,24 @@ async fn main() {
         return;
     }
 
-    // Handle --export-rvf mode: build an RVF container package and exit
-    if let Some(ref rvf_path) = args.export_rvf {
-        eprintln!("Exporting RVF container package...");
+    // Handle --export-rvf: writes a CONTAINER-FORMAT DEMO with placeholder
+    // weights — it is NOT a trained model. Only short-circuit when standalone:
+    // combined with --train/--pretrain the real model is exported by the
+    // training pipeline, and short-circuiting here would silently skip training
+    // and write placeholder weights (#894 — the documented `--train …
+    // --export-rvf` workflow produced a placeholder and never trained).
+    if export_emits_placeholder_demo(args.export_rvf.is_some(), args.train, args.pretrain) {
+        let rvf_path = args
+            .export_rvf
+            .as_ref()
+            .expect("export_emits_placeholder_demo implies export_rvf is set");
+        eprintln!(
+            "WARNING: --export-rvf writes a CONTAINER-FORMAT DEMO with placeholder \
+             weights — it is NOT a trained model. Train one with \
+             `--train --dataset <DIR>` (which exports a calibrated .rvf to the \
+             models/ directory), or download a pretrained encoder. See issue #894."
+        );
+        eprintln!("Exporting RVF container package (placeholder weights)...");
         use rvf_pipeline::RvfModelBuilder;
 
         let mut builder = RvfModelBuilder::new("wifi-densepose", "1.0.0");
@@ -5570,6 +5738,13 @@ async fn main() {
             }
         }
         return;
+    } else if args.export_rvf.is_some() {
+        // --export-rvf alongside --train/--pretrain: don't emit a placeholder.
+        // Fall through so training runs; it exports the real calibrated model.
+        eprintln!(
+            "Note: --export-rvf is ignored in training mode — the trained model \
+             is exported by the training pipeline to the models/ directory."
+        );
     }
 
     // Handle --pretrain mode: self-supervised contrastive pretraining (ADR-024)
@@ -6113,7 +6288,9 @@ async fn main() {
                         model_loaded = true;
                         progressive_loader = Some(loader);
                     }
-                    Err(e) => error!("Progressive loader init failed: {e}"),
+                    Err(e) => {
+                        error!("{}", diagnose_model_load_error(mp, &data, &e.to_string()))
+                    }
                 },
                 Err(e) => error!("Failed to read model file: {e}"),
             }
@@ -6200,56 +6377,13 @@ async fn main() {
                             let Ok(v) = serde_json::from_str::<serde_json::Value>(&json) else {
                                 continue;
                             };
-                            let cls = &v["classification"];
-                            let vit = &v["vital_signs"];
-                            let presence = cls["presence"].as_bool().unwrap_or(false);
-                            let n_persons = v["persons"]
-                                .as_array()
-                                .map(|a| a.len() as u32)
-                                .or_else(|| v["estimated_persons"].as_u64().map(|x| x as u32))
-                                .unwrap_or(0);
-                            let motion = match cls["motion_level"].as_str() {
-                                Some("none") | Some("still") | Some("idle") | Some("") => 0.0,
-                                Some(_) => 1.0,
-                                None => 0.0,
-                            };
-                            let ts = (v["timestamp"].as_f64().unwrap_or(0.0) * 1000.0) as i64;
-                            let conf = cls["confidence"].as_f64().unwrap_or(0.0);
-                            let presence_score = if presence { conf.max(0.0) } else { 0.0 };
-                            let breathing = vit["breathing_rate_bpm"].as_f64();
-                            let hr = vit["heart_rate_bpm"].as_f64();
-                            // #898: emit one snapshot per physical node so each
-                            // surfaces as its own Home-Assistant device (with
-                            // its own RSSI + availability). Falls back to a
-                            // single aggregate snapshot when there is no
-                            // per-node data (e.g. wifi / simulate sources).
-                            let mk = |nid: String, rssi: Option<f64>| mqtt::state::VitalsSnapshot {
-                                node_id: nid,
-                                timestamp_ms: ts,
-                                presence,
-                                motion,
-                                presence_score,
-                                breathing_rate_bpm: breathing,
-                                heartrate_bpm: hr,
-                                n_persons,
-                                rssi_dbm: rssi,
-                                vital_confidence: conf,
-                                ..Default::default()
-                            };
-                            match v["nodes"].as_array() {
-                                Some(arr) if !arr.is_empty() => {
-                                    for node in arr {
-                                        let n = node["node_id"].as_u64().unwrap_or(0);
-                                        let nid = format!("{node_id}-node{n}");
-                                        let _ = vtx.send(mk(nid, node["rssi_dbm"].as_f64()));
-                                    }
-                                }
-                                _ => {
-                                    let _ = vtx.send(mk(
-                                        node_id.clone(),
-                                        v["nodes"][0]["rssi_dbm"].as_f64(),
-                                    ));
-                                }
+                            // #898/#872: emit one snapshot per physical node so
+                            // each surfaces as its own Home-Assistant device with
+                            // its *own* presence/motion/RSSI (see
+                            // vitals_snapshots_from_sensing_json). Falls back to a
+                            // single aggregate snapshot for per-node-less sources.
+                            for snap in vitals_snapshots_from_sensing_json(&v, &node_id) {
+                                let _ = vtx.send(snap);
                             }
                         }
                     });
@@ -7068,3 +7202,169 @@ mod rolling_p95_tests {
         assert_eq!(p.len(), 1);
     }
 }
+
+#[cfg(all(test, feature = "mqtt"))]
+mod mqtt_bridge_tests {
+    use super::vitals_snapshots_from_sensing_json;
+    use serde_json::json;
+
+    /// Regression for the per-node presence bug (#872/#898): each node must
+    /// surface its OWN classification, not the room-level aggregate. Node 1 is
+    /// present+moving; node 2 is absent — node 2 must NOT inherit node 1's
+    /// "present".
+    #[test]
+    fn per_node_presence_uses_each_nodes_own_classification() {
+        let v = json!({
+            "timestamp": 1.0,
+            "classification": { "presence": true, "motion_level": "walking", "confidence": 0.9 },
+            "vital_signs": { "breathing_rate_bpm": 14.0, "heart_rate_bpm": 60.0 },
+            "persons": [{}, {}],
+            "nodes": [
+                { "node_id": 1, "rssi_dbm": -40.0,
+                  "classification": { "presence": true, "motion_level": "walking", "confidence": 0.8 } },
+                { "node_id": 2, "rssi_dbm": -70.0,
+                  "classification": { "presence": false, "motion_level": "absent", "confidence": 0.1 } }
+            ]
+        });
+        let snaps = vitals_snapshots_from_sensing_json(&v, "ruview");
+        assert_eq!(snaps.len(), 2, "one snapshot per node");
+
+        let n1 = snaps.iter().find(|s| s.node_id == "ruview-node1").unwrap();
+        let n2 = snaps.iter().find(|s| s.node_id == "ruview-node2").unwrap();
+
+        assert!(n1.presence && n1.motion > 0.0, "node1 present + moving");
+        assert!(
+            !n2.presence && n2.motion == 0.0,
+            "node2 must be absent — not inherit the room aggregate"
+        );
+        // Per-node RSSI preserved.
+        assert_eq!(n1.rssi_dbm, Some(-40.0));
+        assert_eq!(n2.rssi_dbm, Some(-70.0));
+        // Vitals + person count are room-level, shared across node devices.
+        assert_eq!(n1.n_persons, 2);
+        assert_eq!(n2.n_persons, 2);
+        assert_eq!(n1.breathing_rate_bpm, Some(14.0));
+        assert_eq!(n2.heartrate_bpm, Some(60.0));
+        // presence_score is gated on presence.
+        assert!(n1.presence_score > 0.0);
+        assert_eq!(n2.presence_score, 0.0);
+    }
+
+    /// A node that omits a classification field defers to the room aggregate
+    /// rather than silently reading false/0.
+    #[test]
+    fn per_node_missing_fields_fall_back_to_aggregate() {
+        let v = json!({
+            "timestamp": 1.0,
+            "classification": { "presence": true, "motion_level": "still", "confidence": 0.7 },
+            "vital_signs": {},
+            "nodes": [ { "node_id": 3, "rssi_dbm": -55.0 } ]  // no per-node classification
+        });
+        let snaps = vitals_snapshots_from_sensing_json(&v, "n");
+        assert_eq!(snaps.len(), 1);
+        assert_eq!(snaps[0].node_id, "n-node3");
+        assert!(snaps[0].presence, "defers to aggregate presence");
+        assert_eq!(snaps[0].motion, 0.0, "aggregate 'still' => no motion");
+    }
+
+    /// No `nodes` array (wifi / simulate sources): single aggregate snapshot
+    /// keyed by the base id.
+    #[test]
+    fn falls_back_to_single_aggregate_when_no_nodes() {
+        let v = json!({
+            "timestamp": 2.0,
+            "classification": { "presence": true, "motion_level": "idle", "confidence": 0.6 },
+            "vital_signs": { "breathing_rate_bpm": 12.0 },
+            "persons": [{}]
+        });
+        let snaps = vitals_snapshots_from_sensing_json(&v, "ruview");
+        assert_eq!(snaps.len(), 1);
+        assert_eq!(snaps[0].node_id, "ruview");
+        assert!(snaps[0].presence);
+        assert_eq!(snaps[0].motion, 0.0, "idle => no motion");
+        assert_eq!(snaps[0].n_persons, 1);
+    }
+
+    /// `motion_level: "absent"` must map to zero motion (the old aggregate
+    /// match fell through to `Some(_) => 1.0`, treating absent as full motion).
+    #[test]
+    fn absent_motion_level_is_zero_motion() {
+        let v = json!({
+            "timestamp": 0.0,
+            "classification": { "presence": false, "motion_level": "absent", "confidence": 0.0 },
+            "vital_signs": {}
+        });
+        let snaps = vitals_snapshots_from_sensing_json(&v, "x");
+        assert_eq!(snaps[0].motion, 0.0);
+        assert!(!snaps[0].presence);
+    }
+}
+
+#[cfg(test)]
+mod model_load_diagnostic_tests {
+    use super::diagnose_model_load_error;
+    use std::path::Path;
+
+    #[test]
+    fn safetensors_is_named_and_points_at_894() {
+        // 8-byte LE header length then '{' — the safetensors signature.
+        let data = [0x10, 0, 0, 0, 0, 0, 0, 0, b'{', b'"'];
+        let msg = diagnose_model_load_error(
+            Path::new("models/wifi-densepose-pretrained/model.safetensors"),
+            &data,
+            "invalid magic at offset 0",
+        );
+        assert!(msg.contains("safetensors"), "{msg}");
+        assert!(msg.contains("#894"), "{msg}");
+        assert!(msg.contains("signal heuristics"), "{msg}");
+    }
+
+    #[test]
+    fn quantized_bin_is_identified() {
+        let data = [0x35, 0x57, 0x45, 0x77]; // the 0x77455735 the loader reports
+        let msg = diagnose_model_load_error(Path::new("model-q4.bin"), &data, "bad magic");
+        assert!(msg.contains("quantized weight blob"), "{msg}");
+        assert!(msg.contains("RVFS") || msg.contains("0x52564653"), "{msg}");
+    }
+
+    #[test]
+    fn jsonl_manifest_is_identified() {
+        let data = *b"{\"seg\":0}";
+        let msg = diagnose_model_load_error(Path::new("model.rvf.jsonl"), &data, "x");
+        assert!(msg.contains("JSONL manifest"), "{msg}");
+    }
+
+    #[test]
+    fn unknown_format_still_gives_guidance() {
+        let data = [0u8, 1, 2, 3];
+        let msg = diagnose_model_load_error(Path::new("weird.dat"), &data, "x");
+        assert!(msg.contains("RVF binary container"), "{msg}");
+        assert!(msg.contains("wifi-densepose-train"), "{msg}");
+    }
+}
+
+#[cfg(test)]
+mod export_rvf_mode_tests {
+    use super::export_emits_placeholder_demo;
+
+    #[test]
+    fn standalone_export_emits_placeholder() {
+        // --export-rvf alone → the container-format demo (placeholder weights).
+        assert!(export_emits_placeholder_demo(true, false, false));
+    }
+
+    #[test]
+    fn export_with_train_does_not_short_circuit() {
+        // #894: `--train --export-rvf` must NOT emit a placeholder + skip
+        // training — it must fall through to the real training pipeline.
+        assert!(!export_emits_placeholder_demo(true, true, false));
+        assert!(!export_emits_placeholder_demo(true, false, true));
+        assert!(!export_emits_placeholder_demo(true, true, true));
+    }
+
+    #[test]
+    fn no_export_flag_never_emits() {
+        assert!(!export_emits_placeholder_demo(false, false, false));
+        assert!(!export_emits_placeholder_demo(false, true, false));
+    }
+}

vendor/midstream

@@ -1 +1 @@
-Subproject commit 8f70d2bb9d64a8ddee7745bc18bc4ff9e151845e
+Subproject commit 92250c20d8aa3d867fcc8cb75aeabbc95eb1a0cc

vendor/ruvector

@@ -1 +1 @@
-Subproject commit e3834760148f99373eab2bc008da280d57d80333
+Subproject commit 712e22c14ea5644ba768c82c55a9516be5f1d6f7

vendor/rvcsi

@@ -1 +1 @@
-Subproject commit 72891d740f92903c78a8208a9069f6c82a4d1cc2
+Subproject commit 77c8b6e051fe8a2a3370715d4c3139311b93c364

vendor/sublinear-time-solver

@@ -1 +1 @@
-Subproject commit c25dddf163d8c413628ecdc6e979583d39270f22
+Subproject commit 47804fc5ca495d4fd608866fdb1e0960b34bffbf