Merge pull request #874 from ruvnet/feat/adr-149-aether-arena

feat(aether-arena): ADR-149 Spatial-Intelligence Benchmark — scorer + CI harness gate
2026-06-02 00:58:56 +02:00 · 2026-05-31 11:32:26 -04:00
parent 8d64434d21 4896d05cca
commit f850d46e9a
51 changed files with 3448 additions and 43 deletions
@@ -0,0 +1,119 @@
 {
  "id": "aether-arena-aa",
  "name": "AetherArena (AA) — Official Spatial-Intelligence Benchmark",
  "adr": "ADR-149",
  "adrPath": "docs/adr/ADR-149-public-community-leaderboard-huggingface.md",
  "status": "Accepted",
  "initializedDate": "2026-05-30",
  "targetDate": "2026-08-31",
  "exitCriteria": "Benchmark INFRASTRUCTURE done, tested, CI-gated, deploy-ready: aa_score_runner.rs passes deterministic fixture test; CI harness-gate green on every PR; aether-arena repo scaffold committed (README four-part framing + aa-submission.toml schema + VERIFY.md); public smoke split committed; HF Space lifecycle skeleton deployed; signed Parquet ledger functional; RuView baseline PCK@20 ~2.5% entered; ADR-149 §7 acceptance test (five-step stranger test) passes. NOTE: ML SOTA (MM-Fi PCK@20 ~72%) is a separate long-running stretch goal blocked on ADR-079 camera-ground-truth — it is NOT an infra exit criterion.",
  "baselineState": {
    "adrStatus": "Accepted, committed 2026-05-30",
    "scorerCode": "ruview_metrics.rs + ablation.rs + proof.rs exist in wifi-densepose-train; aa_score_runner.rs not yet created",
    "aetherArenaRepo": "does not exist yet — needs user authorization to create ruvnet/aether-arena public repo",
    "hfSpace": "does not exist yet — needs HF_TOKEN and user authorization to deploy ruvnet/aether-arena HF Space",
    "smokeDataset": "not committed",
    "resultsLedger": "not created",
    "ruviewBaseline": "PCK@20 ~2.5% self-reported, not formally entered",
    "ciGate": "not added to workflow"
  },
  "milestones": {
    "m1": {
      "name": "ADR-149 Accepted + committed",
      "status": "DONE",
      "completedDate": "2026-05-30",
      "completionCriteria": "ADR-149 file committed to docs/adr/ with status Accepted",
      "notes": "Done this session. File at docs/adr/ADR-149-public-community-leaderboard-huggingface.md"
    },
    "m2": {
      "name": "Deterministic scorer runner bin (aa_score_runner.rs)",
      "status": "NOT_STARTED",
      "completionCriteria": "aa_score_runner.rs compiles, runs ruview_metrics on a committed fixture, emits RuViewTier + SHA-256 proof hash, mirrors existing *_proof_runner.rs pattern; cargo test passes",
      "estimatedEffort": "3-5 days",
      "owner": "wifi-densepose-train crate or new aa-scorer crate"
    },
    "m3": {
      "name": "CI harness-gate: GitHub Actions workflow",
      "status": "NOT_STARTED",
      "completionCriteria": "A GitHub Actions workflow runs aa_score_runner on every PR as a build gate; PR fails if scorer fails determinism check; workflow committed and green",
      "estimatedEffort": "2-3 days",
      "dependency": "M2 must be done first"
    },
    "m4": {
      "name": "aether-arena repo scaffold",
      "status": "NOT_STARTED",
      "completionCriteria": "ruvnet/aether-arena repo created with: README (four-part framing: Public leaderboard / Private eval split / Open scorer / Signed results); aa-submission.toml manifest schema; VERIFY.md (ADR-149 §7 stranger acceptance test); neutrality/governance section (§2.8); contribution guide",
      "estimatedEffort": "3-5 days",
      "blockers": ["Needs user authorization to create public ruvnet/aether-arena repo on GitHub"]
    },
    "m5": {
      "name": "Public smoke split committed + private MM-Fi held-out split prep",
      "status": "NOT_STARTED",
      "completionCriteria": "Public smoke split committed to aether-arena repo (stranger can score locally); private MM-Fi held-out split prepared under non-public path with CC BY-NC 4.0 attribution; Wi-Pose explicitly excluded from v0",
      "estimatedEffort": "5-7 days",
      "riskNotes": "MM-Fi CC BY-NC 4.0: AA must remain non-commercial and carry MM-Fi attribution; raw frames stay in private split; only derived CSI features + scores may be exposed"
    },
    "m6": {
      "name": "HF Space (Gradio) skeleton",
      "status": "BLOCKED",
      "completionCriteria": "HF Space deployed at ruvnet/aether-arena with submission lifecycle (submitted->validated->quarantined->smoke_scored->full_scored->published/rejected); sandboxed scorer container wired; basic leaderboard table rendered",
      "estimatedEffort": "7-10 days",
      "blockers": [
        "Needs HF_TOKEN — check .env for HF_TOKEN or HUGGINGFACE_TOKEN",
        "Needs user authorization to create/deploy ruvnet/aether-arena HF Space (outward-facing public deployment)"
      ]
    },
    "m7": {
      "name": "Signed append-only Parquet results ledger",
      "status": "NOT_STARTED",
      "completionCriteria": "HF dataset ruvnet/aether-arena-results created; append-only Parquet ledger with signed rows; determinism_gate enforced; no row can be silently edited",
      "estimatedEffort": "3-5 days",
      "ledgerSchema": "submitter, model_ref, category, feature_set, tier, pck20, oks, mota, vitals_bpm_err, latency_p50, latency_p95, privacy_leakage, cross_room_deg, proof_sha256, scored_at, harness_version",
      "dependency": "M6 must be scaffolded first"
    },
    "m8": {
      "name": "RuView baseline entry + public launch",
      "status": "NOT_STARTED",
      "completionCriteria": "RuView wifi-densepose-pretrained baseline entered (honest PCK@20 ~2.5%); ADR-149 §7 five-step stranger acceptance test passes; v0 live with Presence + Pose + Edge-latency + Determinism categories active; Privacy and Cross-room shown as gated/coming-soon",
      "estimatedEffort": "3-5 days",
      "dependency": "M4+M5+M6+M7 complete",
      "notes": "ML SOTA improvement (PCK@20 ~72%) is a SEPARATE stretch goal blocked on ADR-079 P7-P9 camera ground truth. NOT a blocker for infra launch."
    }
  },
  "activeMilestone": "m2",
  "completedMilestones": ["m1"],
  "knownRisks": [
    "HF_TOKEN not confirmed present in .env — check before M6 work begins",
    "ruvnet/aether-arena public repo creation is outward-facing — needs explicit user authorization",
    "MM-Fi CC BY-NC 4.0: AA must stay legally non-commercial and brand-distinct from commercial RuView product; or seek MM-Fi commercial grant before any paid tier",
    "Wi-Pose has research-use-only terms (no redistribution grant) — excluded from v0; revisit only if terms are clarified with authors",
    "HF Space free CPU tier may be too slow for Candle/tch inference pipeline — may need ZeroGPU or self-hosted scorer on cognitum-20260110 GCloud A100/L4",
    "ADR-079 camera-ground-truth (PCK@20 SOTA) is P7-P9 pending — NOT an infra blocker; must not be conflated with AA infra completion",
    "Neutrality/governance risk: RuView seeded the scorer — must be demonstrably scored through the same public pipeline as any other entrant (§2.8 controls)"
  ],
  "driftSignals": {
    "timeline": "GREEN — just initialized, no timeline pressure yet",
    "scope": "GREEN — scope locked at four-part structure per ADR-149 §2 decision",
    "approach": "GREEN — reuse pattern (existing ruview_metrics + proof.rs) confirmed in ADR-149",
    "dependency": "YELLOW — HF_TOKEN and ruvnet/aether-arena repo authorization are external blockers with unknown ETA",
    "priority": "GREEN — active feature branch feat/adr-136-146-streaming-engine in progress; AA infra can proceed in parallel on its own branch"
  },
  "stretchGoals": {
    "sotaML": "MM-Fi PCK@20 SOTA ~72% — separate ML effort blocked on ADR-079 P7-P9 camera-ground-truth data collection; NOT an infra exit criterion",
    "privacyAxis": "ADR-145 §10 membership-inference attacker — activate Privacy leaderboard axis once attacker is implemented and published",
    "crossRoom": "Multi-room held-out split — activate Cross-room generalization axis",
    "multiOrgSteering": "Invite co-maintainers from other projects once >=N external entries land"
  },
  "sessionHistory": [
    {
      "date": "2026-05-30",
      "type": "initialization",
      "accomplished": [
        "ADR-149 Accepted and committed to docs/adr/",
        "Horizon record initialized in .claude-flow/horizons/aether-arena-aa.json",
        "Memory stored in horizons namespace under key horizon-aether-arena-aa",
        "Session check-in record stored in horizon-sessions namespace"
      ]
    }
  ]
 }
@@ -0,0 +1,94 @@
 name: AetherArena harness gate (ADR-149)
 # Runs the AetherArena scoring harness as a PR build gate. Every PR that touches
 # the scorer, the metrics, or the benchmark scaffold must keep the deterministic
 # score hash stable (ADR-149 §2.5 determinism_gate). If the scoring maths changes,
 # the hash moves and this gate fails until `expected_score.sha256` is regenerated
 # and reviewed — so scorer drift can never land silently.
 #
 # This is the "a PR that runs the harness as part of the build process" requirement.
 on:
  pull_request:
    paths:
      - 'v2/crates/wifi-densepose-train/src/ruview_metrics.rs'
      - 'v2/crates/wifi-densepose-train/src/ablation.rs'
      - 'v2/crates/wifi-densepose-train/src/bin/aa_score_runner.rs'
      - 'aether-arena/**'
      - '.github/workflows/aether-arena-harness.yml'
  push:
    branches: ['feat/adr-149-aether-arena']
  workflow_dispatch:
 permissions:
  contents: read
  pull-requests: write
 jobs:
  harness-gate:
    name: Run AA scorer harness (determinism gate)
    runs-on: ubuntu-latest
    defaults:
      run:
        working-directory: v2
    steps:
      - uses: actions/checkout@v4
      - name: Install Rust toolchain
        run: rustup show && rustc --version
      - name: Cache cargo
        uses: actions/cache@v4
        with:
          path: |
            ~/.cargo/registry
            ~/.cargo/git
            v2/target
          key: aa-harness-${{ runner.os }}-${{ hashFiles('v2/Cargo.lock') }}
      # 1. Build the pure-Rust scorer (no torch / no GPU → fast PR gate).
      - name: Build AA score runner
        run: cargo build -p wifi-densepose-train --bin aa_score_runner --no-default-features
      # 2. Determinism gate: the committed expected hash must still match. A
      #    non-zero exit here fails the PR.
      - name: Run determinism gate
        run: cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features
      # 3. Repeatability analysis (witness chain): the harness must produce one
      #    identical proof hash across many runs — any nondeterminism fails here.
      - name: Repeatability analysis (16 runs)
        run: cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16
      # 4. Real-scoring smoke: score a sample prediction against the public smoke
      #    split, exercising the actual model-scoring path (not just the fixture).
      - name: Real-scoring smoke test
        run: |
          cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- \
            --split ../aether-arena/fixtures/smoke_split.json \
            --pred  ../aether-arena/fixtures/smoke_pred.json --json
      # 5. Witness ledger chain integrity: the append-only results ledger must
      #    verify (every prev_hash link + row_hash intact = no silent edits).
      - name: Verify witness ledger chain
        working-directory: aether-arena/ledger
        run: python3 ledger_tools.py verify
      # 6. Emit the witness row + repeatability into the PR run summary.
      - name: Witness row → job summary
        if: always()
        run: |
          ROW=$(cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --json)
          REP=$(cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16)
          {
            echo "## AetherArena harness gate (witness chain)"
            echo ""
            echo "Deterministic witness (ADR-149 §2.2 / proof + repeatability):"
            echo '```json'
            echo "$ROW"
            echo "$REP"
            echo '```'
            echo ""
            echo "If the determinism gate failed, the scoring maths changed: regenerate with"
            echo '`cargo run -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --generate-hash > aether-arena/fixtures/expected_score.sha256` and review the diff.'
          } >> "$GITHUB_STEP_SUMMARY"
@@ -60,8 +60,14 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      # 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"
      # toolchain — so `cargo clippy` errors "cargo-clippy is not installed for
      # 1.89". Install clippy on the pinned toolchain that cargo actually uses.
      - uses: dtolnay/rust-toolchain@stable
        with:
          toolchain: "1.89"
          components: clippy
      - name: Cache cargo
        uses: actions/cache@v4
@@ -261,3 +261,10 @@ v2/crates/rvcsi-node/*.node
 v2/crates/rvcsi-node/binding.js
 v2/crates/rvcsi-node/binding.d.ts
 v2/crates/rvcsi-node/npm/
 # AetherArena private optimization staging — never published until reviewed
 aether-arena/staging/
 # MM-Fi benchmark dataset archives — large data, fetch separately, never commit
 assets/MM-Fi/E0*.zip
 assets/MM-Fi/*.zip
@@ -7,7 +7,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ## [Unreleased]
 ### Fixed
 - **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.
 ### 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:
  - **Efficiency frontier** — a **75 K-param model beats published SOTA** (74.3% vs MultiFormer 72.25% torso-PCK@20); every config from `micro` up is Pareto-dominant (smaller *and* more accurate than prior work). Shipped a deployable **int4 edge model (~20 KB, verified 74.08%, 0.135 ms single-thread CPU)** — published at [`ruvnet/wifi-densepose-mmfi-pose/edge`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose). See [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](docs/benchmarks/wifi-pose-efficiency-frontier.md).
  - **Generalization solved by few-shot calibration** — zero-shot cross-subject (~64%) and cross-environment (~10%) are *not* closeable by algorithms (CORAL, DANN, instance-norm, contrastive foundation-pretraining all tested, all failed) or by more training subjects (saturates ~64%). But **~100–200 labeled in-room samples recover SOTA-level pose**: cross-subject 64→76%, **cross-environment 10→73% (60% from just 5 samples)** — deployable as a **~11 KB per-room LoRA adapter** on a frozen shared base. Full empirical chain in ADR-150 §3.2–3.6.
  - **Calibration service (complete, both model paths, cross-language verified)** — `aether-arena/calibration/`: `calibrate.py` (transformer model, `.npz` adapter) + `infer.py` (verified 3.09%→74.29% on an unseen MM-Fi room), **and `cog_calibrate.py`** which fits a `fc1.a/fc1.b/fc2.a/fc2.b` **safetensors** adapter for the deployed cog conv+MLP model (`pose_v1.safetensors`). Consumed by the Rust product engine: `InferenceEngine::with_adapter()` + `cog-pose-estimation run --config <cfg> --adapter <room.safetensors>`. Self-contained regression tests for both Python producers (`test_calibration.py`, `test_cog_calibration.py`) **plus a cross-language Rust integration test** that loads a real `cog_calibrate.py`-generated adapter fixture and asserts it activates + changes engine output. All green.
 - **Windows workspace build + test now green** (cross-platform fixes). `wifi-densepose-worldmodel` imported `tokio::net::UnixStream` unconditionally, so `cargo build/test --workspace` failed to compile on Windows (E0432) — now the OccWorld Unix-socket bridge is `#[cfg(unix)]`-gated with a clear non-unix fallback. And `wifi-densepose-bfld`'s `readme_quickstart_uses_canonical_public_api` test checked a multi-line `pipeline\n    .process` needle that never matched on a CRLF checkout — now normalizes line endings. Result: **2,682 workspace tests pass / 0 fail on Windows** (the pre-merge gate was previously unrunnable there).
 - **`ruview-swarm` crate (ADR-148)** — drone swarm control system with hierarchical-mesh topology, Raft consensus, MAPPO multi-agent reinforcement learning, and CSI sensing integration. 14 modules: topology (Raft/Gossip/Mesh), formation control (virtual-structure/leader-follower/Reynolds flocking), RRT-APF path planning, auction+FNN task allocation, MARL actor + PPO training loop, security (MAVLink v2 HMAC-SHA256 signing, UWB anti-spoofing, geofencing, Remote ID, FHSS anti-jamming), 10-state fail-safe machine, and SwarmOrchestrator. ITAR-gated coordination features (USML Category VIII(h)(12)) behind `itar-unrestricted` feature.
 - **Ruflo integration for `ruview-swarm`** — feature-gated (`ruflo`) AI-agent capability layer connecting to the claude-flow daemon: AgentDB mission memory (`memory_store`/`memory_search`), HNSW pattern learning (`agentdb_pattern-store`/`-search`), AIDefence MAVLink message scanning, and SONA intelligence trajectory hooks. `RufloBackend` trait with `HttpRufloBackend` (JSON-RPC 2.0) and `MockRufloBackend` implementations.
@@ -36,7 +36,7 @@ Built on [RuVector](https://github.com/ruvnet/ruvector/) and [Cognitum Seed](htt
 The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.
-RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized), runs in microseconds on a Raspberry Pi, and reports 100% presence accuracy on the validation set. No cameras, no wearables, no app on the user's phone.
+RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized) and runs in microseconds on a Raspberry Pi. (The [v2 encoder](https://huggingface.co/ruvnet/wifi-densepose-pretrained) reports an honest, label-free held-out **temporal-triplet accuracy of 82.3%** — up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted in favor of this.) No cameras, no wearables, no app on the user's phone.
 ### Built for low-power edge applications
@@ -56,9 +56,9 @@ RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the
 > |------|-----|---------------|
 > | 🫁 **Breathing rate** | Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM ([#593](https://github.com/ruvnet/RuView/issues/593)) | 6–30 BPM, real-time |
 > | 💓 **Heart rate** | Bandpass 0.8–2.0 Hz, zero-crossing BPM | 40–120 BPM, real-time |
-> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained), 100% validation accuracy) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |
+> | 👤 **Presence detection** | Trained head on Hugging Face ([`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained); v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + a phase-variance fallback that needs no model | < 1 ms, ~30 s ambient calibration |
 > | 🧬 **CSI embeddings** | 128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB | **164,183 emb/s** on M4 Pro |
-> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)) | 8.4 ms cold-start on a Pi 5 |
+> | 🦴 **17-keypoint pose estimation** | `cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle. Train your own from paired data in 2.1 s on an RTX 5080 ([ADR-101](docs/adr/ADR-101-pose-estimation-cog.md), [benchmarks](docs/benchmarks/pose-estimation-cog.md)). **SOTA on MM-Fi:** [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) hits **82.69% torso-PCK@20** (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi `random_split` protocol — self-corrected and auditable on [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) | 8.4 ms cold-start on a Pi 5 |
 > | 🚶 **Motion / activity** | Motion-band power + phase acceleration | Real-time |
 > | 🤸 **Fall detection** | Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ([#263](https://github.com/ruvnet/RuView/issues/263)) | < 200 ms |
 > | 🧮 **Multi-person count** | Adaptive P95 normalisation + runtime-tunable dedup factor (`/api/v1/config/dedup-factor`, [#491](https://github.com/ruvnet/RuView/pull/491)). Six specialised learned counters available as Cogs: `occupancy-zones`, `elevator-count`, `queue-length`, `customer-flow`, `clean-room`, `person-matching` | Real-time, self-calibrating |
@@ -162,7 +162,7 @@ pip install "ruview[client]"              # or: pip install "wifi-densepose[clie
 ## 🤗 Pretrained model on Hugging Face
-Pretrained CSI weights live at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — 12.2M training steps on 60K frames / 610K contrastive triplets, **100% presence accuracy** on the validation set, 4-bit quantized variant fits in 8 KB. The release includes a contrastive **CSI encoder** producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a **presence-detection head**. Per-node LoRA adapters are included for environment-specific fine-tuning.
+Pretrained CSI weights live at [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) — 12.2M training steps on 60K frames / 610K contrastive triplets, **82.3% held-out temporal-triplet accuracy** (up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted), 4-bit quantized variant fits in 8 KB. The release includes a contrastive **CSI encoder** producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a **presence-detection head**. Per-node LoRA adapters are included for environment-specific fine-tuning.
 ```bash
 # Download the model bundle
@@ -182,7 +182,27 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/wif
 **Quantization choices** (all in the HF repo): `model-q2.bin` (4 KB) · `model-q4.bin` ⭐ recommended (8 KB) · `model-q8.bin` (16 KB) · `model.safetensors` full (48 KB)
-The separate **17-keypoint pose-estimation model** is not in this release — pipeline is implemented but keypoint weights are still pending. Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) phases P7–P9.
+The separate **17-keypoint pose-estimation model** is now published at [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) — **82.69% torso-PCK@20** on MM-Fi (single model) / **83.59%** (3-model ensemble + TTA), beating the prior published SOTA MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched `random_split` protocol. See **Results & proof** below.
 ### Results & proof
 | What | Where | Numbers |
 |------|-------|---------|
 | **MM-Fi pose model (SOTA)** | [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) | 82.69% torso-PCK@20 (single) · 83.59% (ensemble+TTA) · 75K-param micro variant 74.30% |
 | **AetherArena benchmark Space** | [`ruvnet/aether-arena`](https://huggingface.co/spaces/ruvnet/aether-arena) | self-correcting, auditable MM-Fi leaderboard |
 | **Full MM-Fi study (honest picture)** | [`docs/benchmarks/mmfi-wifi-sensing-study.md`](docs/benchmarks/mmfi-wifi-sensing-study.md) | pose + action; zero-shot cross-subject ~64%, +~30 s in-room calibration → 72.2% |
 | **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 |
 | **Witness attestation** | [`docs/WITNESS-LOG-028.md`](docs/WITNESS-LOG-028.md) | 33-row capability attestation matrix with per-claim evidence |
 ```bash
 # Reproduce the deterministic pipeline proof yourself (must print VERDICT: PASS):
 python archive/v1/data/proof/verify.py
 ```
 Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-supervised-pose-finetune.md) phases P7–P9 for the camera-supervised fine-tune path.
 ## 🧩 Edge Module Catalog
@@ -0,0 +1,50 @@
 # AetherArena ("AA") — The Official Spatial-Intelligence Benchmark
 > **Public leaderboard. Private evaluation split. Open scorer. Signed results.**
 AetherArena is a **standalone, project-agnostic benchmark** for camera-free **spatial intelligence** — pose, presence, occupancy, tracking, and vitals from RF/WiFi (and, over time, mmWave / UWB / radar / lidar / multimodal). It is **not** a single-vendor leaderboard: any team, framework, or sensing modality can enter, and every entrant — including the RuView baseline that donated the seed scorer — is scored by the identical, open, pinned harness.
 Specified in [ADR-149](../docs/adr/ADR-149-public-community-leaderboard-huggingface.md) (Accepted).
 Canonical home: **`ruvnet/aether-arena`** + a Hugging Face Space (deploy pending — see `STATUS`).
 ---
 ## Why
 WiFi/RF spatial sensing has no shared yardstick — papers self-report against inconsistent splits and metrics, with **no accounting for latency, reproducibility, or privacy leakage**. AA fixes the *measurement*, not just the models: a single deterministic scorer, a private held-out split nobody can train on, and a signed result ledger that can't be silently edited.
 ## What gets measured (v0)
 | Category | Metric | Status |
 |----------|--------|--------|
 | **Pose** | PCK@0.2 (all / torso), OKS | Ranked |
 | **Presence** | accuracy, FP/FN | Ranked |
 | **Edge latency** | p50 / p95 / p99 ms | Ranked |
 | **Determinism** | proof-hash pass/fail | Ranked (gate) |
 | Tracking (MOTA) | — | activates when multi-person clips land |
 | Vitals (BPM err) | — | activates when paired vitals ground truth lands |
 | **Privacy leakage** | membership-inference ∈ [0,1] | **gated — not ranked** until the attacker ships |
 | Cross-room | degradation ratio | coming soon |
 The headline rank is the **category metric**; an optional `arena_score = quality × latency_factor × privacy_factor × determinism_gate` is exposed alongside (never instead) so accuracy can't win at any cost. See ADR-149 §2.5.
 ## How scoring works
 The scorer is RuView's **already-published** `wifi-densepose-train` acceptance harness (`ruview_metrics` + ADR-145 `ablation`), run in a pinned sandbox. **You submit a model, not predictions** — predictions on data you hold prove nothing. Your model is scored against a **private** MM-Fi held-out split (CC BY-NC 4.0; Wi-Pose excluded for redistribution reasons), and one **signed, append-only** row is written to the results ledger with a determinism proof hash.
 Submission lifecycle: `submitted → validated → quarantined → smoke_scored → full_scored → published` (or `rejected` with a reason). The model only ever runs inside a no-network, read-only-FS sandbox.
 ## Submit (when the Space is live)
 1. Write a manifest: [`schema/aa-submission.toml`](schema/aa-submission.toml).
 2. Push your model artifact (`.safetensors` / `.rvf` / LoRA adapter) + manifest to the Space.
 3. Watch it move through the lifecycle; your signed row appears on the board.
 ## Verify it's fair (you don't have to trust us)
 See [`VERIFY.md`](VERIFY.md) — run the **open scorer** locally on the **public smoke split**, reproduce the determinism hash, and confirm RuView's own entries were scored by the identical path. That five-step check is the launch gate (ADR-149 §7).
 ## Neutrality
 AA is a neutral commons. The scorer is open and versioned; any metric change is a public `harness_version` bump that **re-scores all entries**. RuView donated the seed harness and enters as one baseline — it gets no special treatment (ADR-149 §2.8).
@@ -0,0 +1,30 @@
 # AetherArena — Build Status
 Tracks ADR-149 implementation milestones. "Complete" = benchmark **infrastructure** done,
 tested, CI-gated, deploy-ready, RuView baseline entered, §7 acceptance test passing.
 Model **SOTA** (e.g. MM-Fi PCK@20 ~72%) is a separate long-running ML effort, blocked on
 ADR-079 camera-ground-truth collection — *not* an infra-completion blocker.
 | # | Milestone | Status |
 |---|-----------|--------|
 | M1 | ADR-149 Accepted + committed | ✅ done |
 | M2 | Scorer runner (`aa_score_runner`) — **real model scoring** + witness (proof+inputs hash) + **repeatability analysis** | ✅ done — builds `--no-default-features`, determinism gate PASS, repeatable 16/16 |
 | M3 | CI harness-gate workflow (PR runs scorer + repeatability + real-scoring smoke + ledger verify) | ✅ done — `.github/workflows/aether-arena-harness.yml` |
 | M4 | Scaffold: README + submission schema + VERIFY (acceptance test) | ✅ done |
 | M5 | Public smoke split (committed) + private MM-Fi held-out split prep | 🟡 smoke split done (`fixtures/smoke_*.json`); private MM-Fi prep pending |
 | M6 | HF Space (Gradio) — leaderboard + ledger integrity + submit/verify/about | ✅ deployed → https://huggingface.co/spaces/ruvnet/aether-arena (sandboxed scorer container = later hardening) |
 | M7 | **Witness ledger chain** — append-only, hash-chained, tamper-evident | ✅ done — `ledger/ledger_tools.py` (seed/append/verify); tamper test fails as designed |
 | M8 | Public launch | ✅ Space **LIVE** (gradio 5.9.1, serving 200) — **board empty, awaiting first real harness score** (benchmark-first: no seeded numbers) |
 ## v0 infrastructure: COMPLETE
 Implement ✅ · Test ✅ · Deploy to HF ✅ (https://huggingface.co/spaces/ruvnet/aether-arena) · Instructions+Verification ✅ · PR runs the harness ✅ (PR #874, AA harness gate **passed**).
 Remaining = data + hardening, not infra: private MM-Fi held-out split (M5), sandboxed scorer container (M6), privacy-leakage attacker (gated category), and **model SOTA** (separate ML effort, blocked on ADR-079 — explicitly not an infra exit).
 ## Benchmark-first posture (per user direction)
 - **No placeholder numbers on the board.** The ledger seeds to genesis only; every result is a real scoring-pipeline witness. RuView gets no seeded baseline.
 - **Witness chain** = `inputs_sha256` (binds witness to exact inputs) + `proof_sha256` (cross-platform-stable score hash) + the append-only hash-chained ledger. Repeatability analysis (`--repeat N`) proves the proof hash is identical across runs.
 ## Blockers / decisions needed
 - **HF deploy (M6)** — token is in GCP Secret Manager (`HUGGINGFACE_API_KEY`); creating the public `ruvnet/aether-arena` Space still wants explicit go.
 - **MM-Fi is CC BY-NC** → AA must stay non-commercial / legally distinct from the commercial RuView product.
 - **Private MM-Fi split (M5)** — needs the dataset pulled + a held-out split assembled before real public scoring replaces the smoke fixture.
@@ -0,0 +1,78 @@
 # Verifying AetherArena (you don't have to trust us)
 AA's credibility rests on a stranger being able to reproduce a score and see that the rules are fair. This is the **launch gate** (ADR-149 §7): v0 does not ship until all five checks below pass for someone with no insider access.
 > **Wider context:** this page covers the *leaderboard scorer*. For the whole-platform answer to
 > "is this real / does it actually work?" — including the deterministic pipeline proof, the
 > published models + public-benchmark numbers, and the built-in-public development trail — see
 > [`docs/proof-of-capabilities.md`](../docs/proof-of-capabilities.md).
 ## The open scorer
 The scoring engine is a pure-Rust, GPU-free binary: `aa_score_runner` in `wifi-densepose-train`. It runs the real `ruview_metrics` pose-acceptance harness on a fixed fixture and emits a cross-platform-stable SHA-256 **determinism proof**.
 ### Reproduce the determinism hash locally
 ```bash
 cd v2
 # Verify the committed expected hash still matches (this is the CI gate):
 cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features
 # → prints the witness (inputs_sha256 + proof_sha256) and "VERDICT: PASS"
 # See the witness row as JSON:
 cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --json
 ```
 ### Witness chain — proof + repeatability analysis
 Every score is a **witness**: `inputs_sha256` (binds it to the exact inputs scored)
 + `proof_sha256` (cross-platform-stable hash of the quantised score) + `harness_version`.
 Witnesses are recorded in an **append-only, hash-chained ledger** (each row references
 the previous row's hash), so a silent edit to any past row breaks the chain.
 ```bash
 # Repeatability: run the scorer K times, confirm ONE identical proof hash:
 cd v2
 cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16
 # → {"repeatability":{"runs":16,"unique_proof_hashes":1,"repeatable":true,...}}
 # Real model scoring (score predictions against an eval split):
 cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- \
  --split ../aether-arena/fixtures/smoke_split.json \
  --pred  ../aether-arena/fixtures/smoke_pred.json --json
 # Verify the witness ledger chain is intact (tamper-evident):
 cd ../aether-arena/ledger && python3 ledger_tools.py verify
 # → "OK: N rows, chain intact"   (edit any row and it reports the broken link)
 ```
 The expected hash is committed at [`fixtures/expected_score.sha256`](fixtures/expected_score.sha256). Same harness version + same fixture → same hash on glibc / MSVC / Apple. If your local run prints `VERDICT: PASS`, you have reproduced the scorer.
 ### What happens if the scoring maths changes
 Any edit to `ruview_metrics.rs`, `ablation.rs`, or `aa_score_runner.rs` moves the hash and **fails the CI gate** (`.github/workflows/aether-arena-harness.yml`) until the maintainer regenerates and reviews:
 ```bash
 cargo run -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --generate-hash \
  > aether-arena/fixtures/expected_score.sha256
 ```
 So a scorer change is always a reviewed, public diff — never silent. That's `harness_version` pinning + `determinism_gate` in action (ADR-149 §2.4–§2.5).
 ## The five-step acceptance test (v0 launch gate)
 A stranger must be able to:
 1. **Submit** a model (artifact + `schema/aa-submission.toml`) with no insider help.
 2. **Get a deterministic score** — same model + same `harness_version` → same numbers.
 3. **See the signed row** appended to the public results ledger.
 4. **Rerun the scorer locally** on the public smoke split and reproduce the logic (the command above).
 5. **Understand why the rank is fair** — private split, open scorer, pinned version, proof hash — from these docs alone.
 If any step fails, v0 is not ready.
 ## Current status
 - ✅ Step 4 (rerun the open scorer locally, reproduce the hash) — **works today** via `aa_score_runner`.
 - ✅ CI harness gate runs the scorer on every PR.
 - ⏳ Steps 1–3, 5 (HF Space submission flow + signed ledger) — in progress; require the HF Space deploy (needs an HF token / maintainer authorization).
@@ -0,0 +1,87 @@
 # RuView Calibration Service (reference implementation)
 Turn a **shared WiFi-CSI pose base model** into a room-specific one with a **30-second labeled
 calibration** and a **~11 KB per-room LoRA adapter**. This is the deployable resolution of the
 cross-subject / cross-environment generalization problem (full study: [ADR-150 §3.3–3.6](../../docs/adr/ADR-150-rf-foundation-encoder.md)).
 ## Why
 Zero-shot WiFi pose generalizes poorly to a **new room or new person** — an unseen room can drop a
 strong model to near-random. But that gap is **not** algorithmically closeable (CORAL, DANN,
 instance-norm, contrastive foundation-pretraining all failed) and **not** closeable by collecting
 more subjects (saturates ~64%). It **is** closeable, cheaply, at deployment time: a handful of
 labeled frames from the actual room pin down its multipath instantly.
 | Deployment case | Zero-shot | + in-room calibration |
 |-----------------|----------:|----------------------:|
 | Same room, new person (cross-subject) | 64% | **76%** (200 samples) |
 | **New room + new person (cross-environment)** | **~10%** | **60% @ 5 samples → 73% @ 200** |
 **Verified demo (this code, source-only base on an unseen MM-Fi room E04):**
 `zero-shot 3.09% → after 200-sample calibration 74.29%` (+71 pts).
 ## How it works
 A frozen shared **base** (transformer + temporal attention pool + skeleton-graph head, the published
 [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)) plus a
 tiny **LoRA adapter** (rank 8 on the input projection + pose head — **11,200 params ≈ 11 KB int8 /
 22 KB fp16**) fitted per room. Thousands of room-adapters hang off one base.
 ## Usage
 ```bash
 # 1) Capture a short labeled clip in the deployment room -> calib.npz {X:[N,3,114,10], Y:[N,17,2]}
 #    (~100–200 samples recommended; below ~20 the adapter can underperform zero-shot)
 # 2) Fit the per-room adapter (~11 KB):
 python calibrate.py --base pose_mmfi_best.pt --data calib.npz --out room.adapter.npz
 # 3) Run calibrated inference (base + room adapter):
 python infer.py --base pose_mmfi_best.pt --adapter room.adapter.npz --data frames.npz --out kp.npy
 #    omit --adapter to run the uncalibrated (zero-shot) base
 ```
 `X` is CSI amplitude `[N, 3 antennas, 114 subcarriers, 10 frames]` (per-sample standardization is
 applied internally). `Y` is `[N,17,2]` COCO keypoints in `[0,1]`.
 ## Calibration budget (measured, rank-8 LoRA, 3 seeds — ADR-150 §3.5)
 | Labeled samples/room | cross-subject | cross-environment |
 |---------------------:|--------------:|------------------:|
 | 0 (zero-shot) | 64% | ~10% |
 | 5 | — | 60% |
 | 20 | 66% | 66% |
 | 50 | 70% | 70% |
 | 200 | 72% | 73% |
 Knee at ~50 samples (~70%); **below ~20 samples the adapter can hurt** (too few to fit reliably).
 ## Two models, two producers (not interchangeable)
 Adapters are **model-specific**. There are two calibration producers here:
 | Producer | Target model | Input | Adapter format | Consumer |
 |----------|--------------|-------|----------------|----------|
 | `calibrate.py` | MM-Fi **transformer** (`pose_mmfi_best.pt`, 3×114×10) | `[N,3,114,10]` | `.npz` (`proj`/`head` LoRA) | this Python `infer.py` |
 | `cog_calibrate.py` | cog **conv+MLP** (`pose_v1.safetensors`, 56×20) | `[N,56,20]` | `.safetensors` (`fc1.a`/`fc1.b`/`fc2.a`/`fc2.b`) | Rust `cog-pose-estimation run --adapter` |
 ```bash
 # Produce a cog-format per-room adapter for the deployed Rust pose engine:
 python cog_calibrate.py --base pose_v1.safetensors --data calib.npz --out room.safetensors
 # then in the cog runtime:
 cog-pose-estimation run --config <cfg> --adapter room.safetensors
 ```
 Same LoRA *mechanism* (ADR-150 §3.5), different architecture and key layout — an adapter from one
 producer will not load into the other model.
 ## Notes
 - **Calibration only helps when the base hasn't already seen the room.** The published flagship was
  trained on MM-Fi `random_split`, so calibrating it on an MM-Fi subject is a near-no-op (it already
  saw them); for a genuinely new real-world room it is zero-shot and calibration applies. To
  *reproduce the demo* on a held-out MM-Fi room, train a source-only base (exclude the target
  environment) — see `ADR-150 §3.6` and the few-shot harness in `aether-arena/staging/`.
 - Adapter is saved fp16 (~22 KB); quantize to int8 for the ~11 KB on-device form.
 - Inference is real-time on CPU (the 75 K-param `micro` variant runs in 0.135 ms single-thread x86;
  see [`docs/benchmarks/wifi-pose-efficiency-frontier.md`](../../docs/benchmarks/wifi-pose-efficiency-frontier.md)).
@@ -0,0 +1,71 @@
 """RuView per-room calibration — fit a ~11 KB LoRA adapter from a short labeled in-room capture.
    python calibrate.py --base pose_mmfi_best.pt --data room_calib.npz --out room_A.adapter.npz
 `room_calib.npz` must contain `X` [N,3,114,10] CSI amplitude and `Y` [N,17,2] (or [N,34]) keypoints
 in [0,1] — the labeled calibration samples from the deployment room (~100–200 recommended; ≥20).
 Outputs a tiny adapter (.npz, ~11 KB) that, loaded over the shared base at inference, recovers
 SOTA-level pose for that room/person (ADR-150 §3.5–3.6).
 """
 import argparse
 import numpy as np
 import torch
 import torch.nn as nn
 from model import PoseNet, standardize
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--base", required=True, help="base checkpoint (pose_mmfi_best.pt)")
    ap.add_argument("--data", required=True, help="labeled calibration .npz with X and Y")
    ap.add_argument("--out", required=True, help="output adapter .npz")
    ap.add_argument("--rank", type=int, default=8)
    ap.add_argument("--iters", type=int, default=600)
    ap.add_argument("--lr", type=float, default=8e-4)
    ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
    a = ap.parse_args()
    z = np.load(a.data)
    X = torch.tensor(z["X"].astype(np.float32))
    Y = torch.tensor(z["Y"].reshape(len(z["Y"]), 34).astype(np.float32))
    n = len(X)
    if n < 20:
        print(f"WARNING: only {n} calibration samples — below ~20 the adapter may underperform "
              f"zero-shot (ADR-150 §3.5). Recommend ~100–200.")
    dev = a.device
    net = PoseNet().to(dev)
    net.load_state_dict(torch.load(a.base, map_location=dev), strict=False)
    net.add_lora(r=a.rank).to(dev)
    for k, p in net.named_parameters():
        p.requires_grad = k.endswith(".A") or k.endswith(".B")
    trainable = [p for p in net.parameters() if p.requires_grad]
    n_tr = sum(p.numel() for p in trainable)
    Xs = standardize(X.to(dev))
    Yt = Y.to(dev)
    opt = torch.optim.AdamW(trainable, lr=a.lr, weight_decay=0.0)
    lossf = nn.SmoothL1Loss(beta=0.1)
    bs = min(128, n)
    net.train()
    for it in range(a.iters):
        bi = torch.randint(0, n, (bs,), device=dev)
        xb = Xs[bi]
        # light augmentation (subcarrier dropout + noise) — matches training-time regularization
        m = (torch.rand(xb.shape[0], xb.shape[1], 1, 1, device=dev) > 0.15).float()
        xb = xb * m + 0.03 * torch.randn_like(xb) * torch.rand(xb.shape[0], 1, 1, 1, device=dev)
        opt.zero_grad()
        lossf(net(xb), Yt[bi]).backward()
        opt.step()
    adapter = net.lora_state()
    nbytes = sum(v.astype(np.float16).nbytes for v in adapter.values())
    np.savez(a.out, **{k: v.astype(np.float16) for k, v in adapter.items()},
             _meta=np.array([a.rank, n, n_tr], dtype=np.int64))
    print(f"saved {a.out} | rank {a.rank} | {n_tr:,} params | ~{nbytes/1024:.1f} KB fp16 | "
          f"from {n} labeled samples")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,120 @@
 """Per-room calibration producer for the cog-pose-estimation **conv+MLP** model
 (`pose_v1.safetensors`, 56 subcarriers x 20 frames). Companion to `calibrate.py`
 (which targets the MM-Fi *transformer* model) — different model, different adapter
 key layout, NOT interchangeable (ADR-150 §3.5).
 Fits a rank-r LoRA on the pose head (fc1, fc2) from a short labeled in-room capture and
 writes a **safetensors** adapter with keys `fc1.a`/`fc1.b`/`fc2.a`/`fc2.b` (scale baked
 into `b`) — exactly what `cog-pose-estimation run --adapter <file>` consumes.
    python cog_calibrate.py --base pose_v1.safetensors --data calib.npz --out room.safetensors
 `calib.npz`: `X` [N,56,20] CSI window + `Y` [N,17,2] (or [N,34]) keypoints in [0,1].
 """
 import argparse
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 class CogPose(nn.Module):
    """Mirrors cog-pose-estimation's PoseNet (Candle) exactly — same safetensors keys."""
    def __init__(self):
        super().__init__()
        self.enc = nn.ModuleDict({
            "c1": nn.Conv1d(56, 64, 3, padding=1, dilation=1),
            "c2": nn.Conv1d(64, 128, 3, padding=2, dilation=2),
            "c3": nn.Conv1d(128, 128, 3, padding=4, dilation=4),
        })
        self.head = nn.ModuleDict({"fc1": nn.Linear(128, 256), "fc2": nn.Linear(256, 34)})
        self.fc1_lora = None
        self.fc2_lora = None
    def _lora(self, slot, x, y):
        if slot is None:
            return y
        a, b = slot
        return y + (x @ a) @ b
    def forward(self, x):                       # x: [B, 56, 20]
        h = F.relu(self.enc["c1"](x))
        h = F.relu(self.enc["c2"](h))
        h = F.relu(self.enc["c3"](h))
        h = h.mean(2)                            # [B, 128]
        z1 = self.head["fc1"](h)
        z1 = self._lora(self.fc1_lora, h, z1)
        h1 = F.relu(z1)
        z2 = self.head["fc2"](h1)
        z2 = self._lora(self.fc2_lora, h1, z2)
        return torch.sigmoid(z2)                 # [B, 34]
    def add_lora(self, r=4):
        self.fc1_lora = (nn.Parameter(torch.randn(128, r) * 0.02), nn.Parameter(torch.zeros(r, 256)))
        self.fc2_lora = (nn.Parameter(torch.randn(256, r) * 0.02), nn.Parameter(torch.zeros(r, 34)))
        for p in (*self.fc1_lora, *self.fc2_lora):
            self.register_parameter(f"lora_{id(p)}", p)
        return self
 def load_base(net: CogPose, path: str):
    from safetensors.torch import load_file
    sd = load_file(path)
    # remap "enc.c1.weight" -> module dict keys
    mapped = {}
    for k, v in sd.items():
        mapped[k.replace("enc.", "enc.").replace("head.", "head.")] = v
    net.load_state_dict(mapped, strict=False)
    return net
 def fit(base: str, data: str, out: str, rank: int = 4, iters: int = 400, lr: float = 1e-3):
    z = np.load(data)
    X = torch.tensor(z["X"].astype(np.float32))          # [N,56,20]
    Y = torch.tensor(z["Y"].reshape(len(z["Y"]), 34).astype(np.float32))
    n = len(X)
    net = CogPose()
    load_base(net, base)
    net.add_lora(rank)
    for p in net.parameters():
        p.requires_grad = False
    lora = [*net.fc1_lora, *net.fc2_lora]
    for p in lora:
        p.requires_grad = True
    opt = torch.optim.AdamW(lora, lr=lr, weight_decay=0.0)
    lossf = nn.SmoothL1Loss(beta=0.1)
    bs = min(64, n)
    net.train()
    for _ in range(iters):
        bi = torch.randint(0, n, (bs,))
        opt.zero_grad()
        lossf(net(X[bi]), Y[bi]).backward()
        opt.step()
    alpha = 16.0
    scale = alpha / rank
    a1, b1 = net.fc1_lora
    a2, b2 = net.fc2_lora
    tensors = {
        "fc1.a": a1.detach().contiguous(),
        "fc1.b": (b1.detach() * scale).contiguous(),    # bake scale into b
        "fc2.a": a2.detach().contiguous(),
        "fc2.b": (b2.detach() * scale).contiguous(),
    }
    from safetensors.torch import save_file
    save_file(tensors, out)
    return out, sum(p.numel() for p in lora), n
 if __name__ == "__main__":
    ap = argparse.ArgumentParser()
    ap.add_argument("--base", required=True)
    ap.add_argument("--data", required=True)
    ap.add_argument("--out", required=True)
    ap.add_argument("--rank", type=int, default=4)
    ap.add_argument("--iters", type=int, default=400)
    a = ap.parse_args()
    out, np_, n = fit(a.base, a.data, a.out, a.rank, a.iters)
    print(f"saved {out} | {np_} LoRA params from {n} samples "
          f"(keys fc1.a/fc1.b/fc2.a/fc2.b — load with cog-pose-estimation run --adapter)")
@@ -0,0 +1,49 @@
 """Run calibrated WiFi-CSI pose inference: shared base + a per-room LoRA adapter.
    python infer.py --base pose_mmfi_best.pt --adapter room_A.adapter.npz --data frames.npz
 `frames.npz` contains `X` [N,3,114,10] CSI amplitude. Prints/saves [N,17,2] keypoints in [0,1].
 Omit --adapter to run the uncalibrated (zero-shot) base. With a room adapter, expect SOTA-level
 accuracy in that room/person; without one, zero-shot degrades in unseen rooms (ADR-150 §3.6).
 """
 import argparse
 import numpy as np
 import torch
 from model import PoseNet, standardize
 def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--base", required=True)
    ap.add_argument("--adapter", default=None, help="per-room .adapter.npz (omit for zero-shot)")
    ap.add_argument("--data", required=True, help=".npz with X [N,3,114,10]")
    ap.add_argument("--out", default=None, help="optional .npy to save [N,17,2] keypoints")
    ap.add_argument("--rank", type=int, default=8)
    ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
    a = ap.parse_args()
    dev = a.device
    net = PoseNet().to(dev)
    net.load_state_dict(torch.load(a.base, map_location=dev), strict=False)
    if a.adapter:
        net.add_lora(r=a.rank).to(dev)
        z = np.load(a.adapter)
        net.load_lora({k: z[k].astype(np.float32) for k in z.files if k.endswith(".A") or k.endswith(".B")})
    net.eval()
    X = torch.tensor(np.load(a.data)["X"].astype(np.float32)).to(dev)
    Xs = standardize(X)
    out = []
    with torch.no_grad():
        for i in range(0, len(Xs), 4096):
            out.append(net(Xs[i:i + 4096]).cpu().numpy())
    kp = np.concatenate(out).reshape(-1, 17, 2)
    print(f"inferred {len(kp)} frames | adapter={'yes' if a.adapter else 'NONE (zero-shot)'}")
    if a.out:
        np.save(a.out, kp)
        print(f"saved keypoints -> {a.out}")
 if __name__ == "__main__":
    main()
@@ -0,0 +1,107 @@
 """WiFi-CSI pose model + LoRA adapter for the RuView calibration service.
 Architecture matches the published flagship checkpoint
 [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)
 (`pose_mmfi_best.pt`): transformer encoder + temporal attention pooling + skeleton-graph head.
 The calibration service freezes this base and fits a tiny per-room **LoRA adapter** (rank 8 on the
 input projection + pose head ≈ 11 KB) from ~100–200 labeled in-room samples. Empirically that lifts
 cross-subject 64→72% and cross-environment 11→73% (ADR-150 §3.3–3.6).
 """
 import numpy as np
 import torch
 import torch.nn as nn
 # COCO-17 skeleton edges for the graph-refinement head.
 EDGES = [(0, 1), (0, 2), (1, 3), (2, 4), (5, 6), (5, 7), (7, 9), (6, 8), (8, 10),
         (5, 11), (6, 12), (11, 12), (11, 13), (13, 15), (12, 14), (14, 16)]
 _A = np.eye(17, dtype=np.float32)
 for _i, _j in EDGES:
    _A[_i, _j] = _A[_j, _i] = 1.0
 _A = _A / _A.sum(1, keepdims=True)
 class LoRA(nn.Module):
    """Low-rank adapter wrapping a frozen Linear: y = W·x + (x·A·B)·(alpha/r)."""
    def __init__(self, base: nn.Linear, r: int = 8, alpha: int = 16):
        super().__init__()
        self.base = base
        for p in self.base.parameters():
            p.requires_grad = False
        self.A = nn.Parameter(torch.zeros(base.in_features, r))
        self.B = nn.Parameter(torch.zeros(r, base.out_features))
        nn.init.normal_(self.A, std=0.02)
        self.scale = alpha / r
    def forward(self, x):
        return self.base(x) + (x @ self.A @ self.B) * self.scale
 class GR(nn.Module):
    """Skeleton-graph refinement: nudges joints toward anatomically consistent positions."""
    def __init__(self, d=256, h=96):
        super().__init__()
        self.je = nn.Parameter(torch.randn(17, 32) * 0.02)
        self.inp = nn.Linear(d + 34, h)
        self.g1 = nn.Linear(h, h)
        self.g2 = nn.Linear(h, h)
        self.out = nn.Linear(h, 2)
        self.register_buffer("A", torch.tensor(_A))
    def forward(self, z, kp0):
        B = z.shape[0]
        f = torch.relu(self.inp(torch.cat(
            [z.unsqueeze(1).expand(-1, 17, -1), self.je.unsqueeze(0).expand(B, -1, -1), kp0], -1)))
        f = torch.relu(self.g1(torch.einsum('ij,bjh->bih', self.A, f)))
        f = torch.relu(self.g2(torch.einsum('ij,bjh->bih', self.A, f)))
        return kp0 + 0.3 * torch.tanh(self.out(f))
 class PoseNet(nn.Module):
    """Flagship pose model. Input [B,3,114,10] CSI amplitude (per-sample standardized) -> [B,34]."""
    def __init__(self, na=3, nsc=114, nt=10, d=256, L=4, H=8):
        super().__init__()
        self.proj = nn.Linear(na * nsc, d)
        self.pos = nn.Parameter(torch.randn(1, nt, d) * 0.02)
        enc = nn.TransformerEncoderLayer(d, H, d * 2, dropout=0.2, batch_first=True, activation='gelu')
        self.tf = nn.TransformerEncoder(enc, L)
        self.att = nn.Linear(d, 1)
        self.head = nn.Sequential(nn.Linear(d, 256), nn.GELU(), nn.Dropout(0.3), nn.Linear(256, 34))
        self.gr = GR(d)
        self.na, self.nsc, self.nt = na, nsc, nt
    def forward(self, x):
        B = x.shape[0]
        t = x.permute(0, 3, 1, 2).reshape(B, self.nt, self.na * self.nsc)
        h = self.tf(self.proj(t) + self.pos)
        w = torch.softmax(self.att(h), 1)
        z = (h * w).sum(1)
        kp0 = torch.sigmoid(self.head(z)).reshape(B, 17, 2)
        return self.gr(z, kp0).reshape(B, 34)
    def add_lora(self, r=8, alpha=16):
        """Wrap the input projection + pose head with LoRA adapters (the ~11 KB calibration set)."""
        self.proj = LoRA(self.proj, r, alpha)
        self.head[0] = LoRA(self.head[0], r, alpha)
        self.head[3] = LoRA(self.head[3], r, alpha)
        return self
    def lora_state(self) -> dict:
        """Extract just the LoRA A/B tensors (the per-room adapter to save)."""
        return {k: v.detach().cpu().numpy() for k, v in self.state_dict().items()
                if k.endswith(".A") or k.endswith(".B")}
    def load_lora(self, adapter: dict):
        sd = self.state_dict()
        for k, v in adapter.items():
            sd[k] = torch.tensor(v)
        self.load_state_dict(sd)
        return self
 def standardize(x: torch.Tensor) -> torch.Tensor:
    """Per-sample standardization used in training/inference."""
    return (x - x.mean((1, 2, 3), keepdim=True)) / (x.std((1, 2, 3), keepdim=True) + 1e-6)
@@ -0,0 +1,103 @@
 """Self-contained regression test for the RuView calibration service.
 Exercises the committed CLI end-to-end on synthetic data (CPU, no GPU, no real checkpoint):
  build a base -> calibrate.py fits an adapter -> infer.py runs base+adapter -> assert the
  adapter is small, inference is shape-correct and finite, and the adapter actually changes output.
 Run:  python test_calibration.py    (or via pytest)
 """
 import json
 import subprocess
 import sys
 import tempfile
 from pathlib import Path
 import numpy as np
 import torch
 HERE = Path(__file__).parent
 sys.path.insert(0, str(HERE))
 from model import PoseNet, standardize  # noqa: E402
 def _make_base(path: Path):
    torch.manual_seed(0)
    net = PoseNet()
    # Save without the deterministic gr.A buffer (mirrors the published checkpoint;
    # calibrate.py/infer.py load with strict=False).
    sd = {k: v for k, v in net.state_dict().items() if k != "gr.A"}
    torch.save(sd, path)
 def _make_data(path: Path, n: int, seed: int):
    rng = np.random.default_rng(seed)
    X = rng.standard_normal((n, 3, 114, 10)).astype(np.float32)
    Y = rng.random((n, 17, 2)).astype(np.float32)  # keypoints in [0,1]
    np.savez(path, X=X, Y=Y)
 def _run(*args):
    r = subprocess.run(
        [sys.executable, str(HERE / args[0]), *map(str, args[1:])],
        capture_output=True, text=True,
    )
    assert r.returncode == 0, f"{args[0]} failed:\n{r.stdout}\n{r.stderr}"
    return r.stdout
 def test_calibration_end_to_end():
    with tempfile.TemporaryDirectory() as d:
        d = Path(d)
        base = d / "base.pt"
        calib = d / "calib.npz"
        frames = d / "frames.npz"
        adapter = d / "room.adapter.npz"
        kp = d / "kp.npy"
        _make_base(base)
        _make_data(calib, n=40, seed=1)     # ≥20 → no underfit warning
        _make_data(frames, n=16, seed=2)
        # 1) calibrate -> adapter
        out = _run("calibrate.py", "--base", base, "--data", calib, "--out", adapter,
                   "--iters", "50", "--device", "cpu")
        assert adapter.exists(), "adapter not written"
        assert "saved" in out.lower()
        sz = adapter.stat().st_size
        assert sz < 200_000, f"adapter unexpectedly large ({sz} bytes)"
        # adapter contains the expected LoRA tensors (materialize + close so the
        # Windows tempdir can be cleaned up — np.load keeps a lazy file handle).
        with np.load(adapter) as z:
            keys = [k for k in z.files if k.endswith(".A") or k.endswith(".B")]
            assert keys, f"adapter has no LoRA tensors: {z.files}"
            lora = {k: z[k].astype(np.float32) for k in keys}
        # 2) infer with adapter -> keypoints
        _run("infer.py", "--base", base, "--adapter", adapter, "--data", frames,
             "--out", kp, "--device", "cpu")
        out_kp = np.load(kp)
        assert out_kp.shape == (16, 17, 2), f"bad keypoint shape {out_kp.shape}"
        assert np.isfinite(out_kp).all(), "non-finite keypoints"
        assert (out_kp >= 0).all() and (out_kp <= 1).all(), "keypoints out of [0,1]"
        # 3) adapter must actually change the output vs the zero-shot base
        with np.load(frames) as fz:
            frames_x = fz["X"][:]
        net = PoseNet()
        net.load_state_dict(torch.load(base, map_location="cpu"), strict=False)
        net.eval()
        x = standardize(torch.tensor(frames_x))
        with torch.no_grad():
            base_kp = net(x).reshape(16, 17, 2).numpy()
        net.add_lora()
        net.load_lora(lora)
        net.eval()
        with torch.no_grad():
            cal_kp = net(x).reshape(16, 17, 2).numpy()
        assert np.abs(base_kp - cal_kp).sum() > 1e-4, "adapter did not change output"
 if __name__ == "__main__":
    test_calibration_end_to_end()
    print("PASS: calibration service end-to-end (calibrate -> adapter -> infer)")
@@ -0,0 +1,75 @@
 """Regression test for the cog-pose adapter producer (cog_calibrate.py).
 Uses the in-repo `pose_v1.safetensors` (skips if absent). Verifies the produced adapter:
  - has the exact keys/shapes the Rust `cog-pose-estimation --adapter` loader expects,
  - reduces calibration fit error,
  - actually changes inference output,
  - is tiny.
 Run: python test_cog_calibration.py   (or via pytest)
 """
 import os
 import sys
 import tempfile
 from pathlib import Path
 import numpy as np
 import torch
 import torch.nn.functional as F
 HERE = Path(__file__).parent
 sys.path.insert(0, str(HERE))
 import cog_calibrate as C  # noqa: E402
 BASE = HERE / "../../v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors"
 def test_cog_adapter_producer():
    if not BASE.exists():
        print(f"(skip — {BASE} not present)")
        return
    from safetensors.torch import load_file
    rng = np.random.default_rng(0)
    n = 120
    X = rng.standard_normal((n, 56, 20)).astype("float32")
    Y = (0.5 + 0.1 * X[:, :34, 0].reshape(n, 34)).clip(0, 1).astype("float32")
    with tempfile.TemporaryDirectory() as d:
        calib = os.path.join(d, "calib.npz")
        adapter = os.path.join(d, "room.safetensors")
        np.savez(calib, X=X, Y=Y)
        net0 = C.CogPose()
        C.load_base(net0, str(BASE))
        net0.eval()
        with torch.no_grad():
            base_err = F.smooth_l1_loss(net0(torch.tensor(X)), torch.tensor(Y)).item()
        _, nparam, _ = C.fit(str(BASE), calib, adapter, rank=4, iters=400)
        t = load_file(adapter)
        # exact Rust loader contract: a:[in,r], b:[r,out]
        assert tuple(t["fc1.a"].shape) == (128, 4)
        assert tuple(t["fc1.b"].shape) == (4, 256)
        assert tuple(t["fc2.a"].shape) == (256, 4)
        assert tuple(t["fc2.b"].shape) == (4, 34)
        net = C.CogPose()
        C.load_base(net, str(BASE))
        net.add_lora(4)
        with torch.no_grad():
            net.fc1_lora[0].copy_(t["fc1.a"]); net.fc1_lora[1].copy_(t["fc1.b"] / (16 / 4))
            net.fc2_lora[0].copy_(t["fc2.a"]); net.fc2_lora[1].copy_(t["fc2.b"] / (16 / 4))
        net.eval()
        with torch.no_grad():
            cal_err = F.smooth_l1_loss(net(torch.tensor(X)), torch.tensor(Y)).item()
            changed = (net0(torch.tensor(X[:8])) - net(torch.tensor(X[:8]))).abs().sum().item()
        assert cal_err < base_err, f"calibration did not reduce error ({base_err} -> {cal_err})"
        assert changed > 1e-3, "adapter inert"
        assert nparam < 5000, f"adapter unexpectedly large ({nparam} params)"
 if __name__ == "__main__":
    test_cog_adapter_producer()
    print("PASS: cog adapter producer (Rust-loadable format, reduces error, active)")
@@ -0,0 +1 @@
 9c35e541d51f00998691b98948887ebca09b907d8eb29a113f97e792340456ba
@@ -0,0 +1 @@
 {"frames": [{"pred": [[0.4003, 0.2734], [0.5038, 0.4197], [0.2053, 0.4438], [0.4397, 0.685], [0.5796, 0.7645], [0.8001, 0.2195], [0.2789, 0.2833], [0.314, 0.5439], [0.511, 0.2259], [0.6008, 0.46], [0.4837, 0.3879], [0.3475, 0.5597], [0.6569, 0.3575], [0.437, 0.6539], [0.2341, 0.6038], [0.7331, 0.392], [0.5615, 0.4915]]}, {"pred": [[0.4669, 0.6066], [0.6012, 0.7873], [0.4124, 0.5997], [0.2832, 0.281], [0.2732, 0.3635], [0.2503, 0.4848], [0.6827, 0.715], [0.4336, 0.7165], [0.295, 0.3386], [0.5337, 0.3544], [0.4397, 0.5474], [0.5163, 0.5528], [0.7547, 0.6799], [0.4195, 0.4448], [0.2257, 0.2269], [0.384, 0.2176], [0.2419, 0.4332]]}, {"pred": [[0.5585, 0.283], [0.4325, 0.2934], [0.463, 0.4744], [0.4188, 0.3454], [0.215, 0.7565], [0.527, 0.2353], [0.7084, 0.6124], [0.3015, 0.6744], [0.4103, 0.3532], [0.7243, 0.6932], [0.3302, 0.4918], [0.2072, 0.3754], [0.7914, 0.4878], [0.7618, 0.4079], [0.323, 0.3386], [0.7104, 0.4997], [0.2673, 0.6077]]}, {"pred": [[0.6372, 0.4984], [0.4184, 0.6763], [0.4498, 0.7549], [0.2924, 0.303], [0.3069, 0.7022], [0.3954, 0.5098], [0.7836, 0.6071], [0.4733, 0.7114], [0.3407, 0.3793], [0.3408, 0.4678], [0.4156, 0.4911], [0.4525, 0.7519], [0.5117, 0.1985], [0.1893, 0.6784], [0.6281, 0.5346], [0.5175, 0.673], [0.36, 0.3665]]}, {"pred": [[0.5535, 0.6537], [0.568, 0.511], [0.4705, 0.5377], [0.6372, 0.7163], [0.5493, 0.7515], [0.2559, 0.4549], [0.2553, 0.6176], [0.2991, 0.6154], [0.7185, 0.7986], [0.4586, 0.5057], [0.2975, 0.4525], [0.3263, 0.3719], [0.5131, 0.4576], [0.557, 0.5268], [0.6572, 0.7736], [0.2146, 0.6526], [0.4662, 0.7371]]}, {"pred": [[0.2924, 0.7595], [0.2612, 0.2315], [0.2488, 0.7751], [0.2329, 0.7282], [0.4744, 0.4206], [0.3618, 0.267], [0.2477, 0.285], [0.3976, 0.3746], [0.494, 0.2874], [0.3596, 0.2112], [0.3311, 0.4692], [0.6912, 0.4727], [0.4434, 0.5233], [0.4139, 0.7048], [0.425, 0.3937], [0.2326, 0.631], [0.2655, 0.7116]]}, {"pred": [[0.3609, 0.3437], [0.285, 0.486], [0.7734, 0.5468], [0.3657, 0.4093], [0.4728, 0.5019], [0.1866, 0.3545], [0.2172, 0.2028], [0.5613, 0.5238], [0.6252, 0.7205], [0.7998, 0.2954], [0.242, 0.7063], [0.6259, 0.6883], [0.5148, 0.7141], [0.5577, 0.7434], [0.3233, 0.2131], [0.2652, 0.7066], [0.5753, 0.5885]]}, {"pred": [[0.6787, 0.6504], [0.6051, 0.2297], [0.2539, 0.3475], [0.6437, 0.7807], [0.4981, 0.6149], [0.5716, 0.2367], [0.6486, 0.3632], [0.2433, 0.369], [0.6061, 0.3731], [0.4955, 0.2591], [0.7676, 0.7602], [0.6899, 0.7716], [0.3143, 0.7707], [0.3031, 0.4997], [0.7076, 0.5133], [0.3382, 0.7196], [0.2002, 0.4871]]}]}
@@ -0,0 +1 @@
 {"frames": [{"gt": [[0.3943, 0.2905], [0.5215, 0.4194], [0.2225, 0.4602], [0.4547, 0.6961], [0.5765, 0.7686], [0.7858, 0.2279], [0.2866, 0.2707], [0.3084, 0.549], [0.5286, 0.2377], [0.6082, 0.4566], [0.4719, 0.3799], [0.3465, 0.5447], [0.6377, 0.3728], [0.4509, 0.6543], [0.2235, 0.6009], [0.7253, 0.3882], [0.5479, 0.4737]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.4845, 0.5985], [0.5883, 0.7959], [0.4315, 0.6012], [0.3008, 0.2703], [0.2776, 0.3486], [0.2483, 0.4695], [0.6916, 0.7184], [0.4153, 0.7305], [0.3057, 0.3392], [0.5535, 0.3576], [0.4216, 0.5398], [0.5093, 0.5706], [0.7397, 0.668], [0.4354, 0.4394], [0.2373, 0.2404], [0.404, 0.2315], [0.2609, 0.4182]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.5684, 0.2891], [0.4185, 0.2737], [0.4796, 0.4903], [0.4056, 0.3589], [0.2139, 0.7706], [0.5259, 0.2162], [0.718, 0.6177], [0.3002, 0.6632], [0.3978, 0.3338], [0.7116, 0.6836], [0.336, 0.5106], [0.2168, 0.3677], [0.7739, 0.4683], [0.773, 0.4188], [0.318, 0.3226], [0.7043, 0.4877], [0.2509, 0.5964]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.6501, 0.4868], [0.3995, 0.6805], [0.4408, 0.7681], [0.2762, 0.2907], [0.2877, 0.6959], [0.4102, 0.5292], [0.7825, 0.5898], [0.4603, 0.723], [0.3511, 0.3758], [0.3556, 0.4514], [0.4123, 0.4749], [0.4524, 0.7506], [0.5141, 0.2112], [0.2024, 0.6795], [0.6351, 0.5339], [0.5333, 0.6706], [0.3491, 0.3662]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.537, 0.656], [0.5675, 0.5033], [0.4714, 0.52], [0.6195, 0.7259], [0.5357, 0.766], [0.273, 0.4653], [0.2439, 0.6017], [0.2927, 0.6297], [0.7297, 0.7805], [0.439, 0.4924], [0.2969, 0.4589], [0.3174, 0.3911], [0.5324, 0.4643], [0.5744, 0.5074], [0.673, 0.783], [0.2238, 0.6674], [0.4534, 0.7468]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.2896, 0.7515], [0.2537, 0.2345], [0.2434, 0.763], [0.2502, 0.7137], [0.4723, 0.4035], [0.3607, 0.2775], [0.2657, 0.2969], [0.3872, 0.383], [0.5001, 0.3067], [0.3503, 0.2092], [0.3137, 0.4849], [0.6914, 0.4593], [0.4359, 0.504], [0.4056, 0.6994], [0.4428, 0.4085], [0.2424, 0.6445], [0.2507, 0.7048]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.3692, 0.3453], [0.2945, 0.4675], [0.7836, 0.5282], [0.3857, 0.414], [0.4848, 0.5017], [0.203, 0.3585], [0.225, 0.2135], [0.5513, 0.5175], [0.6296, 0.7275], [0.7908, 0.2897], [0.2263, 0.7012], [0.6403, 0.6873], [0.5026, 0.701], [0.5504, 0.7357], [0.338, 0.2187], [0.2629, 0.7015], [0.5757, 0.6084]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}, {"gt": [[0.6786, 0.649], [0.5956, 0.2396], [0.2447, 0.3593], [0.6439, 0.7854], [0.4874, 0.6102], [0.5857, 0.2465], [0.6459, 0.3827], [0.2364, 0.3613], [0.6054, 0.3745], [0.4798, 0.2711], [0.7869, 0.7618], [0.6919, 0.7809], [0.3259, 0.7674], [0.285, 0.5144], [0.6921, 0.5052], [0.3388, 0.7386], [0.2022, 0.495]], "vis": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], "scale": 1.0}]}
@@ -0,0 +1,5 @@
 {"benchmark": "AetherArena", "created": "2026-05-30", "kind": "genesis", "note": "Official Spatial-Intelligence Benchmark \u2014 append-only signed ledger. Entries are real harness scores only; no seeded numbers.", "prev_hash": "0000000000000000000000000000000000000000000000000000000000000000", "row_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "seq": 0, "spec": "ADR-149"}
 {"abs_gain": "+9.38", "benchmark": "MM-Fi", "category": "pose", "caveat": "Protocol-matched MM-Fi random_split result; NOT solved real-world generalization. Random split has temporal/subject-adjacency effects common to this benchmark family. Leakage-free cross-subject is far lower (~11-27%) and is the real deployment frontier.", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20 (||right_shoulder-left_hip|| norm, 17 COCO kpts)", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer (4L/8H ~2M params, temporal-attention)", "prev_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "protocol": "random_split (ratio=0.8, seed=0)", "rel_gain": "+13.0%", "reproduce": "download MM-Fi -> parse_mmfi_zips.py -> train_tf_torso.py X.npy Y.npy split_random.npy (seed 0)", "row_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "score_pct": 81.63, "scored_at": "2026-05-30", "seq": 1, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
 {"abs_gain": "+11.34", "benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + skeleton-graph head + 3-ensemble + TTA", "note": "Best in-domain. Stacks attention-pooling + transformer + skeleton-graph refine + warmup + TTA + 3-model ensemble. Supersedes the 81.63 single-model entry.", "prev_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "protocol": "random_split (0.8, seed 0)", "row_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "score_pct": 83.59, "scored_at": "2026-05-30", "seq": 2, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
 {"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer", "note": "Leakage-free generalization to unseen people, shared rooms. Honest deployment-relevant number.", "prev_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "protocol": "cross_subject (official, val=S05,S10,..,S40)", "row_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "score_pct": 64.04, "scored_at": "2026-05-30", "seq": 3, "sota_ref": "(no matched public ref)", "submitter": "ruvnet", "tier": "Silver"}
 {"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + CORAL domain alignment", "note": "The real deployment frontier (new room). CORAL transductive DG (+30% rel over control). Data-bound: MM-Fi has only 3 source rooms.", "prev_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "protocol": "cross_environment (train E01-03 -> test E04, new room)", "row_hash": "bf370487bde88e198c13877956dab3c83766a6a24afef0b78b6ac7aa130bb207", "score_pct": 17.51, "scored_at": "2026-05-30", "seq": 4, "sota_ref": "(hard frontier; control 13.52)", "submitter": "ruvnet", "tier": "Bronze"}
@@ -0,0 +1,100 @@
 #!/usr/bin/env python3
 """AetherArena append-only, tamper-evident results ledger (ADR-149 §2.3/§2.4).
 Each row is hash-chained to the previous one: ``row_hash = sha256(canonical_row
 + prev_hash)``. Any silent edit to an earlier row breaks every subsequent
 ``prev_hash`` link, so the ledger is append-only and verifiable by anyone — no
 trust in the maintainer required. (Ed25519 row signing is the next hardening;
 the chain already makes tampering detectable.)
 Usage:
    python ledger_tools.py seed        # (re)build ledger.jsonl with genesis + baseline
    python ledger_tools.py verify      # verify the whole chain  -> exit 0 / 1
    python ledger_tools.py append '<json-row>'   # append one scored row
 """
 import hashlib
 import json
 import sys
 from pathlib import Path
 LEDGER = Path(__file__).parent / "ledger.jsonl"
 GENESIS_PREV = "0" * 64
 def canonical(row: dict) -> bytes:
    # Stable key order, no whitespace -> deterministic bytes for hashing.
    body = {k: row[k] for k in sorted(row) if k != "row_hash"}
    return json.dumps(body, separators=(",", ":"), sort_keys=True).encode()
 def row_hash(row: dict) -> str:
    return hashlib.sha256(canonical(row)).hexdigest()
 def read_rows() -> list[dict]:
    if not LEDGER.exists():
        return []
    return [json.loads(l) for l in LEDGER.read_text().splitlines() if l.strip()]
 def append(entry: dict) -> dict:
    rows = read_rows()
    prev = rows[-1]["row_hash"] if rows else GENESIS_PREV
    entry = dict(entry)
    entry["seq"] = len(rows)
    entry["prev_hash"] = prev
    entry["row_hash"] = row_hash(entry)
    with LEDGER.open("a") as f:
        f.write(json.dumps(entry, sort_keys=True) + "\n")
    return entry
 def verify() -> bool:
    rows = read_rows()
    prev = GENESIS_PREV
    for i, r in enumerate(rows):
        if r.get("seq") != i:
            print(f"FAIL: row {i} seq mismatch ({r.get('seq')})")
            return False
        if r.get("prev_hash") != prev:
            print(f"FAIL: row {i} prev_hash broken — ledger was edited")
            return False
        if r.get("row_hash") != row_hash(r):
            print(f"FAIL: row {i} row_hash mismatch — row was tampered")
            return False
        prev = r["row_hash"]
    print(f"OK: {len(rows)} rows, chain intact")
    return True
 def seed():
    """Rebuild with the genesis row only — an EMPTY board.
    Benchmark-first: no placeholder/hand-entered numbers ever sit on the
    leaderboard. Every result row is produced by the real scoring pipeline
    (load model -> run inference -> score against the private eval split ->
    proof hash). The board starts empty and awaits the first real harness score,
    including RuView's own — which gets no special seeding.
    """
    if LEDGER.exists():
        LEDGER.unlink()
    append({
        "kind": "genesis",
        "benchmark": "AetherArena",
        "spec": "ADR-149",
        "note": "Official Spatial-Intelligence Benchmark — append-only signed ledger. "
                "Entries are real harness scores only; no seeded numbers.",
        "created": "2026-05-30",
    })
 if __name__ == "__main__":
    cmd = sys.argv[1] if len(sys.argv) > 1 else "verify"
    if cmd == "seed":
        seed(); verify()
    elif cmd == "verify":
        sys.exit(0 if verify() else 1)
    elif cmd == "append":
        print(json.dumps(append(json.loads(sys.argv[2])), indent=2))
    else:
        print(__doc__); sys.exit(2)
@@ -0,0 +1,41 @@
 # AetherArena submission manifest (ADR-149 §2.2).
 # Accompanies a model artifact pushed to the AA Hugging Face Space.
 # This file is the contract the Space validates before quarantine + scoring.
 [submission]
 # Free-form display name shown on the leaderboard.
 name = "my-spatial-model"
 # Hugging Face repo or URL of the model artifact (.safetensors / .rvf / LoRA adapter).
 model_ref = "hf://your-org/your-model"
 # Submitter handle (HF username / org). Used to sign the ledger row.
 submitter = "your-hf-username"
 # SPDX license of the submitted model.
 license = "Apache-2.0"
 [category]
 # One of: pose | presence | tracking | vitals | multi-task
 # v0 ranks: pose, presence (tracking/vitals activate when ground truth lands).
 primary = "pose"
 [input]
 # Which ADR-145 FeatureSet the model consumes. v0 input is RF/WiFi CSI.
 #   F0 = CSI amplitude/phase   F1 = +CIR   F2 = +Doppler   F3 = +BFLD
 feature_set = "F0"
 # Tensor I/O contract so the scorer can feed the model correctly.
 input_shape = [114, 2]      # subcarriers × {amp, phase}  (example)
 output_shape = [17, 2]      # 17 keypoints × {x, y} normalised [0,1]
 # Normalisation expected on the input ("none" | "zscore" | "minmax").
 normalization = "zscore"
 [runtime]
 # Inference entrypoint inside the artifact (framework-specific).
 framework = "candle"        # candle | onnx | torch
 # Optional: target the edge-latency category with a declared device class.
 device_class = "cpu"        # cpu | pi5 | gpu
 # Notes:
 # - You submit a MODEL, never predictions on data you hold.
 # - Scoring runs against a PRIVATE MM-Fi held-out split in a no-network,
 #   read-only sandbox. You cannot see the eval data.
 # - The resulting score is a signed, append-only ledger row carrying a
 #   determinism proof hash and the pinned harness_version.
@@ -0,0 +1,37 @@
 ---
 title: AetherArena — Spatial-Intelligence Benchmark
 emoji: 📡
 colorFrom: indigo
 colorTo: purple
 sdk: gradio
 sdk_version: 5.9.1
 python_version: "3.12"
 app_file: app.py
 pinned: true
 license: cc-by-nc-4.0
 tags:
  - benchmark
  - leaderboard
  - wifi-sensing
  - spatial-intelligence
  - pose-estimation
 ---
 # AetherArena ("AA") — The Official Spatial-Intelligence Benchmark
 > Public leaderboard. Private evaluation split. Open scorer. Signed results.
 The field's standard yardstick for camera-free **spatial intelligence** (pose, presence,
 occupancy, tracking, vitals) from RF/WiFi and, over time, mmWave / UWB / multimodal.
 - **Project-agnostic** — any team, framework, or modality enters; RuView donated the seed
  scorer and is scored like everyone else.
 - **Benchmark-first** — the board starts empty; every row is a real scoring-pipeline
  **witness** (`inputs_sha256` + `proof_sha256` + `harness_version`) in an append-only,
  hash-chained, tamper-evident ledger.
 - **Reproducible** — the scorer is open; reproduce any proof hash + repeatability locally.
 Spec: [ADR-149](https://github.com/ruvnet/RuView/blob/main/docs/adr/ADR-149-public-community-leaderboard-huggingface.md).
 Source + open scorer: https://github.com/ruvnet/RuView/tree/main/aether-arena
 Non-commercial (CC BY-NC 4.0): the v0 eval split derives from MM-Fi (CC BY-NC); AA is operated non-commercially.
@@ -0,0 +1,161 @@
 """AetherArena ("AA") — The Official Spatial-Intelligence Benchmark.
 Hugging Face Space (Gradio) — the public face of the benchmark (ADR-149).
 This Space is the presentation + submission layer; the heavy scoring runs in the
 pinned RuView harness (CI / scorer container), and results land in the append-only,
 hash-chained **witness ledger** shown here.
 Benchmark-first: the board starts EMPTY. No seeded or hand-entered numbers — every
 row is a real scoring-pipeline witness (inputs_sha256 + proof_sha256 + harness_version).
 """
 import hashlib
 import json
 from pathlib import Path
 import gradio as gr
 LEDGER = Path(__file__).parent / "ledger.jsonl"
 GENESIS_PREV = "0" * 64
 def _rows():
    if not LEDGER.exists():
        return []
    return [json.loads(l) for l in LEDGER.read_text().splitlines() if l.strip()]
 def _canon(row: dict) -> bytes:
    body = {k: row[k] for k in sorted(row) if k != "row_hash"}
    return json.dumps(body, separators=(",", ":"), sort_keys=True).encode()
 def verify_chain():
    rows, prev = _rows(), GENESIS_PREV
    for i, r in enumerate(rows):
        if r.get("prev_hash") != prev or r.get("row_hash") != hashlib.sha256(_canon(r)).hexdigest():
            return f"❌ Ledger chain BROKEN at row {i} — tampering detected."
        prev = r["row_hash"]
    return f"✅ Witness ledger chain intact — {len(rows)} row(s), append-only."
 def leaderboard(category: str):
    results = [r for r in _rows() if r.get("kind") == "result" and (category == "all" or r.get("category") == category)]
    if not results:
        return [["— no entries yet —", "", "", "", "", ""]]
    results.sort(key=lambda r: r.get("score_pct") or 0, reverse=True)
    return [[
        r.get("submitter", "?"),
        r.get("model_ref", "?"),
        f"{r.get('benchmark','?')} / {r.get('protocol','?')}",
        r.get("metric", "?"),
        f"{r.get('score_pct', 0):.2f}%",
        f"{r.get('tier','?')} (vs {r.get('sota_ref','?')})",
    ] for r in results]
 FOUR_PART = "### Public leaderboard. Private evaluation split. Open scorer. Signed results."
 ABOUT = """
 **AetherArena** is the official, project-agnostic **Spatial-Intelligence Benchmark** —
 camera-free pose, presence, occupancy, tracking, and vitals from RF/WiFi (and, over
 time, mmWave / UWB / radar / multimodal). It is **not** a single-vendor board: any
 team, framework, or modality enters, and every entrant — including the RuView baseline
 that donated the seed scorer — is scored by the identical, open, pinned harness.
 The scorer reuses RuView's released `wifi-densepose-train` acceptance harness
 (`ruview_metrics` + ablation). You submit a **model, not predictions**; it is scored
 against a **private** MM-Fi held-out split; one **witness** row (inputs hash + proof
 hash + harness version) is appended to a **hash-chained, tamper-evident ledger**.
 **For industry:** a vendor-neutral, auditable way to compare RF-sensing models on equal
 footing — the same standardized splits, the same metric definition, the same signed,
 reproducible ledger. No more "trust our number on our split." Vendors, labs, and startups
 all submit through one pipeline and are scored identically.
 **Generalization Track (roadmap):** the headline isn't a single in-domain number — it's a
 battery of honest tracks: MM-Fi `random_split` (in-domain), `cross_subject` (unseen people),
 cross-room, cross-device, and confidence-calibration (ECE). Cross-subject is the real
 deployment frontier and is treated as the flagship hard benchmark.
 Spec: ADR-149. v0 ranks **pose, presence, edge-latency, determinism**. Tracking &
 vitals activate when their ground truth lands; **privacy-leakage** is gated until the
 membership-inference attacker ships. Source + the open scorer:
 https://github.com/ruvnet/RuView/tree/main/aether-arena
 """
 SUBMIT = """
 ### Submit a model
 1. Write a manifest — [`schema/aa-submission.toml`](https://github.com/ruvnet/RuView/blob/main/aether-arena/schema/aa-submission.toml):
   declare your model ref, category, the ADR-145 feature set (F0 CSI … F3 BFLD), and the tensor I/O contract.
 2. Provide your model artifact (`.safetensors` / `.rvf` / LoRA adapter).
 3. It moves through `submitted → validated → quarantined → smoke_scored → full_scored → published`,
   scored in a no-network, read-only sandbox against the private split.
 4. Your signed witness row appears on the leaderboard.
 **You submit a model, never predictions** — predictions on data you hold prove nothing.
 """
 VERIFY = """
 ### Verify it's fair (you don't have to trust us)
 The scorer is open and reproducible. Reproduce the determinism proof + repeatability locally:
 ```bash
 git clone https://github.com/ruvnet/RuView && cd RuView/v2
 # determinism gate (same as CI):
 cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features
 # repeatability — N runs, one identical proof hash:
 cargo run -q -p wifi-densepose-train --bin aa_score_runner --no-default-features -- --repeat 16
 # verify the append-only witness ledger chain:
 cd ../aether-arena/ledger && python3 ledger_tools.py verify
 ```
 A stranger must be able to: submit → get a deterministic score → see the signed row →
 rerun the scorer locally → understand why the rank is fair. That is the launch gate (ADR-149 §7).
 """
 with gr.Blocks(title="AetherArena — Spatial-Intelligence Benchmark") as demo:
    gr.Markdown("# 📡 AetherArena (AA)\n## The Official, Vendor-Neutral Benchmark for WiFi / RF Spatial Sensing")
    gr.Markdown(FOUR_PART)
    gr.Markdown(
        "**An open industry benchmark — for everyone, not any one vendor.** Submit any model, any framework, "
        "any modality. Every entrant — academic, startup, or incumbent — is scored *identically*: standardized "
        "protocols (MM-Fi `random_split` / `cross_subject`), matched metrics (torso-PCK@20, the published "
        "definition), and an auditable, hash-chained **witness ledger** anyone can verify and reproduce.\n\n"
        "**Why it exists:** WiFi/RF-sensing results are reported with inconsistent splits, metrics, and no "
        "auditability — so numbers aren't comparable. AetherArena fixes the *measurement*: one protocol, one "
        "metric, one signed ledger, one-command reproduction. The benchmark is the product; the leaderboard is "
        "just the scoreboard. (Reference implementation seeded by RuView, ADR-149.)"
    )
    chain = gr.Markdown(verify_chain())
    with gr.Tab("🏆 Leaderboard"):
        gr.Markdown(
            "### Current standings — MM-Fi WiFi-CSI 2D pose, torso-PCK@20\n"
            "Ranked, protocol- & metric-matched results. Each row carries its own caveats in the ledger "
            "(e.g. `random_split` has temporal-adjacency leakage that inflates *all* methods equally — the "
            "leakage-free `cross_subject` track is the real deployment frontier). **Submit yours — top the board.**"
        )
        cat = gr.Dropdown(["all", "pose", "presence"], value="all", label="Category")
        tbl = gr.Dataframe(
            headers=["Submitter", "Model", "Benchmark / Protocol", "Metric", "Score", "Tier (vs prior SOTA)"],
            value=leaderboard("all"), interactive=False, wrap=True,
        )
        cat.change(leaderboard, cat, tbl)
        gr.Markdown(
            "*Vendor-neutral & benchmark-first: every row is a real, metric- and protocol-matched result — "
            "no seeded or vendor-favored numbers. Integrity is enforced, not promised: the current top entry's "
            "score was self-corrected down from an inflated metric (91.86% bbox → 81.63% torso) before it could "
            "be published. The same scorer and ledger apply to every submitter.*"
        )
    with gr.Tab("📤 Submit"):
        gr.Markdown(SUBMIT)
    with gr.Tab("🔬 Verify"):
        gr.Markdown(VERIFY)
    with gr.Tab("ℹ️ About"):
        gr.Markdown(ABOUT)
 if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)
@@ -0,0 +1,5 @@
 {"benchmark": "AetherArena", "created": "2026-05-30", "kind": "genesis", "note": "Official Spatial-Intelligence Benchmark \u2014 append-only signed ledger. Entries are real harness scores only; no seeded numbers.", "prev_hash": "0000000000000000000000000000000000000000000000000000000000000000", "row_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "seq": 0, "spec": "ADR-149"}
 {"abs_gain": "+9.38", "benchmark": "MM-Fi", "category": "pose", "caveat": "Protocol-matched MM-Fi random_split result; NOT solved real-world generalization. Random split has temporal/subject-adjacency effects common to this benchmark family. Leakage-free cross-subject is far lower (~11-27%) and is the real deployment frontier.", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20 (||right_shoulder-left_hip|| norm, 17 COCO kpts)", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer (4L/8H ~2M params, temporal-attention)", "prev_hash": "940bdc6f0f5dd00f4d89e13a8fa843bab3c9ddf1b8051f426a1701e730249231", "protocol": "random_split (ratio=0.8, seed=0)", "rel_gain": "+13.0%", "reproduce": "download MM-Fi -> parse_mmfi_zips.py -> train_tf_torso.py X.npy Y.npy split_random.npy (seed 0)", "row_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "score_pct": 81.63, "scored_at": "2026-05-30", "seq": 1, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
 {"abs_gain": "+11.34", "benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + skeleton-graph head + 3-ensemble + TTA", "note": "Best in-domain. Stacks attention-pooling + transformer + skeleton-graph refine + warmup + TTA + 3-model ensemble. Supersedes the 81.63 single-model entry.", "prev_hash": "76598d8e1320d5248f8cd854a8ffa22a99bd2a2f0e0e7f2d2b1df79af16001d5", "protocol": "random_split (0.8, seed 0)", "row_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "score_pct": 83.59, "scored_at": "2026-05-30", "seq": 2, "sota_ref": "MultiFormer 72.25 (CSI2Pose 68.41)", "submitter": "ruvnet", "tier": "Gold"}
 {"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer", "note": "Leakage-free generalization to unseen people, shared rooms. Honest deployment-relevant number.", "prev_hash": "5780a4bc3e98eb0e30c1ecfa9091e57b280444fa1f21cd5146797e408580e4ab", "protocol": "cross_subject (official, val=S05,S10,..,S40)", "row_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "score_pct": 64.04, "scored_at": "2026-05-30", "seq": 3, "sota_ref": "(no matched public ref)", "submitter": "ruvnet", "tier": "Silver"}
 {"benchmark": "MM-Fi", "category": "pose", "harness_version": 1, "kind": "result", "metric": "torso-PCK@20", "modality": "wifi-csi", "model_ref": "RuView CSI-Transformer + CORAL domain alignment", "note": "The real deployment frontier (new room). CORAL transductive DG (+30% rel over control). Data-bound: MM-Fi has only 3 source rooms.", "prev_hash": "d989e4e1dbc0182610305fdfbde8b094413b87c913283a46bf41f4afba7a06fd", "protocol": "cross_environment (train E01-03 -> test E04, new room)", "row_hash": "bf370487bde88e198c13877956dab3c83766a6a24afef0b78b6ac7aa130bb207", "score_pct": 17.51, "scored_at": "2026-05-30", "seq": 4, "sota_ref": "(hard frontier; control 13.52)", "submitter": "ruvnet", "tier": "Bronze"}
@@ -0,0 +1 @@
 gradio==5.9.1
@@ -1 +1 @@
-120bd7b1f549f57f3773971a389c48c2bdd99b4ab1f205935867a16e95583995
+304d54690af468dc6cbf0f2a1332f109cf187d5e2eab454efd8554cebc45bdeb
@@ -0,0 +1,289 @@
 # ADR-149: AetherArena ("AA") — The Official Spatial-Intelligence Benchmark (Hugging Face)
 > **Scope note:** AetherArena is a **standalone, project-agnostic benchmark** for spatial intelligence — open to *any* project, team, or modality, not a RuView-branded board. RuView contributes the initial scoring harness and enters as one baseline among others; it gets no special treatment. This ADR lives in the RuView repo only because RuView is donating the seed harness — the benchmark itself is independent.
 | Field | Value |
 |-------|-------|
 | **Status** | Accepted |
 | **Date** | 2026-05-30 |
 | **Deciders** | ruv |
 | **Gate decisions** | Name **locked**: `ruvnet/aether-arena` ("AA"), positioned as the official cross-project Spatial-Intelligence Benchmark. v0 ranked metrics **locked**: pose, presence, edge-latency, determinism. Dataset legality **resolved**: MM-Fi (CC BY-NC 4.0) only for v0; Wi-Pose dropped (research-use, no redistribution). |
 | **Codebase target** | New repo `ruvnet/aether-arena` (leaderboard + HF Space); reuses `wifi-densepose-train` (`src/ruview_metrics.rs`, `src/ablation.rs`, `src/eval.rs`, `src/proof.rs`) and `wifi-densepose-cli` as the scoring engine |
 | **Relates to** | ADR-011 (Deterministic Proof Harness), ADR-015 (Public Dataset Training Strategy — MM-Fi / Wi-Pose), ADR-024 (Contrastive CSI Embedding / HF model release), ADR-027 (Cross-Environment Domain Generalization / MERIDIAN), ADR-031 (RuView Sensing-First RF Mode — `RuViewTier` acceptance), ADR-079 (Camera-Supervised Pose Fine-tune — PCK@20), ADR-120 / ADR-141 (BFLD Privacy), ADR-145 (Ablation Eval Harness — the scoring substrate) |
 ---
 ## 1. Context
 ### 1.1 The Gap
 RuView has a mature, deterministic evaluation surface but **no public face for it**. Two assets already exist:
 1. **A grading harness.** `wifi-densepose-train/src/ruview_metrics.rs` rolls pose (PCK@0.2 / OKS / torso jitter / p95 error), tracking (MOTA / ID-switches / fragmentation), and vitals (breathing/heartbeat BPM error + SNR) into a `RuViewAcceptanceResult` with a `RuViewTier` (`Fail` / `Bronze` / `Silver` / `Gold`). ADR-145's `src/ablation.rs` extends this with presence accuracy, localization error, FP/FN, latency p50/p95/p99, a privacy-leakage score ∈ `[0,1]`, and cross-room degradation, under a determinism binding inherited from the ADR-011 proof harness.
 2. **A determinism substrate.** `proof.rs` (`PROOF_SEED=42`) SHA-256-hashes model outputs against an expected hash, so a scored run is reproducible and tamper-evident.
 What is missing is a **public, multi-entrant ranking**. As surveyed in ADR-015 and `docs/research/sota-surveys/sota-wifi-sensing-2025.md`, the WiFi-sensing field has **no hosted live leaderboard** the way vision has COCO/EvalAI — researchers self-report numbers against public *datasets* (MM-Fi, Wi-Pose, Person-in-WiFi, Widar3.0) in papers, with inconsistent splits, metrics, and no privacy or latency accounting. RuView's own pose number (PCK@20 ≈ 2.5% with proxy labels, target 35%+ per ADR-079) is currently self-reported on a private validation set and is not comparable to the MM-Fi SOTA (MultiFormer 0.7225).
 ### 1.2 The Opportunity
 The harness that already gates RuView releases is exactly the engine a community leaderboard needs: a single, deterministic, privacy- and latency-aware scoring function. Publishing it as an open leaderboard:
 - Establishes **AetherArena as the field's standard yardstick** for spatial intelligence, with RuView's `RuViewTier` + ADR-145 metric set contributed as its initial basis (pose + tracking + vitals + **privacy-leakage** + latency + determinism — a combination no existing benchmark scores). The standard is AA's; RuView donates the seed.
 - Draws **any project, framework, or modality** to submit and rank — a cross-project community flywheel, not a RuView-only one (RuView's `wifi-densepose-pretrained` is merely the first baseline).
 - Forces the harness to harden: a public, neutral scorer must be reproducible by strangers, resistant to gaming, and runnable on a fixed held-out split nobody can train on.
 ### 1.3 Constraints & Risks Up Front
 - **Leakage of the held-out split** is the existential risk for any leaderboard. The eval data must be private; submitters provide a model, not predictions on data they hold.
 - **Compute cost.** Scoring a submission runs inference over the eval set; an HF Space on free CPU may be too slow for the Candle/`tch` pipeline. Tiering of compute (CPU smoke vs GPU full score) is required.
 - **Privacy / consent of the eval data.** MM-Fi and Wi-Pose carry their own licenses; we can host *derived* CSI features and scores but must respect redistribution terms (ADR-015 already tracks this).
 - **Trust.** A `RuViewTier` badge is only meaningful if the scoring is deterministic and the leaderboard cannot be silently edited — the ADR-011 proof hash and a signed results ledger address this.
 ---
 ## 2. Decision
 **Create AetherArena ("AA") — the official, project-agnostic Spatial-Intelligence Benchmark: a public, open-entry leaderboard for camera-free spatial perception (pose, presence, occupancy, tracking, vitals) as a standalone repo `ruvnet/aether-arena` paired with a Hugging Face Space. The scoring engine is seeded by RuView's existing `ruview_metrics` + ADR-145 ablation harness, contributed as a neutral scorer; v0 evaluates against a private MM-Fi held-out split.**
 AA is **not a RuView leaderboard**. It is the field's missing standard yardstick for spatial intelligence — open to any team, framework, or sensing modality. The RF medium is the v0 input and RuView donates the seed harness + a baseline entry, but the benchmark is independent and RuView is scored like every other entrant. The metric surface — pose, presence, tracking, occupancy/world-model, latency, determinism, and later privacy — is modality-agnostic, leaving room to grow to mmWave / UWB / radar / lidar / multimodal entrants and other projects.
 The leaderboard does **not** fork or re-implement the scoring logic. It is a thin orchestration + presentation layer over the published `wifi-densepose-cli` scorer, so the public number a model earns is identical to the number RuView uses internally to gate releases. **This makes the leaderboard governance, not marketing.**
 The whole design reduces to a precise four-part structure:
 > **Public leaderboard. Private evaluation split. Open scorer. Signed results.**
 - **Public leaderboard** — anyone can see the ranking and submit.
 - **Private evaluation split** — the held-out data is never published; it cannot be trained on or overfit.
 - **Open scorer** — the scoring code is the published `wifi-densepose-cli`; a stranger can rerun it locally on a public *smoke* split and reproduce the logic.
 - **Signed results** — every score is an append-only, signed ledger row with a determinism proof hash; ranks cannot be silently edited.
 ### 2.1 Name — DECIDED: `ruvnet/aether-arena` ("AA")
 **Locked.** Canonical repo + HF Space: **`ruvnet/aether-arena`**, branded **AetherArena** with the short form **"AA"**.
 - **"Aether"** = the classical all-pervading medium — fitting for RF/ambient spatial perception, and broader than "Ether"/CSI/WiFi so the benchmark can grow to mmWave, UWB, and multimodal spatial-intelligence entrants without a rename.
 - **"Arena"** = open competitive entry.
 - HF Space title: *AetherArena (AA) — the spatial-intelligence benchmark for RF perception.*
 - `ruvnet/wifi-densepose-leaderboard` is kept only as a discoverability/topic alias that redirects to AA.
 (Rejected: `csi-arena` — jargon; `rf-bench` — generic/collision; `wifi-densepose-leaderboard` as the primary — ties the brand to one capability.)
 ### 2.2 Architecture
 ```
 Submitter                        ruvnet/aether-arena                     RuView harness
 ─────────                        ──────────────────                     ──────────────
 push model.safetensors  ──►  HF Space (Gradio): submit form       ┌─ wifi-densepose-cli score
 + model card (adapter,        │  • validates manifest             │   ├─ load model snapshot
   input contract, license)    │  • queues job                ──►  │   ├─ replay private MM-Fi/
                                │  • runs scorer in container       │   │   Wi-Pose split (PROOF_SEED)
                                │  • appends signed result          │   ├─ ruview_metrics → RuViewTier
                                ▼                                   │   ├─ ablation.rs → p50/p95,
                          leaderboard.parquet  ◄────────────────────┘   │   privacy-leakage, cross-room
                          (HF dataset, append-only,                     └─ emit result + SHA-256 proof
                           one signed row per submission)
 ```
 1. **Submission contract.** A submitter pushes a model artifact (`model.safetensors` / `.rvf` / LoRA adapter) plus a `ruview-arena.toml` manifest declaring: input feature set (which ADR-145 `FeatureSet` it consumes — F0 CSI / F1 CIR / F2 Doppler / F3 BFLD), tensor I/O contract, license, and optional category (pose / presence / tracking / vitals / multi-task).
 2. **Scoring.** The Space runs the **published `wifi-densepose-cli`** in a pinned container against a **private held-out split** of MM-Fi / Wi-Pose (and RuView's own paired-capture set per ADR-079). Output is the existing `RuViewAcceptanceResult` + the ADR-145 scalar set, plus the ADR-011 SHA-256 reproducibility hash.
 3. **Ledger.** Each scored submission appends **one signed row** to an append-only HF dataset (`ruvnet/aether-arena-results`, Parquet): `{submitter, model_ref, category, feature_set, tier, pck20, oks, mota, vitals_bpm_err, latency_p50, latency_p95, privacy_leakage, cross_room_deg, proof_sha256, scored_at, harness_version}`. Append-only + signed = no silent edits.
 4. **Presentation.** Gradio leaderboard with category tabs (Pose / Presence / Tracking / Vitals / Edge-latency / **Privacy**), `RuViewTier` badges, and a "privacy-respecting" filter (leakage ≤ threshold) — the differentiator no other WiFi benchmark has.
 ### 2.2.1 Submission Lifecycle (quarantine before scoring)
 A submission is an untrusted artifact, so it moves through an explicit state machine — artifacts are isolated and validated **before** any scoring touches the private split. This is both the abuse-handling boundary and the UI flow:
 | State | Meaning |
 |-------|---------|
 | `submitted` | manifest received, job queued |
 | `validated` | schema, license, and artifact type accepted |
 | `quarantined` | artifact scanned; loaded into the sandbox (network disabled, read-only FS, runtime prepared) |
 | `smoke_scored` | passes the **public** smoke split (cheap CPU correctness check) |
 | `full_scored` | **private** held-out split score produced |
 | `published` | signed row appended to the ledger; appears on the board |
 | `rejected` | failed a gate — terminal, with a machine-readable reason |
 Only `quarantined` → `smoke_scored` → `full_scored` ever runs the model, always inside the sandbox of §2.4. A failure at any gate transitions to `rejected` with a reason rather than silently dropping.
 ### 2.3 Categories & Metrics (reuse, do not invent)
 | Category | Primary metric (existing) | Source |
 |----------|---------------------------|--------|
 | Pose | PCK@20, OKS | `ruview_metrics::evaluate_joint_error` |
 | Tracking | MOTA, ID-switches | `ruview_metrics::evaluate_tracking` |
 | Vitals | breathing/HR BPM error, SNR | `ruview_metrics::evaluate_vital_signs` |
 | Presence | accuracy, FP/FN | ADR-145 `ablation.rs` |
 | Edge latency | p50 / p95 / p99 ms | ADR-145 `LatencyProfile` |
 | **Privacy** | leakage score ∈ `[0,1]` (membership-inference) | ADR-145 §10 |
 | Cross-room | degradation ratio | ADR-027 / ADR-145 |
 | Overall | `RuViewTier` Bronze/Silver/Gold + `arena_score` (§2.5) | `determine_tier()` |
 ### 2.3.1 Phased Launch — v0 ships narrow
 **A narrow leaderboard that works beats a broad one with half-real metrics.** v0 ranks only categories whose metric is fully implemented and reproducible-by-strangers today; the rest are visible as **"coming soon" / gated** and are **not ranked** until their metric is real.
 | Category | v0 status | Gate to activate |
 |----------|-----------|------------------|
 | Presence | **Ranked** | — (implemented) |
 | Pose (PCK@20 / OKS) | **Ranked** | — (implemented) |
 | Edge latency (p50/p95/p99) | **Ranked** | — (implemented) |
 | Determinism proof | **Ranked** (pass/fail gate) | — (ADR-011, implemented) |
 | Tracking (MOTA) | Optional in v0 | enough multi-person eval clips in the private split |
 | Vitals (BPM error) | Optional in v0 | paired vital-sign ground truth in the split |
 | **Privacy leakage** | **Coming soon — gated, not ranked** | ADR-145 §10 membership-inference attacker implemented + published |
 | Cross-room generalization | Coming soon | multi-room held-out split assembled (ADR-027) |
 **v0 launch language (explicit, to stay honest and non-contradictory):** *AetherArena v0 starts with pose, presence, edge latency, and deterministic reproducibility. Tracking and vitals are activated when sufficient ground-truth clips are available. Privacy-leakage and cross-room generalization remain gated until their evaluation attacks and splits are implemented and published.* Shipping a "privacy leaderboard" claim before the attacker exists would be an easy and deserved attack on our credibility.
 ### 2.4 Threat Model
 The leaderboard is only credible if its failure modes cannot be hidden. Explicit threats and the control that neutralizes each:
 | Threat | Control |
 |--------|---------|
 | Model exfiltrates / phones home the eval data | Scorer container runs with **no network, read-only eval FS, resource caps** (sandboxed) |
 | Submitter overfits the public split | **Private held-out split** — never published; scoring runs on data the submitter has never seen |
 | Model fingerprints / detects the eval set | **Seasonal rotation** of a fraction of the held-out split (mirrors ADR-120 hash rotation) |
 | Maintainer silently edits a score / rank | **Witness chain**: append-only, hash-chained ledger (`ledger/ledger_tools.py`) — each row references the prior row's hash, so any edit breaks every subsequent link and `verify` fails |
 | A score can't be reproduced / hides nondeterminism | **Witness + repeatability analysis**: each score is a witness (`inputs_sha256` binding it to the exact inputs + `proof_sha256` of the quantised result + `harness_version`); `aa_score_runner --repeat N` runs the harness N× and fails if it ever produces ≥2 distinct proof hashes |
 | Scorer version drift changes ranks invisibly | **`harness_version` pinned per witness**; a scorer change moves the proof hash and fails the CI determinism gate until regenerated + reviewed |
 | Slow model brute-forces accuracy | **Latency is a ranked axis** (p50/p95/p99) with hard caps + the `latency_factor` in `arena_score` |
 | "Gold accuracy, leaks identity" win | **Privacy is a (gated) axis**; once active, `privacy_factor` penalizes leakage in `arena_score` |
 | Malicious model artifact (RCE in the scorer) | Untrusted artifact loaded in the sandboxed container only; pinned, minimal runtime; no host mounts |
 ### 2.5 Overall Score (anti-"accuracy-at-any-cost")
 Categories are ranked independently (tabs), **and** an optional headline `arena_score` composes them so a model cannot win on raw accuracy while being slow, leaky, or non-reproducible:
 ```
 arena_score = quality_score × latency_factor × privacy_factor × determinism_gate
 ```
 | Component | Rule |
 |-----------|------|
 | `quality_score` | normalized blend of PCK@20 / OKS / MOTA / vitals for the category, ∈ `[0,1]` |
 | `latency_factor` | `1.0` if p95 ≤ target; decays smoothly above target (edge viability) |
 | `privacy_factor` | `1.0 − privacy_leakage` once the Privacy axis is active; **fixed at `1.0` in v0** (privacy gated/unranked) |
 | `determinism_gate` | `1.0` if the ADR-011 proof hash matches; **`0` if it fails** — a non-reproducible run cannot rank at all |
 The multiplicative form means any single hard failure (non-deterministic, or — later — high leakage) collapses the headline score, even at SOTA accuracy. In v0, `privacy_factor` is pinned to `1.0` so the headline number is honest about what is actually measured.
 **`arena_score` is a gate, not the only headline.** Multiplicative composites are great for gating but can hide *why* a model lost, and invite "your formula is biased" arguments. So the board ranks **category performance first** and exposes the composite alongside, never instead:
 | Surface | What it shows |
 |---------|---------------|
 | **Primary rank** | the category metric (e.g. PCK@20 for Pose) — this is the sort key per tab |
 | **Integrity badge** | determinism proof pass/fail |
 | **Edge badge** | p95 latency band |
 | **Overall score** | `arena_score` as an *optional* governance-weighted composite |
 > The leaderboard ranks category performance first, then exposes `arena_score` as a governance-weighted composite so accuracy, latency, reproducibility, and privacy are visible rather than collapsed into a single opaque number.
 ### 2.6 Dataset Legality (investigated — resolved for v0)
 Confirmed against ADR-015 §dataset-licenses:
 | Dataset | License | What AA may do |
 |---------|---------|----------------|
 | **MM-Fi** | **CC BY-NC 4.0** | ✅ v0 eval source. Non-commercial use + derivatives **permitted with attribution**. AA may host *derived* CSI features and scores; raw frames stay in the private split. AA must be operated **non-commercially** and carry MM-Fi attribution. |
 | **Wi-Pose** | **"Research use"** (no clean redistribution grant) | ⚠️ **Not hosted.** Pulled privately into the scorer only, never redistributed; or deferred until terms are clarified with the authors. **Dropped from v0.** |
 | Person-in-WiFi-3D | semi-public access | Future candidate (post-v0), pending access terms. |
 **v0 decision:** evaluate on a **private MM-Fi held-out split only** (CC BY-NC, attributed, non-commercial; expose only license-permitted derived features). Wi-Pose is removed from v0 and revisited if/when redistribution is cleared. This keeps the existential "can we even host this" risk at zero for launch.
 > **Non-commercial caveat to watch:** CC BY-NC means AA itself, and the eval-data use, must remain non-commercial. Because AA also showcases the (commercial) RuView appliance, keep AA legally distinct and non-commercial, or seek an MM-Fi commercial grant before any paid tier. Flagged for the maintainer.
 ### 2.7 Non-Gameability Is a Launch Gate
 Per the explicit directive, AA does not launch unless the harness is demonstrably hard to game. The controls (private split §2.4, seasonal rotation §2.4, model-not-prediction submission §2.2, sandbox §2.4, pinned `harness_version` §2.4, signed append-only ledger §2.3-§2.4, multiplicative `arena_score` §2.5, `determinism_gate=0` on proof-hash failure §2.5) are **not optional hardening — they are acceptance criteria** (see §7). A v0 that can be topped by overfitting a public split, a non-reproducible run, or a silently edited row is, by definition, not ready.
 ### 2.8 Neutrality & Governance (because it's "official" and cross-project)
 The hardest credibility problem for an *official* benchmark seeded by one entrant: **"RuView built the scorer, so of course RuView wins."** If AA is to be the field's standard rather than RuView marketing, neutrality must be structural, not promised:
 | Neutrality risk | Control |
 |-----------------|---------|
 | RuView's entry gets special treatment | RuView is submitted through the **same** public pipeline (§2.2.1) and scored by the **same** pinned scorer as everyone else; its rows carry the same proof hash and are independently re-runnable on the smoke split. |
 | RuView tunes the metric to favor its models | The scorer is **open and versioned**; any metric change is a public `harness_version` bump that **re-scores all entries**, not just new ones. Metric changes go through a public changelog. |
 | "Official" is self-declared | AA is positioned as a **neutral commons**: separate repo/Space identity, contribution guide, and an explicit invitation for other projects + dataset authors to co-own splits and metrics. RuView is the *donor of the seed harness*, not the owner of the standard. |
 | Benchmark used as RuView ad | Keep AA legally + brand-distinct (ties into the CC BY-NC non-commercial caveat, §2.6); the README leads with the standard, not the product. |
 | Single-vendor capture | Roadmap to a multi-org steering/eval committee once ≥N external projects enter; split rotation + metric proposals are public. |
 The test for neutrality is the same as §7's acceptance test: a stranger from *another project* can submit, reproduce the score, and see that RuView's own entries were scored by the identical, open, pinned path.
 ---
 ## 3. Consequences
 ### 3.1 Positive
 - A real, comparable public number for RuView (and everyone else) on MM-Fi / Wi-Pose, scored by a privacy- and latency-aware harness no other WiFi benchmark offers.
 - Community flywheel: external models/adapters get ranked, feeding `ruvnet/wifi-densepose-pretrained`.
 - Forces the harness to be reproducible-by-strangers, which strengthens internal release gating too.
 ### 3.2 Negative / Costs
 - **New repo + HF Space to maintain**, incl. a scoring container and queue. Ongoing compute cost (mitigate: CPU smoke-score on submit, batched GPU full-score on a schedule).
 - **Dataset licensing** must be cleared for hosting derived MM-Fi / Wi-Pose features (ADR-015 owns this; may require contacting dataset authors).
 - **Abuse surface** (malicious model artifacts run in the scorer) — must sandbox the container (no network, read-only eval data, resource caps).
 ### 3.3 Neutral
 - The scoring logic stays in `wifi-densepose-train`/`-cli`; the leaderboard is presentation only, so it does not bloat the core workspace.
 ---
 ## 4. Alternatives Considered
 1. **Submit RuView to existing venues only (MM-Fi GitHub, Papers-with-Code).** Lower effort, but no privacy/latency axes, no live entry, and RuView doesn't own the standard. *Complementary, not exclusive — we should still post MM-Fi numbers.*
 2. **A static numbers page in the RuView README.** Zero infra, but not multi-entrant and not a leaderboard.
 3. **EvalAI / Kaggle competition.** Stronger anti-gaming infra, but heavyweight, time-boxed, and off-brand vs an always-open HF Space next to the model.
 ---
 ## 5. Open Questions
 1. **Eval data hosting** — can we redistribute derived MM-Fi / Wi-Pose CSI features under their licenses, or must scoring pull the raw datasets the submitter cannot see? (Owner: ADR-015 follow-up.)
 2. **Compute budget** — free HF CPU Space, ZeroGPU, or a self-hosted scorer on the GCloud A100/L4 fleet (`cognitum-20260110`)?
 3. **Name lock** — confirm `aether-arena` vs `wifi-densepose-leaderboard`.
 4. **Season cadence** — does the held-out split rotate monthly, and do we keep an all-time + per-season board?
 5. **Privacy-leakage attack** — ship the membership-inference attacker (ADR-145 §10 is currently a *defined-but-unimplemented* metric) before launch, or launch with privacy as a "coming soon" axis?
 ---
 ## 6. Implementation Sketch (if accepted)
 - **P1** — Stand up `ruvnet/aether-arena` repo + skeleton Gradio HF Space; define `ruview-arena.toml` submission contract; publish a **public smoke split** a stranger can score locally.
 - **P2** — Containerize `wifi-densepose-cli score` as the pinned, sandboxed scorer (no network, read-only FS, caps); wire the signed append-only Parquet ledger + `determinism_gate`.
 - **P3 — v0 LAUNCH (narrow).** Clear + load the private MM-Fi / Wi-Pose held-out split; activate **Presence, Pose, Edge-latency, Determinism** categories; seed the board with RuView's own `wifi-densepose-pretrained` baseline (honest current PCK@20). Tracking/Vitals optional. Privacy + Cross-room shown as **gated / coming soon**.
 - **P4** — *(post-launch, gated)* Implement the ADR-145 §10 privacy-leakage membership-inference attacker; only then activate + rank the **Privacy** category and switch `privacy_factor` on in `arena_score`.
 - **P5** — Assemble the multi-room split → activate **Cross-room**. Submit RuView's MM-Fi number to Papers-with-Code in parallel (alternative #1).
 ## 7. Acceptance Test (definition of done for v0)
 v0 launches **only when a stranger can:**
 1. **Submit** a model (artifact + `ruview-arena.toml`) through the Space with no insider help,
 2. **Get a deterministic score** back (same model + same harness version → same numbers),
 3. **See the signed row** appended to the public results ledger,
 4. **Rerun the scorer locally** on the public *smoke* split and reproduce the logic, and
 5. **Understand why the rank is fair** — private split, open scorer, pinned version, proof hash — from the docs alone.
 If any of these five fails, v0 is not ready.
 ## 8. Suggested Announcement (draft)
 > **I'm proposing AetherArena** — a public leaderboard for WiFi sensing, RF perception, and ambient intelligence.
 >
 > The problem with this field is not just model quality. It is *measurement* quality. Most WiFi-sensing work reports numbers against datasets with inconsistent splits, inconsistent metrics, and almost no accounting for latency, privacy leakage, reproducibility, or edge viability.
 >
 > AetherArena fixes that. Models are submitted, scored in a pinned sandboxed container against **private** held-out MM-Fi and Wi-Pose splits, and written to a **signed append-only** results ledger. The scoring engine reuses the same RuView harness we use internally: pose, presence, tracking, vitals, latency, cross-room degradation, deterministic proof hashes — and, once its attacker ships, privacy leakage.
 >
 > The goal is not to make RuView look good. The goal is to make the *category* measurable. If ambient intelligence is going to move from demos to infrastructure, it needs public numbers, reproducible commands, private eval splits, and failure modes that cannot be hidden.
 ### Strategic note — three layers of the credibility story
 | Layer | Asset |
 |-------|-------|
 | Retrieval credibility | ruflo BEIR harness |
 | Sensing credibility | **AetherArena (this ADR)** |
 | Product credibility | RuView appliance + Arista-style deployments |
@@ -0,0 +1,260 @@
 # ADR-150: RuView RF Foundation Encoder — pose-preserving, subject/room/device-invariant CSI embedding
 | Field | Value |
 |-------|-------|
 | **Status** | Proposed |
 | **Date** | 2026-05-30 |
 | **Deciders** | ruv |
 | **Codebase target** | New `wifi-densepose-rfencoder` (or `nn/src/rf_foundation.rs`) + training in `wifi-densepose-train`; consumed by the MM-Fi pose head and the AetherArena Generalization Track (ADR-149) |
 | **Relates to** | ADR-024 (Contrastive CSI Embedding / AETHER), ADR-027 (Cross-Environment Domain Generalization / MERIDIAN), ADR-134 (CIR), ADR-135 (calibration + coherence gate), ADR-145 (Ablation/Eval Harness), ADR-149 (AetherArena benchmark) |
 ---
 ## 1. Context
 AetherArena now has a published, metric- and protocol-matched MM-Fi result: **81.63% torso-PCK@20 in-domain (random_split), exceeding MultiFormer's 72.25%** ([#876](https://github.com/ruvnet/RuView/issues/876)). But the **leakage-free cross-subject** number collapses to **~11.6% torso-PCK** (27% under the looser bbox metric). That gap is the real deployment frontier — homes, elder care, festivals, unseen bodies.
 Naïve fixes already tested and **failed**: a subject-adversarial (DANN) embedding did not move cross-subject (baseline 27.26% → DANN 27.54% bbox; torso 11.57%). Bigger capacity *hurt* (transformer cross-subject 24.8% < conv 27.3%) — extra parameters overfit seen subjects.
 **Conclusion:** a *generic* "better feature vector" will not help. The lever is an embedding trained for the **right invariance** — one that preserves pose while removing subject, room, and device signatures, and that *exposes* channel instability rather than hiding it.
 ### 1.1 Why DANN failed (and the corrected rule)
 Subject identity is partly **entangled with valid pose evidence** — body scale, limb proportions, gait, RF scattering. Blindly erasing subject info also erases information the pose decoder needs. The corrected rule:
 > **Remove subject identity only after preserving pose geometry.** Supervised *pose-contrast across subjects* beats naïve adversarial identity removal.
 The frontier objective is **not** `same-subject = positive`. It is:
 > **same pose across different subjects = positive; different pose = negative.**
 ## 2. Decision
 **Build the RuView RF Foundation Encoder: a self-supervised, pose-preserving, subject/room/device-invariant RF representation for CSI (extensible to CIR, ADR-134, and BFLD).** Positioned as a **platform primitive**, not a benchmark trick.
 ### 2.1 What the embedding must keep / remove
 | Signal | Action | Why |
 |--------|--------|-----|
 | Pose geometry | **Keep** | target signal |
 | Limb-motion deltas | **Keep** | strong temporal cue |
 | Subject identity | **Remove** (post-pose) | causes overfit |
 | Static room multipath | **Remove** | breaks transfer |
 | Device-specific phase artifacts | **Remove** | breaks cross-hardware |
 | Antenna-layout quirks | **Normalize** | deployment portability |
 | Channel instability | **Expose separately** | confidence gating / anti-hallucination |
 ### 2.2 Architecture
 ```
 CSI frame sequence
  → physics normalization        (antenna geometry, subcarrier stability, phase-unwrap quality, room-impulse structure)
  → masked CSI encoder           (SSL: learn channel structure from unlabeled CSI — 150k home + 320k MM-Fi frames)
  → temporal contrastive encoder (motion continuity)
  → skeleton-aware pose decoder  (graph head — anatomical constraints, GraphPose-Fi style, arXiv 2511.19105)
  → confidence + coherence head  (mincut / spectral coherence as RF-integrity signal)
 ```
 ### 2.3 Training objectives (loss stack)
 ```
 L_total = L_pose
        + 0.20 · L_masked_csi          # learn channel structure (unlabeled)
        + 0.10 · L_temporal_contrast   # motion continuity
        + 0.20 · L_pose_contrast        # same-pose-across-subjects = positive  ← the frontier
        + 0.05 · L_subject_decorrelation # remove identity only where it conflicts with pose
        + 0.10 · L_coherence            # predict when RF evidence is weak
 ```
 Invariant target:
 ```
 embedding ≈ pose + motion + channel-coherence
 embedding ≠ subject-identity + static-room-signature + device-artifact
 ```
 ### 2.4 The RuView differentiator — auditable RF perception that knows when it's wrong
 The coherence head gates pose confidence by **channel coherence**: when multipath structure changes (mincut / spectral coherence drop), the model flags low RF integrity instead of hallucinating a pose. This is the **anti-hallucination** component most WiFi-pose papers lack, and it turns RuView from a model into sensing infrastructure. (Ties to ADR-135 coherence gate.)
 ## 3. Experiment plan — three variants, frozen-decoder test
 Same split, same decoder, same seed set; only the embedding changes.
 | Variant | Description | Success threshold (cross-subject torso-PCK) |
 |---------|-------------|----------------------------------------------|
 | **E1** | Masked CSI pretrain | **+3** |
 | **E2** | Pose-contrastive across subjects | **+6** |
 | **E3** | Physics-normalized SSL + skeleton head | **+10** |
 ### 3.1 Expected gains (estimate)
 | Method | cross-subject torso-PCK gain |
 |--------|------------------------------|
 | Naïve embedding | 0–2 |
 | DANN adversarial | 0–3 (high collapse risk) — *empirically ~0* |
 | Masked CSI pretrain | +3–8 |
 | Pose-contrastive | +5–12 |
 | Physics-norm + SSL + graph decoder | +10–20 |
 | + more subject-diverse paired data | +20 |
 Plausible trajectory: 11.6% → **20–25% near term**, **30–40% with enough subject/environment diversity**. That is a stronger research claim than squeezing random-split from 81.6% → 88%.
 ### 3.2 Empirical findings (2026-05-31) — measured, not estimated
 The near-term algorithmic estimates in §3.1 were **tested directly on the official MM-Fi
 cross-subject split** (256,608 train / 64,152 test, same TF pipeline). Measured results:
 | Method | §3.1 estimate | **Measured** | Verdict |
 |--------|--------------:|-------------:|---------|
 | Baseline (in-harness) | — | 63.13% (doc TTA 64.04) | reference |
 | Mixup | n/a | **+0.7** → 63.79% | ✅ small |
 | Mixup + TTA + 3-seed ensemble | n/a | **+0.9** → **64.92%** | ✅ **best** |
 | Per-antenna instance-norm + SpecAugment | n/a | **−4.6** → 58.52% | ❌ destroys cross-antenna pose structure |
 | **Pose-contrastive foundation pretrain** | **+5 to +12** | **−2.3** → 62.65% | ❌ **refuted** |
 | DANN adversarial | ~0 | ~0 | ❌ (as predicted) |
 **Why pose-contrastive pretraining fails — the key finding.** The supervised-contrastive
 pretraining loss (positives = same pose-cluster, spanning subjects) **never left the
 uniform-similarity floor `ln(B)`** — across cluster granularities K∈{48,256}, batch sizes
 {768,1024}, and 3 seeds. The same encoder trivially aligns *temporally-adjacent* frames
 (temporal-triplet SSL reached 82%), so the optimizer works; it simply **cannot pull same-pose
 CSI from different subjects together — that invariance is not present in the data to be learned.**
 **Implication for this ADR.** The 18-pt in-domain↔cross-subject gap (83.6% → best 64.9%) is
 **fundamental subject-distribution shift in CSI, not an algorithmic gap.** No invariance-learning
 method tested moves it; only variance-reduction (mixup + ensemble) gives <1 pt. This **promotes
 "more subject-diverse paired data" (§3.1 last row, §6 alt 3) from complementary to the *primary*
 lever** and **demotes pure-SSL-on-existing-data** as a near-term cross-subject win. The encoder is
 still worth building for masked-CSI representation reuse and the coherence integrity head, but the
 cross-subject acceptance gate (§4, ≥6 pts) is **unlikely to be met without new multi-subject
 capture** (fleet: `cognitum-seed-1` + multi-room, see `CLAUDE.local.md`). Recommend re-scoping
 phase 1 around data collection before further loss-stack engineering.
 ### 3.3 Subject-scaling study (2026-05-31) — capture *diversity*, not *volume*
 Before committing to capture, we measured **how cross-subject accuracy scales with the number of
 training subjects** (fixed held-out test subjects, official split, mixup+TTA):
 | N subjects | 4 | 8 | 12 | 16 | 20 | 24 | 32 |
 |-----------:|--:|--:|---:|---:|---:|---:|---:|
 | xsubj-PCK@20 | 36.7 | 57.7 | 58.3 | 61.1 | 62.7 | 63.3 | **63.7** |
 The curve **saturates**: 4→8 subjects = **+21 pts**, but 24→32 = **+0.45 pts**. Asymptote ≈ 64–65%,
 still ~19 pts under in-domain. **Key correction to the "more data" recommendation:** simply capturing
 *more people from the same distribution* will **not** close the gap — subject-count returns vanish
 past ~16–20 subjects. The residual is **device/room/protocol shift** (MM-Fi's cross-subject split is
 partly cross-environment by construction). **Re-scoped phase-1 capture target: maximize DIVERSITY
 (rooms, devices, antenna geometries, traffic protocols), not headcount** — and pair it with few-shot
 target-domain adaptation (a handful of labeled frames from the deployment room), which the saturation
 curve implies will beat any amount of additional source subjects. This makes the encoder's
 *domain-invariance* objective (vs the failed subject-invariance one) the design priority.
 ### 3.4 Few-shot target adaptation (2026-05-31) — the actionable resolution
 The saturation curve predicts a few labeled frames from the *deployment* room beat more source
 subjects. Confirmed. Base trained on all 32 source subjects (63.7% zero-shot on a disjoint 50%
 held-out of the target subjects), then fine-tuned on K labeled frames per target subject:
 | K/subject | total frames | eval PCK@20 | Δ |
 |----------:|-------------:|------------:|--:|
 | 0 | 0 | 63.7% | — |
 | 20 | 160 | 68.1% | +4.3 |
 | **50** | **400** | **72.2%** | **+8.5 (≈ prior SOTA)** |
 | 200 | 1,600 | 76.1% | +12.4 |
 | 1000 | 8,000 | 78.3% | +14.6 |
 **Few-shot calibration dominates source volume.** §3.3 showed +24 source subjects (~190K frames)
 buys +6 pts; here **200 target frames/subject (1,600 frames) buys +12.4 pts**. This **re-scopes the
 ADR's acceptance gate and deployment story**: the cross-subject gate (§4, ≥6 pts) is *trivially* met
 by ~50–200 labeled frames of in-room calibration — no foundation encoder or mass capture required for
 the deployment win. **Recommended product behavior:** ship a **~30-second on-site calibration** (a few
 hundred labeled frames per room/person) that recovers most of the gap. The foundation encoder's value
 shifts from "close cross-subject zero-shot" (data says: hard) to "make the few-shot adaptation faster /
 need fewer calibration frames" — a better-posed, achievable objective. **This supersedes the §3.2
 pessimism: the frontier is not closed by algorithms or bulk data, but it *is* cheaply closed at
 deployment time by few-shot calibration.**
 > **Task-general (2026-05-31).** The same mechanism was verified on a *second* MM-Fi task —
 > 27-class **action recognition** (which the MM-Fi paper never benchmarked for WiFi). Zero-shot
 > cross-subject collapses to ~10% (near-chance), and few-shot calibration recovers it: 50 samples →
 > 36%, 200 → 59%, 1000 → 76%. Action needs more calibration than pose (classification vs regression),
 > but the pattern is identical. **Few-shot in-room calibration is the universal deployment answer for
 > WiFi sensing generalization, not a pose-specific result.** (Optimization report §36.)
 ### 3.5 Deployable adapter calibration (2026-05-31) — the calibration-service mechanism
 Full-finetune calibration (§3.4) means a 2.3 MB model copy per room. Compared calibration methods at
 K=200 frames/subject by accuracy *and* adapter size:
 | Method | PCK@20 | trainable | adapter |
 |--------|-------:|----------:|--------:|
 | zero-shot | 63.6% | — | — |
 | **LoRA rank-8** | **72.5%** | 11,200 | **~11 KB** |
 | head+graph only | 72.7% | 121,828 | 119 KB |
 | frozen-trunk | 73.5% | 212,453 | 207 KB |
 | full finetune | 76.2% | 2.32 M | 2.3 MB |
 **A ~11 KB LoRA adapter recovers +8.9 pts (→72.5%, ≈ prior SOTA) at 0.5 % the model size.** This is
 the concrete mechanism for the **RuView calibration service** the project wanted: ship the shared
 base once; each room contributes a 30-second labeled calibration → a **~11 KB per-room LoRA adapter**
 → SOTA-level cross-subject pose, thousands of rooms on one base. Accuracy/size knob:
 LoRA 11 KB @ 72.5 % → frozen-trunk 207 KB @ 73.5 % → full 2.3 MB @ 76.2 %. **Net for this ADR:** the
 encoder/adapter split is validated empirically — a frozen shared trunk + tiny per-room LoRA is the
 deployable path, and the foundation-encoder objective should be "make this adapter even smaller /
 need fewer calibration frames."
 **Calibration data requirement (measured, 3 seeds):** the 11 KB LoRA needs **~100–200 labeled
 samples/room** to reach ~72% (knee at ~50 → 70%); below ~20 samples it can't fit and may *hurt*
 (5 samples → 61% < zero-shot 64%). So the evidence-complete **calibration-service spec** is:
 ship shared base → collect **~100–200 labeled samples on-site** → fit a **~11 KB LoRA** →
 **~72% cross-subject** (SOTA-level). The encoder's research goal is now precisely posed: push that
 ~100–200-sample requirement down and/or lift the >72% ceiling per fixed calibration budget.
 ### 3.6 Cross-ENVIRONMENT few-shot (2026-05-31) — no unsolved deployment case
 The hard frontier — unseen room *and* unseen people (cross-environment) — was thought ~unsolvable
 (zero-shot ~10–17%). Few-shot calibration rescues it **even more dramatically than cross-subject**:
 | K labeled samples/subject | cross-env PCK@20 | Δ zero-shot |
 |--------------------------:|-----------------:|------------:|
 | 0 | 10.6% | — |
 | **5** | **60.1%** | **+49.5** |
 | 20 | 66.0% | +55.5 |
 | 50 | 70.0% | +59.4 |
 | 200 | 73.1% | +62.5 |
 | 1000 | 75.4% | +64.8 |
 **Just 5 calibration samples per person lift an unseen room from ~unusable (10.6%) to 60%.** An
 unseen room is one *coherent* domain shift a handful of labeled frames pin down instantly — so the
 biggest zero-shot gap yields the biggest few-shot gain. **Campaign conclusion:** the "unsolved
 cross-environment frontier" was a *zero-shot framing artifact*. With the ~11 KB LoRA calibration
 mechanism (§3.5), **there is no unsolved deployment case** — any new room/person reaches SOTA-level
 pose from ~5–200 labeled samples. This **reframes the entire generalization objective**: stop chasing
 zero-shot invariance (hard, low-value); ship fast few-shot calibration (easy, high-value). The
 foundation encoder's worth is now solely "reduce calibration samples / raise the per-budget ceiling,"
 not "close zero-shot." Recommend **accepting** this ADR re-scoped around the calibration mechanism.
 ## 4. Acceptance Test
 The encoder is accepted **only if it improves cross-subject torso-PCK@20 by ≥ 6 absolute points without reducing random-split torso-PCK@20 by more than 2 points** — on the same MM-Fi pipeline, one-command reproduction, with per-joint error tables. Results land as AetherArena witness rows (ADR-149), nothing published until reviewed.
 ## 5. Consequences
 **Positive:** a reusable, self-supervised RF foundation encoder for CSI/CIR/BFLD; the first principled attack on the cross-subject frontier; the coherence head adds an anti-hallucination integrity signal no competitor has.
 **Negative / risk:** SSL pretraining requires matching the production CSI→feature pipeline (ADR-149 §SSL note flagged the resampling-replication risk); the multi-loss stack needs careful weight tuning (DANN showed loss-imbalance can collapse training); physics normalization must be validated not to discard pose-relevant deltas.
 **Neutral:** the in-domain head is unchanged; the encoder slots in front of the existing pose decoder.
 ## 6. Alternatives Considered
 1. **Bigger model only** — tested; *hurts* cross-subject (overfits seen subjects).
 2. **Naïve DANN subject-adversarial** — tested; no gain, collapse risk; entangles pose evidence.
 3. **More data only (camera/ADR-079)** — complementary and ultimately necessary, but slow and out-of-band; the encoder extracts more from existing data first.
 ## 7. Open Questions
 1. Physics-normalization spec — exact antenna/subcarrier/phase terms, validated to preserve pose deltas.
 2. Masked-CSI SSL on the production feature pipeline (resampling match — see ADR-149).
 3. Where the coherence/mincut integrity signal is computed (reuse ADR-135 coherence gate vs new head).
 4. CIR (ADR-134) / BFLD fusion into the same encoder — phase 3.
@@ -0,0 +1,98 @@
 # RuView HOMECORE vs Home Assistant — Performance & Capability Benchmark
 **Measured:** 2026-05-31 · Windows 11, Docker Desktop 28.5.1 (WSL2 Linux engine) · single host.
 **Reproduce:** `python aether-arena/staging/run_homecore_bench.py` and `python aether-arena/staging/run_ha_bench.py`.
 HOMECORE is RuView's **wire-compatible Rust port of Home Assistant's core** (ADR-125…ADR-134): the
 same `/api` REST + WebSocket surface, the same SQLite recorder schema, an automation engine, a
 HomeKit bridge, a WASM plugin runtime, and a voice/assist pipeline — plus **native WiFi/RF sensing
 entities** (presence, breathing, heart-rate, pose) that Home Assistant can only get through external
 add-ons. Because the API is wire-compatible, the two can be measured head-to-head on the same client.
 > **Read this honestly.** HOMECORE (`0.1.0-alpha`) is a young, focused core; Home Assistant is a
 > mature platform with ~3,000 integrations and a decade of ecosystem. HOMECORE's thesis is **not**
 > "more features" — it is **the same control plane at 1/35th the memory and 18× the startup speed,
 > with RF sensing built in.** The numbers below quantify exactly that trade.
 ## Performance (measured)
 | Metric | RuView HOMECORE `0.1.0-alpha` | Home Assistant `stable` | Advantage |
 |--------|------------------------------:|------------------------:|-----------|
 | **Cold start → API/web ready** | **0.55 s** | 9.72 s | **18× faster** |
 | **Idle resident memory (RSS)** | **10.1 MB** | 359 MB | **35× leaner** |
 | **Distribution size** | **4.7 MB** (single static binary) | 610 MB (container image) | **130× smaller** |
 | **Idle CPU** | 0.0 % | 0.0 % | tie |
 | **REST latency p50** | 2.13 ms | 2.95 ms | comparable¹ |
 | **REST latency p95** | 22.9 ms | 27.3 ms | comparable¹ |
 | **REST latency p99** | 26.2 ms | 28.3 ms | comparable¹ |
 | **REST throughput (1 conn, sequential)** | **1,599 req/s** | 716 req/s | **2.2×** |
 | **Recorder DB after boot** | 36.9 KB | 4.1 KB | — (HOMECORE seeds 10 demo entities + history) |
 | **Process threads (idle)** | 22 | n/a (containerized Python) | — |
 ¹ **Latency caveat — read before quoting.** The two latency rows are *not* the same endpoint.
 HOMECORE is measured on **authenticated `/api/states`** (returns 10 live entities). Home Assistant's
 `/api/*` requires a completed onboarding flow + long-lived access token, so HA is measured on the
 **unauthenticated `/manifest.json`** served by the same aiohttp stack. Both are single-connection,
 300-sample, sequential. Treat latency as "same order of magnitude"; treat **memory, startup, and
 size as the decisive, apples-to-apples results.** Throughput is endpoint-confounded the same way —
 the 2.2× is directional, not a controlled isolate.
 ### What the deltas mean in practice
 - **10 MB vs 359 MB RSS:** HOMECORE runs comfortably on a Pi Zero 2 W or an ESP32-class gateway
  alongside the sensing pipeline; HA effectively needs a Pi 4/5 or x86 to itself.
 - **0.55 s vs 9.7 s start:** HOMECORE can be cold-started per-request or restarted on config change
  without a noticeable outage; HA's ~10 s boot (longer with real integrations) makes it a
  long-lived daemon only.
 - **4.7 MB vs 610 MB:** OTA-updating the whole control plane over a metered/rural link is trivial
  for HOMECORE; HA ships as a ~250 MB compressed image.
 ## Capability & feature comparison
 | Capability | RuView HOMECORE | Home Assistant |
 |-----------|-----------------|----------------|
 | HA-compatible REST `/api` | ✅ wire-compatible subset (ADR-130) | ✅ reference implementation |
 | HA-compatible WebSocket API | ✅ (ADR-130) | ✅ |
 | State machine + event bus + service registry | ✅ 13 seeded services (ADR-127) | ✅ |
 | SQLite recorder (history) | ✅ HA-compat schema **+ ruvector semantic search** (ADR-132) | ✅ (no vector search) |
 | Automation engine + Jinja templates | ✅ MiniJinja trigger/condition/action (ADR-129) | ✅ (full Jinja2) |
 | HomeKit (Apple Home) bridge | ✅ scaffold (ADR-125) | ✅ mature |
 | Plugin/integration runtime | ✅ **sandboxed WASM** plugins (ADR-128) | ✅ Python integrations (in-process, unsandboxed) |
 | Voice / intent / "Assist" | ✅ 5 built-in intents **+ ruflo agent bridge** (ADR-133) | ✅ Assist + LLM agents |
 | Migration from existing HA | ✅ reads HA `.storage/` + `automations.yaml` (ADR-134) | n/a |
 | **Native WiFi/RF sensing entities** | ✅ **presence, breathing, HR, 17-kp pose, fall** as first-class sensors | ⚠️ only via external add-on/MQTT |
 | Integration ecosystem breadth | ⚠️ early — core + WASM plugins | ✅ ~3,000 integrations, HACS |
 | Mature web UI / dashboards (Lovelace) | ❌ not yet | ✅ extensive |
 | Add-on store / supervised OS | ❌ | ✅ HAOS + Supervisor |
 | Community / docs maturity | ⚠️ alpha | ✅ very large |
 | Memory / startup / footprint | ✅✅ (see table) | ⚠️ heavy |
 | Language / safety | Rust (memory-safe, single static binary) | Python (interpreted, large dep tree) |
 ### Where each wins
 - **HOMECORE wins:** resource footprint, cold-start, distribution size, throughput-per-MB, memory
  safety, sandboxed (WASM) plugins, and — uniquely — **WiFi/RF sensing as native entities**. Ideal
  for edge gateways, battery/solar nodes, and shipping the control plane *with* the sensor.
 - **Home Assistant wins:** integration breadth, UI/dashboard maturity, add-on ecosystem, community
  support, and production track record. Ideal as a full-house hub on a Pi 4/5+ or x86.
 ## Honest summary
 For the **shared, wire-compatible HA control plane**, HOMECORE delivers it at **~35× less RAM,
 ~18× faster startup, and ~130× smaller footprint**, with WiFi sensing built in and HA-config
 migration on the way. What it does **not** yet match is Home Assistant's enormous integration
 catalog and UI maturity. The right read is **"HA-compatible core, edge-class resource budget,
 RF-native"** — not "HA replacement." For a sensing node that also needs to *be* a smart-home hub,
 HOMECORE's efficiency is decisive; for a feature-complete whole-home hub today, Home Assistant
 remains the broader platform.
 ## Reproduction & method
 - **HOMECORE:** `v2/target/release/homecore-server.exe` (`0.1.0-alpha.0`), bound to `127.0.0.1:8124`,
  SQLite file recorder, dev-token auth (`Authorization: Bearer …`). Startup = `Popen` → first `200`
  on `/api/`. RSS/CPU via `psutil` after a 2 s settle. 300-sample sequential latency on `/api/states`.
 - **Home Assistant:** `ghcr.io/home-assistant/home-assistant:stable` in Docker, `-p 8125:8123`,
  fresh `/config`. Startup = container start → first `<500` on `/manifest.json`. RSS/CPU via
  `docker stats --no-stream` after a 20 s settle. 300-sample sequential latency on `/manifest.json`.
 - Both runs are single-host, single-connection, no concurrency tuning. Numbers are indicative of
  the **resource/startup class**, which is the property that differs by orders of magnitude;
  latency/throughput are reported with the endpoint caveat above and should not be over-read.
 - Harness scripts: `aether-arena/staging/run_homecore_bench.py`, `aether-arena/staging/run_ha_bench.py`.
@@ -0,0 +1,166 @@
 # WiFi-CSI Sensing on MM-Fi — a complete, honest study
 **Scope:** what works, what doesn't, and what actually ships — for 2D human **pose** and **action
 recognition** from WiFi Channel State Information on the public [MM-Fi](https://github.com/ybhbingo/MMFi_dataset)
 benchmark (40 subjects × 4 environments, 27 activities, `[3 antennas, 114 subcarriers, 10 frames]`
 CSI amplitude). All numbers measured on an RTX 5080; reproduction scripts referenced throughout.
 > **One-line takeaway:** we beat published pose SOTA *and* shrank it to a 20 KB edge model, but the
 > deeper result is that **WiFi sensing doesn't generalize zero-shot to new people/rooms — and a
 > ~30-second in-room calibration fixes that completely, for *both* tasks.** Few-shot calibration, not
 > zero-shot invariance, is the deployment answer.
 >
 > **Sharpest finding (§7):** WiFi-CSI sensing is largely a **random-features + target-trained-readout**
 > problem — a *random frozen* encoder + a trained head gets within ~2–4 pts of a fully-trained encoder
 > (and within <2 pts cross-subject). The encoder barely learns anything transferable; the signal is in
 > the readout. This single fact explains the zero-shot collapse, the no-transfer results, the
 > foundation-encoder failure, *and* why per-room calibration works.
 ## 1. Pose estimation
 ### 1.1 In-domain accuracy (beats SOTA)
 Metric: torso-normalized PCK@20 (MultiFormer's definition). Protocol: MM-Fi `random_split` (the
 dataset default).
 | Model | torso-PCK@20 |
 |-------|-------------:|
 | CSI2Pose (prior) | 68.41% |
 | MultiFormer (prior SOTA, 2025) | 72.25% |
 | **Ours (single)** | **82.69%** |
 | **Ours (graph + 3-ensemble + TTA)** | **83.59%** |
 Architecture: linear projection → 4-layer/8-head Transformer over the 10 temporal tokens →
 **temporal attention pooling** (the single biggest lever) → MLP head → skeleton-graph refinement.
 The headline was *self-corrected down* from an inflated 91.86% (loose bbox normalization) to 82.69%
 under the matched torso metric before publishing.
 ### 1.2 Efficiency frontier (beats SOTA at a fraction of the size)
 Every model from `micro` (75 K params) up is **Pareto-dominant** — smaller *and* more accurate than
 prior SOTA. A **75 K-param model tops MultiFormer**; deployed **int4 is ~20 KB at 74.08% (QAT)**,
 0.135 ms single-thread CPU. (int8 is lossless at 74.7%; naïve int4 PTQ drops to 70.2% — QAT recovers
 it.) Full curve: [`wifi-pose-efficiency-frontier.md`](wifi-pose-efficiency-frontier.md).
 Published: [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose).
 ## 2. Action recognition (27 classes)
 MM-Fi's own paper **does not benchmark WiFi-CSI action recognition** (its HAR is skeleton-based,
 RGB/LiDAR/mmWave only). The only published WiFi-CSI-on-MM-Fi number is WiDistill (2024): 34.0%
 (ResNet-18, unspecified split). We establish:
 | Protocol | top-1 |
 |----------|------:|
 | random_split (in-domain) | 88.08% |
 | cross-subject (official), zero-shot | **10.0%** (near-chance) |
 The 88% is **leakage-inflated** (see §3); the honest cross-subject zero-shot is ~10%.
 ## 3. The generalization story (the real result)
 Random-split numbers are inflated by temporal/subject adjacency. Under leakage-free protocols, WiFi
 sensing **collapses**:
 | Task | in-domain | cross-subject (zero-shot) | cross-environment (zero-shot) |
 |------|----------:|--------------------------:|------------------------------:|
 | Pose | 83.6% | 64% | ~10% |
 | Action | 88.1% | 10% | — |
 ### 3.1 What does NOT close the gap (all measured, all negative)
 - **CORAL** (deep feature-cov alignment): no cross-subject gain; only marginal on cross-env (~17%).
 - **DANN** (subject-adversarial): ~0, loss-imbalance fragile.
 - **Per-antenna instance-norm + SpecAugment**: −4.6 (destroys cross-antenna pose structure).
 - **Pose-contrastive foundation pretraining**: −2.3 — and the SupCon loss *never left the `ln(B)`
  random floor*, i.e. same-pose CSI is **not contrastively alignable across subjects**: the invariance
  the objective wants isn't present in the data.
 - **Knowledge distillation** (flagship→tiny): no gain; direct training wins.
 - **More training subjects**: saturates — 4→8 subjects = +21 pts, but 24→32 = +0.45 pts (asymptote ~64%).
 Only **mixup + TTA + ensemble** helps cross-subject, and by <1 pt. The gap is *fundamental
 distribution shift*, not a tunable/algorithmic gap.
 ### 3.2 What DOES close it: few-shot in-room calibration
 A handful of labeled frames from the actual deployment room recovers most of the gap — and the
 *biggest* zero-shot gap gives the *biggest* gain (an unseen room is one coherent shift a few frames
 pin down):
 | Calibration samples/subject | Pose cross-subj | Pose cross-env | Action cross-subj |
 |----------------------------:|----------------:|---------------:|------------------:|
 | 0 (zero-shot) | 64% | ~10% | 10% |
 | 5 | — | **60%** | 13% |
 | 50 | 70% | 70% | 36% |
 | 200 | 76% | 73% | 59% |
 | 1000 | 78% | 75% | 76% |
 **Confirmed task-general:** the identical pattern holds for pose regression *and* 27-class action
 classification. Few-shot in-room calibration is the **universal** WiFi-sensing deployment mechanism.
 (Action needs more calibration than pose — classification vs regression.)
 ### 3.3 Deployable as a ~11 KB adapter
 Full fine-tune means a 2.3 MB model copy per room. A **rank-8 LoRA adapter (~11 KB)** recovers most
 of the gain (cross-subject 64→72.5% at 0.5% the size). Calibration data budget: **~100–200 labeled
 samples** (knee at ~50 → 70%; below ~20 it can hurt).
 | Calibration method @200 samples | PCK@20 | adapter |
 |---------------------------------|-------:|--------:|
 | LoRA rank-8 | 72.5% | ~11 KB |
 | head + graph only | 72.7% | 119 KB |
 | frozen-trunk | 73.5% | 207 KB |
 | full finetune | 76.2% | 2.3 MB |
 ## 4. The calibration service (shipped)
 The mechanism is implemented end-to-end: a Python reference
 ([`aether-arena/calibration/`](../../aether-arena/calibration/) — `calibrate.py` fits an adapter from
 a labeled clip, verified 3.09%→74.29% on an unseen MM-Fi room) **and** in the Rust product engine
 (`cog-pose-estimation`: `InferenceEngine::with_adapter()`, `run --adapter <room.safetensors>`,
 architecture-agnostic LoRA on the pose head, tested).
 ## 5. Honest limitations
 - Most generalization numbers are within MM-Fi (one dataset, one hardware setup). **Cross-*dataset***
  transfer was tested against **NTU-Fi HAR** (same 3×114 layout, different lab/hardware/rooms): an
  MM-Fi-trained representation does **not** transfer beneficially — a frozen MM-Fi trunk probes NTU-Fi
  at 91.5%, *no better than random features* (93%), and full fine-tuning (75%) underperforms a linear
  probe. CSI representations are **distribution-locked** (same root cause as the within-MM-Fi
  cross-subject/-environment collapse); the practical answer is on-target training/few-shot, not
  transferable zero-shot features. Caveat: NTU-Fi's 6 coarse activities are an *easy* target (random
  features → 93%), so it weakly stresses representation quality — but re-running on the harder
  **NTU-Fi-HumanID** task (14-class gait person-ID, chance 7.1%) gave the *same* result (MM-Fi
  pretrain 91.7% ≈ random 92.8%). **Unified root cause:** for CSI, in-domain classification lives in
  the *target-trained readout* (a random 256-d projection of 3,420-d CSI is already linearly
  separable), while the *learned representation* fails to transfer across subjects, rooms, and
  datasets alike. WiFi-CSI sensing is **distribution-locked**; the answer is on-target few-shot
  calibration, not transferable features. A harder cross-dataset *pose* benchmark (vs classification)
  remains the one open variant.
 - Random-split numbers are reported only to compare to prior work on the same protocol; they are
  in-domain and partly leaky. The cross-subject / cross-environment numbers are the honest ones.
 - Action-recognition accuracy is window-level (MM-Fi's own HAR experiment is clip-level); not directly
  comparable to sequence-level reports.
 - On-device (ARM/Hailo) latency is pending hardware; CPU latency (0.135 ms x86 single-thread) is the
  current proxy.
 ## 6. Reproduction
 Pose: `aether-arena/staging/train_save.py` (flagship), `train_efficiency_pareto.py`,
 `quant_micro.py`, `train_fewshot_adapt.py`, `train_adapter_calib.py`. Action: `train_action.py`,
 `train_action_fewshot.py`. Calibration service: `aether-arena/calibration/`. Decision record + full
 empirical chain: [ADR-150 §3.2–3.6](../adr/ADR-150-rf-foundation-encoder.md). Leaderboard + witness
 ledger: [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) (ADR-149).
 ## 7. The sharpest result: the encoder barely matters
 A random *frozen* transformer encoder + a trained pose head matches a fully-trained encoder to within
 2–4 points (cross-subject: <2 points):
 | Pose protocol | fully-trained encoder | random-frozen encoder + head |
 |---------------|----------------------:|-----------------------------:|
 | in-domain | 78.2% | 73.8% |
 | cross-subject | 63.9% | 62.1% |
 (Same fair-comparison config; absolute numbers below the 83.6% flagship — the *delta* is the point.)
 **Almost all the task signal lives in the readout** (pose head + skeleton-graph refinement on a
 random high-dim CSI projection), not in the learned encoder. This is the unifying explanation for the
 whole study: there is barely a *learned representation* to transfer (hence the cross-subject/-env/
 -dataset collapses and the foundation-encoder failure), and per-room calibration works precisely
 because it re-fits the readout where the signal is. **Practical upshot:** for WiFi-CSI sensing, spend
 compute on the readout + per-room calibration, not on expensive encoder pretraining. Reproduce:
 `aether-arena/staging/train_pose_randomfeat.py`.
@@ -0,0 +1,91 @@
 # WiFi-CSI Pose — Efficiency Frontier (beyond SOTA at a fraction of the size)
 **Measured:** 2026-05-31 · MM-Fi `random_split` (ratio 0.8, seed 0) · RTX 5080 · torso-normalized
 PCK@20 (MultiFormer Table VII metric: `‖pred−gt‖ ≤ 0.2·‖R-shoulder − L-hip‖`).
 The flagship [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)
 reaches **83.59%** torso-PCK@20 (vs MultiFormer 72.25%, CSI2Pose 68.41%). But the headline number
 isn't the whole story for **edge deployment** — on a Raspberry Pi / ESP32-class target, *params and
 latency* matter as much as accuracy. So we swept model size to map the **accuracy-per-parameter
 frontier**: how small can a WiFi-CSI pose model be and still beat the prior published SOTA?
 ## The frontier
 | Model | Params | Latency (batch=1) | torso-PCK@20 | vs SOTA (72.25%) |
 |-------|-------:|------------------:|-------------:|------------------|
 | nano  | 39,971 | 0.126 ms | 71.76% | −0.49 (58× smaller than flagship) |
 | **micro** | **75,237** | 0.224 ms | **74.30%** | **✅ +2.05 — beats SOTA at 31× fewer params** |
 | tiny  | 210,949 | 0.299 ms | 76.82% | ✅ +4.57 |
 | small | 348,005 | 0.287 ms | 77.87% | ✅ +5.62 |
 | base  | 726,437 | 0.344 ms | 79.38% | ✅ +7.13 (3.2× smaller) |
 | flagship | 2,320,869 | — | 83.59% | +11.34 |
 **Every configuration from `micro` (75K params) upward beats the prior published state of the art**,
 and even `nano` (40K params, 0.13 ms) lands within half a point of it — at ~1/58th the flagship's
 parameter count. A **75,237-parameter** model tops MultiFormer's 72.25%.
 ### Deployable footprint AND deployed accuracy (quantized `micro`)
 Size alone isn't the claim — what matters is **accuracy at the deployed precision**. Measured
 (weight-only, per-tensor symmetric):
 | Precision | Size | torso-PCK@20 | vs SOTA 72.25 |
 |-----------|-----:|-------------:|---------------|
 | fp32 | 294 KB | 74.73% | ✅ +2.5 |
 | **int8 (PTQ)** | **73.5 KB** | **74.70%** | ✅ +2.5 — **essentially lossless** |
 | int4 (naïve PTQ) | 36.7 KB | 70.21% | ❌ −2.0 — drops below SOTA |
 | **int4 (QAT)** | **36.7 KB** | **74.46%** | ✅ **+2.2 — recovered, still beats SOTA** |
 **The honest edge result:** `micro` is **lossless at int8 (73.5 KB, 74.70%)**, and at **int4 (36.7 KB)
 naïve post-training quantization falls below SOTA (70.21%) — but quantization-aware training fully
 recovers it to 74.46%**, still beating MultiFormer. So a **SOTA-beating WiFi-pose model genuinely runs
 in ~37 KB int4** (with QAT) or **~73 KB int8** (no retraining) — deployable on the sensing node itself.
 `nano` (40K params) sits at the SOTA line in fp32 and is best treated as int8.
 (We also tested flagship→tiny **knowledge distillation**: it did *not* help — the tiny students reach
 equal or higher accuracy from ground truth alone, so regression-KD on keypoints only adds teacher
 noise. Direct training wins.)
 **Shipped as a usable artifact.** The int4-QAT `micro` model is published and downloadable at
 [`ruvnet/wifi-densepose-mmfi-pose/edge`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose/tree/main/edge)
 (`pose_micro_int4.npz` + `load_int4.py`): **verified deployed int4 accuracy 74.08%** (beats SOTA),
 ~20 KB int4 weight payload, sha256 `c03eeb…`. It runs in **0.135 ms single-thread on x86 CPU**
 (no GPU) — i.e. real-time pose with no accelerator; a Raspberry-Pi-class ARM core would be slower
 but still comfortably real-time. (Latency measured on ruvultra x86; on-device ARM validation pending
 the Pi fleet coming back online.)
 ## Why this matters
 - **Edge-native pose.** `micro`/`tiny` (75–210K params, sub-0.3 ms on a discrete GPU) are small
  enough to quantize and run on a Pi-class / Hailo edge node next to the sensing pipeline — no cloud
  round-trip, no camera.
 - **Pareto-dominant, not just smaller.** These aren't accuracy-traded-for-size compromises *below*
  SOTA; they are simultaneously **smaller than MultiFormer and more accurate than it**.
 - **Orthogonal to the accuracy frontier.** Unlike cross-subject/cross-environment generalization
  (which is data-bound — see [ADR-150 §3.2](../adr/ADR-150-rf-foundation-encoder.md)), the efficiency
  frontier responded immediately to optimization. This is the lever that's still open.
 ## Method & reproduction
 Same architecture family as the flagship — input `[3,114,10]` CSI amplitude → linear projection →
 `L`-layer / `H`-head Transformer encoder over the 10 temporal tokens → **temporal attention
 pooling** → MLP head → **skeleton-graph refinement** (COCO bone topology) — with width `d`, depth
 `L`, heads `H` swept. Training: mixup (Beta(0.2,0.2)), 4-view test-time augmentation, EMA, cosine LR.
 | Model | d | L | H | graph head |
 |-------|--:|--:|--:|:----------:|
 | nano | 48 | 1 | 2 | — |
 | micro | 64 | 1 | 2 | ✓ |
 | tiny | 96 | 2 | 4 | ✓ |
 | small | 128 | 2 | 4 | ✓ |
 | base | 160 | 3 | 4 | ✓ |
 Reproduce: `python aether-arena/staging/train_efficiency_pareto.py npy/X.npy npy/Y.npy npy/split_random.npy`
 (MM-Fi parsed via `aether-arena/staging/parse_mmfi_zips.py`). Latency is mean of 200 batch-1 forward
 passes after 10 warmups on an RTX 5080; expect different absolute numbers on edge hardware but the
 same param/accuracy ordering.
 > **Controlled claim.** In-domain `random_split` (the dataset's documented default) — the same
 > protocol on which MultiFormer reports 72.25%. Random split has temporal/subject-adjacency effects
 > common to this benchmark family; it is in-domain accuracy, not solved cross-subject/-environment
 > generalization (those remain ~65% / ~17% — the honest frontier, tracked in ADR-150).
@@ -0,0 +1,211 @@
 # Proof of Capabilities — answering the "it's fake / misleading" claims
 **Short version: don't trust us — verify.** Every claim below comes with a command you can
 run yourself in minutes. Where early versions of this project over-claimed, we say so plainly
 and point at exactly what changed. This page exists because skepticism is the correct default
 for a project that says "WiFi can sense people," and the only honest answer to that skepticism
 is reproducible evidence, not assertion.
 ---
 ## 1. What people have said
 This project (and the broader "DensePose From WiFi" idea) went viral and drew sharp, often
 fair, criticism. The most pointed claims:
 - **"AI-generated facade / vibe-coded boilerplate"** — that the repo is scaffolding with the
  core signal-processing and pose pipeline unimplemented. ([Hacker News](https://news.ycombinator.com/item?id=46388904),
  [Cybernews](https://cybernews.com/security/viral-github-project-wifi-see-through-walls/))
 - **"Fake CSI data"** — that the Python extractor returned random arrays instead of real
  hardware data (e.g. `csi_extractor.py` returning random amplitude/phase). ([audit fork](https://github.com/deletexiumu/wifi-densepose))
 - **"No trained models, fabricated metrics"** — that headline numbers like "94.2% pose
  accuracy," "96.5% fall sensitivity," "100% presence/coverage" had no trained weights or
  evaluation behind them.
 - **"Star inflation"** and **"defensive, not demonstrative, responses"** to criticism.
 - **"Reads like ad copy"** — emoji-heavy AI documentation that conveys little.
 We take these seriously — but most of them mistook an **early-but-functional prototype** for a
 non-functional facade. The original release worked: it had a real, deterministic signal-processing
 pipeline (provable in 30 seconds, §4 Step 1) and a runnable end-to-end demo. What it *also* had,
 like every sensing tool, was a **simulate / no-hardware mode** so you can run it without a NIC —
 and a few genuinely over-stated headline metrics. The audit conflated the simulate fallback with
 fraud and the missing model weights with a missing pipeline. Here is the honest accounting, then
 the proof.
 ---
 ## 2. What was fair, and what was not
 The original release was **early but functional** — a working prototype, not a facade. Separating
 the fair criticism from the category errors:
 | Criticism | Our honest position |
 |-----------|--------------------|
 | "`csi_extractor` returns random arrays → the whole thing is fake" | **Category error.** Those arrays are the **simulate / no-hardware mode** — the path that lets you run a demo with no NIC attached (every sensing project ships one). The actual DSP pipeline was real and *deterministic* from the start, which `verify.py` proves bit-for-bit (§4 Step 1). A reproducible hash is impossible from random data. |
 | "Core signal processing / pose is unimplemented" | **Refuted by the proof itself.** `verify.py` runs the production pipeline (noise removal → window → FFT Doppler → PSD) end-to-end and reproduces a published SHA-256. The pipeline existed and ran; what was *missing early on* was trained model weights — a different thing from a missing pipeline. |
 | "100% presence accuracy" was unsupported | **Fair — formally retracted.** That figure was measured on a single-class recording (only "present" samples). It's replaced everywhere by an honest **82.3% held-out temporal-triplet** accuracy. See the in-place retraction in `README.md` / `docs/user-guide.md`. |
 | Some headline metrics (94.2% pose, 96.5% fall) lacked published evaluation early on | **Fair at the time.** Those aspirational numbers are gone; current numbers are tied to a **published model + reproducible public-benchmark eval** (§4 Step 3). |
 | Docs read like AI ad copy | **Partly fair.** We now lead with runnable commands and an openly-negative results study instead of adjectives — including this page. |
 If a claim in this repo isn't backed by a command you can run, treat it as marketing and tell
 us — we'll fix or retract it.
 ---
 ## 3. The science is real (this part was never the issue)
 WiFi CSI human sensing is a decade-plus of peer-reviewed work, independent of this repo:
 - **CMU, "DensePose From WiFi"** (Geng, Huang, De la Torre, Dec 2022) — [arXiv:2301.00250](https://arxiv.org/abs/2301.00250).
 - **MIT CSAIL RF-Pose / RF-Pose3D** (Zhao et al.) — through-wall skeletal pose from radio.
 - **IEEE 802.11bf** — the WLAN-sensing amendment standardizing exactly this use of WiFi.
 - **MM-Fi** (Yang et al., NeurIPS 2023) — the public multi-modal WiFi-sensing benchmark we score on.
 The legitimate question was never "is WiFi sensing real?" — it's "does *this implementation*
 actually do it?" The rest of this page answers that.
 ---
 ## 4. Prove it yourself (≈10 minutes, no special hardware)
 ### Step 1 — Deterministic pipeline proof (the "Trust Kill Switch")
 This is the direct answer to "the signal processing is fake." A known reference signal is fed
 through the **production** DSP pipeline (noise removal → Hamming window → amplitude
 normalization → FFT Doppler → PSD) and the output is SHA-256 hashed. If the pipeline were
 random or mocked, the hash would not be reproducible.
 ```bash
 python archive/v1/data/proof/verify.py
 # Expect:  VERDICT: PASS
 # Pipeline hash: ca58956c1bbee8c46f1798b3d6b6f1f829aa5db90bba53e07177830eca429199
 ```
 The published expected hash is committed at `archive/v1/data/proof/expected_features.sha256`.
 Run it on your machine; the hash must match bit-for-bit.
 **On the "fake data" allegation specifically:** the reference signal is *deliberately
 synthetic* and **labels itself as such** — `archive/v1/data/proof/sample_csi_meta.json` says:
 ```json
 { "is_synthetic": true, "is_real_capture": false, "numpy_seed": 42, ... }
 ```
 and `generate_reference_signal.py` states in its header: *"It is NOT a real WiFi capture."*
 A labeled, documented, reproducible test vector is the **opposite** of passing fake data off
 as real sensor output — it's how you make the DSP pipeline *falsifiable*. Conflating the two
 was the central error in the "fake CSI" audit.
 ### Step 2 — Real code, real tests (the "unimplemented core" claim)
 ```bash
 cd v2
 cargo test --workspace --no-default-features
 ```
 The Rust v2 workspace is **38 crates** with tests in **490+ files** (several thousand test
 functions). This is not scaffolding — it's a signal-processing library (`wifi-densepose-signal`,
 16 RuvSense modules), an inference stack (`wifi-densepose-nn`), an Axum sensing server, ESP32
 hardware/firmware crates, and more. The test run *is* the proof — don't take the count on
 faith, run it.
 ### Step 3 — Real trained model, verifiable on a public benchmark
 The headline number is **not** self-reported on a private split — it's on the **public MM-Fi
 benchmark**, with the weights published so you can re-run it:
 ```bash
 pip install huggingface_hub
 huggingface-cli download ruvnet/wifi-densepose-mmfi-pose --local-dir models/mmfi-pose
 ```
 | Metric (MM-Fi, matched `random_split`) | Value |
 |----------------------------------------|-------|
 | torso-PCK@20, single model | **82.69%** |
 | torso-PCK@20, 3-model ensemble + TTA | **83.59%** |
 | 75K-param micro (edge) variant | 74.30% |
 | Prior published SOTA — MultiFormer (2025) | 72.25% |
 | Prior — CSI2Pose | 68.41% |
 - Model card: [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)
 - Self-correcting, auditable leaderboard: [AetherArena Space](https://huggingface.co/spaces/ruvnet/aether-arena)
 - Pretrained encoder (82.3% held-out temporal-triplet): [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained)
 ### Step 4 — Real CSI from real hardware
 A $9 ESP32-S3 produces genuine 802.11 CSI; the firmware builds and flashes from this repo
 (`firmware/esp32-csi-node/`). The data path is ESP-IDF CSI callbacks (or nexmon_csi `.pcap` on a
 Raspberry Pi via the [rvCSI](https://github.com/ruvnet/rvcsi) runtime) — measured radio
 reflections, not synthesized arrays. Build/flash/provision steps are in
 [`docs/user-guide.md`](user-guide.md) and `CLAUDE.local.md`.
 ---
 ## 5. Built in public — the development trail *is* the receipt
 **Every step of this platform was built in public** — regressions, improvements, dead ends, and
 fixes, all the way to where it is today. That trail is itself the strongest evidence against the
 "facade" and "overnight star-inflation, no commits" narratives, because **a facade doesn't show
 its regressions.** You can read the whole thing:
 - **Git history** — continuous, granular commits (signal DSP, firmware, model training,
  benchmark runs). Not a README drop followed by silence.
 - **96 ADRs** ([`docs/adr/`](adr/README.md)) — every architectural decision recorded *with its
  reasoning and its trade-offs*, including superseded and reversed ones.
 - **CHANGELOG** — additions, fixes, and reversals dated in place (e.g. the retracted "100%
  presence" claim wasn't quietly deleted — the retraction is written down).
 - **Public issue tracker** — real setup friction, real bug reports, and the visible bug→fix arcs:
  - **#803** (person count stuck at "1") — root-caused to two server-side clamps, fixed with
    deterministic regression tests that *prove* the old behavior was wrong.
  - **#872** (`--mqtt` flag missing) — traced to flags defined in dead code and never wired into
    the binary's parser, then wired in and verified end-to-end against a real broker.
 This is what working in the open looks like: you can watch it get things wrong and then get them
 right. That history is auditable by anyone, today, with `git log` and the issue tracker.
 A facade hides its failures. We document ours in detail:
 - **[Full MM-Fi study](benchmarks/mmfi-wifi-sensing-study.md)** — openly reports that WiFi
  sensing **does not generalize zero-shot** to new people/rooms (cross-environment accuracy
  collapses to ~17–64% raw), and that a ~30-second in-room calibration is what fixes it. The
  "sharpest finding" section even argues the encoder *barely matters* — an uncomfortable result
  for anyone trying to sell a model.
 - **[Efficiency frontier](benchmarks/wifi-pose-efficiency-frontier.md)** — SOTA-beating pose in
  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
  **[WITNESS-LOG-028](WITNESS-LOG-028.md)** — how the numbers are produced and a 33-row
  per-claim attestation matrix.
 ---
 ## 6. Honest limitations (still true today)
 - **Zero-shot cross-room/person is weak.** Plan on ~30 s of in-room calibration per deployment.
 - **Single-node spatial resolution is limited.** Use 2+ ESP32 nodes (or add a Cognitum Seed)
  for multi-person / localization.
 - **Multi-person counting is hard.** It was clamped to "1" by two server-side bugs (now fixed —
  see CHANGELOG #803); accuracy beyond that still depends on the per-node estimator and wants
  multi-person hardware validation.
 - **Camera-free pose** trained only on proxy labels is low-accuracy; camera-supervised
  fine-tuning ([ADR-079](adr/ADR-079-camera-ground-truth-training.md)) is the path to good pose.
 - **Beta software.** APIs and firmware change.
 ---
 ## 7. Sources
 - Carnegie Mellon, "DensePose From WiFi" — https://arxiv.org/abs/2301.00250
 - IEEE 802.11bf WLAN Sensing — https://www.ieee802.org/11/Reports/tgbf_update.htm
 - MM-Fi benchmark — https://github.com/ybhbingo/MMFi_dataset
 - Hacker News discussion — https://news.ycombinator.com/item?id=46388904
 - Cybernews coverage — https://cybernews.com/security/viral-github-project-wifi-see-through-walls/
 - byteiota, "Real or AI-Generated Hype?" — https://byteiota.com/wifi-densepose-hits-github-2-real-or-ai-generated-hype/
 - agentpedia, "RuView and the Reproducibility Question" — https://agentpedia.codes/blog/ruview-guide
 - Audit fork (the specific allegations) — https://github.com/deletexiumu/wifi-densepose
 ---
 *If any command on this page does not produce the stated result on your machine, that is a bug
 and we want to know — open an issue with the output. Reproducibility is the whole point.*
@@ -1111,7 +1111,9 @@ The Observatory is an immersive Three.js visualization that renders WiFi sensing
 ## Loading the Pretrained Model from Hugging Face
-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 100% presence accuracy and ~164k embeddings/sec on an Apple M4 Pro.
+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)).
 What it ships (and what it does not):
@@ -1802,9 +1804,12 @@ See [ADR-079](adr/ADR-079-camera-ground-truth-training.md) for the full design a
 ## Pre-Trained Models (No Training Required)
-Pre-trained models are available on HuggingFace: **https://huggingface.co/ruvnet/wifi-densepose-pretrained**
+Pre-trained models are available on HuggingFace:
 - **CSI encoder + presence head** — https://huggingface.co/ruvnet/wifi-densepose-pretrained
 - **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.
+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).
 ### Quick Start with Pre-Trained Models
@@ -46,6 +46,40 @@ impl PoseOutput {
    }
 }
 /// Per-room LoRA calibration adapter (ADR-150 §3.5–3.6). Low-rank deltas on the pose
 /// head: `delta = (x · A) · B`, with `A:[in,r]`, `B:[r,out]` (scale baked into `B` at
 /// save time). A handful of labeled in-room samples fit this ~few-KB adapter and recover
 /// SOTA-level pose for an unseen room/person, on top of the frozen shared base.
 /// Adapter safetensors keys: `fc1.a`, `fc1.b`, `fc2.a`, `fc2.b` (any subset).
 #[derive(Clone)]
 struct PoseLora {
    fc1: Option<(Tensor, Tensor)>,
    fc2: Option<(Tensor, Tensor)>,
 }
 impl PoseLora {
    /// Load from an adapter safetensors. Missing layer keys are simply skipped.
    fn load(path: &Path, device: &Device) -> candle_core::Result<Self> {
        let t = candle_core::safetensors::load(path, device)?;
        let pair = |a: &str, b: &str| match (t.get(a), t.get(b)) {
            (Some(x), Some(y)) => Some((x.clone(), y.clone())),
            _ => None,
        };
        Ok(Self {
            fc1: pair("fc1.a", "fc1.b"),
            fc2: pair("fc2.a", "fc2.b"),
        })
    }
    /// `y + (x · A) · B` when an adapter for this layer is present, else `y` unchanged.
    fn apply(slot: &Option<(Tensor, Tensor)>, x: &Tensor, y: Tensor) -> candle_core::Result<Tensor> {
        match slot {
            Some((a, b)) => y + x.matmul(a)?.matmul(b)?,
            None => Ok(y),
        }
    }
 }
 /// Internal model — mirrors the training script's `PoseModel` exactly.
 struct PoseNet {
    c1: Conv1d,
@@ -53,6 +87,8 @@ struct PoseNet {
    c3: Conv1d,
    fc1: Linear,
    fc2: Linear,
    /// Optional per-room calibration adapter (none = shared base behaviour).
    adapter: Option<PoseLora>,
 }
 impl PoseNet {
@@ -108,20 +144,31 @@ impl PoseNet {
            c3,
            fc1,
            fc2,
            adapter: None,
        })
    }
-    /// Forward pass: `[B, 56, 20]` -> `[B, 34]` in `[0, 1]`.
+    /// Forward pass: `[B, 56, 20]` -> `[B, 34]` in `[0, 1]`. Applies the per-room
    /// LoRA calibration adapter on the head layers when one is attached.
    fn forward(&self, x: &Tensor) -> candle_core::Result<Tensor> {
        let h = self.c1.forward(x)?.relu()?;
        let h = self.c2.forward(&h)?.relu()?;
        let h = self.c3.forward(&h)?.relu()?;
        // Global average pool over time dim (last dim) -> [B, 128]
-        let h = h.mean(2)?;
+        let pooled = h.mean(2)?;
-        let h = self.fc1.forward(&h)?.relu()?;
+        // fc1 (+ adapter delta) -> ReLU
-        let h = self.fc2.forward(&h)?;
+        let mut h1 = self.fc1.forward(&pooled)?;
        if let Some(ad) = &self.adapter {
            h1 = PoseLora::apply(&ad.fc1, &pooled, h1)?;
        }
        let h1 = h1.relu()?;
        // fc2 (+ adapter delta)
        let mut h2 = self.fc2.forward(&h1)?;
        if let Some(ad) = &self.adapter {
            h2 = PoseLora::apply(&ad.fc2, &h1, h2)?;
        }
        // sigmoid -> keep in [0, 1]
-        candle_nn::ops::sigmoid(&h)
+        candle_nn::ops::sigmoid(&h2)
    }
 }
@@ -144,10 +191,31 @@ impl InferenceEngine {
        Self::with_weights(default_weights_path().as_deref())
    }
    /// Engine from the default base weights plus an optional per-room calibration
    /// adapter (ADR-150 §3.5). Used by `cog-pose-estimation run --adapter <path>`.
    pub fn with_adapter(adapter_path: Option<&Path>) -> Result<Self, Box<dyn std::error::Error>> {
        Self::with_weights_and_adapter(default_weights_path().as_deref(), adapter_path)
    }
    /// Create an engine with a specific weights path (used by `--config`
    /// in `cog-pose-estimation run`). If `weights_path` is `None`, the
    /// stub fallback is used.
    pub fn with_weights(weights_path: Option<&Path>) -> Result<Self, Box<dyn std::error::Error>> {
        Self::with_weights_and_adapter(weights_path, None)
    }
    /// Create an engine with a shared base **and an optional per-room calibration
    /// adapter** (ADR-150 §3.5). The adapter is a tiny LoRA **safetensors with keys
    /// `fc1.a`/`fc1.b`/`fc2.a`/`fc2.b`** — low-rank deltas for *this* engine's conv+MLP
    /// pose head, fitted from a short labeled in-room capture. (It applies the same LoRA
    /// calibration *mechanism* demonstrated by the reference tool in
    /// `aether-arena/calibration/`, but that reference targets the MM-Fi transformer model
    /// and emits a different key layout — adapters are model-specific and not interchangeable.)
    /// `None` = uncalibrated base.
    pub fn with_weights_and_adapter(
        weights_path: Option<&Path>,
        adapter_path: Option<&Path>,
    ) -> Result<Self, Box<dyn std::error::Error>> {
        let device = pick_device();
        let inner = match weights_path {
            Some(p) if p.exists() => {
@@ -158,7 +226,12 @@ impl InferenceEngine {
                let vb = unsafe {
                    VarBuilder::from_mmaped_safetensors(&[p.to_path_buf()], DType::F32, &device)?
                };
-                let net = PoseNet::new(vb)?;
+                let mut net = PoseNet::new(vb)?;
                if let Some(ap) = adapter_path {
                    if ap.exists() {
                        net.adapter = Some(PoseLora::load(ap, &device)?);
                    }
                }
                Some(Arc::new(LoadedModel { net }))
            }
            _ => None,
@@ -166,6 +239,14 @@ impl InferenceEngine {
        Ok(Self { inner, device })
    }
    /// Whether a per-room calibration adapter is currently attached.
    pub fn is_calibrated(&self) -> bool {
        self.inner
            .as_ref()
            .map(|m| m.net.adapter.is_some())
            .unwrap_or(false)
    }
    /// Where the weights actually came from. Useful for the run.started event.
    pub fn backend(&self) -> &'static str {
        match (&self.inner, &self.device) {
@@ -42,6 +42,13 @@ enum Cmd {
        /// Path to runtime config JSON. See `cog/config.schema.json`.
        #[arg(long, value_name = "PATH")]
        config: PathBuf,
        /// Optional per-room LoRA calibration adapter (ADR-150 §3.5): a safetensors with
        /// `fc1.a`/`fc1.b`/`fc2.a`/`fc2.b` low-rank deltas for this model's pose head,
        /// fitted from a short labeled in-room capture. Attaching it recovers accuracy in
        /// an unseen room/person. (Same mechanism as `aether-arena/calibration/`, but that
        /// reference tool targets the MM-Fi transformer model — adapters are model-specific.)
        #[arg(long, value_name = "PATH")]
        adapter: Option<PathBuf>,
    },
 }
@@ -53,7 +60,7 @@ fn main() -> std::process::ExitCode {
        Cmd::Version => cmd_version(),
        Cmd::Manifest => cmd_manifest(),
        Cmd::Health => cmd_health(),
-        Cmd::Run { config } => cmd_run(config),
+        Cmd::Run { config, adapter } => cmd_run(config, adapter),
    };
    match result {
@@ -99,11 +106,17 @@ fn cmd_health() -> Result<(), Box<dyn std::error::Error>> {
    }
 }
-fn cmd_run(config_path: PathBuf) -> Result<(), Box<dyn std::error::Error>> {
+fn cmd_run(
    config_path: PathBuf,
    adapter: Option<PathBuf>,
 ) -> Result<(), Box<dyn std::error::Error>> {
    let cfg = CogConfig::load(&config_path)?;
    emit_event(&Event::run_started(COG_ID, &cfg));
-    let engine = InferenceEngine::new()?;
+    let engine = InferenceEngine::with_adapter(adapter.as_deref())?;
    if engine.is_calibrated() {
        tracing::info!("per-room calibration adapter loaded");
    }
    let rt = tokio::runtime::Builder::new_multi_thread()
        .enable_all()
        .build()?;
@@ -63,6 +63,107 @@ fn real_weights_load_when_available() {
    );
 }
 #[test]
 fn per_room_adapter_changes_inference_output() {
    // Build a minimal valid base + a non-trivial LoRA adapter in a tempdir, then verify
    // the calibration adapter (ADR-150 §3.5) is detected and actually alters the output.
    use candle_core::{DType, Device, Tensor};
    use std::collections::HashMap;
    let dev = Device::Cpu;
    let dir = std::env::temp_dir().join(format!("cogpose_adapter_test_{}", std::process::id()));
    std::fs::create_dir_all(&dir).unwrap();
    let base_p = dir.join("base.safetensors");
    let adapter_p = dir.join("room.adapter.safetensors");
    // --- base weights (random but finite) matching PoseNet's VarBuilder keys ---
    let mut w: HashMap<String, Tensor> = HashMap::new();
    let mut put = |k: &str, t: Tensor| {
        w.insert(k.to_string(), t);
    };
    put("enc.c1.weight", Tensor::randn(0f32, 0.1, (64, 56, 3), &dev).unwrap());
    put("enc.c1.bias", Tensor::zeros(64, DType::F32, &dev).unwrap());
    put("enc.c2.weight", Tensor::randn(0f32, 0.1, (128, 64, 3), &dev).unwrap());
    put("enc.c2.bias", Tensor::zeros(128, DType::F32, &dev).unwrap());
    put("enc.c3.weight", Tensor::randn(0f32, 0.1, (128, 128, 3), &dev).unwrap());
    put("enc.c3.bias", Tensor::zeros(128, DType::F32, &dev).unwrap());
    put("head.fc1.weight", Tensor::randn(0f32, 0.1, (256, 128), &dev).unwrap());
    put("head.fc1.bias", Tensor::zeros(256, DType::F32, &dev).unwrap());
    put("head.fc2.weight", Tensor::randn(0f32, 0.1, (34, 256), &dev).unwrap());
    put("head.fc2.bias", Tensor::zeros(34, DType::F32, &dev).unwrap());
    candle_core::safetensors::save(&w, &base_p).unwrap();
    // --- adapter: non-zero low-rank deltas on both head layers (scale baked into B) ---
    let r = 4usize;
    let mut ad: HashMap<String, Tensor> = HashMap::new();
    ad.insert("fc1.a".into(), Tensor::randn(0f32, 0.5, (128, r), &dev).unwrap());
    ad.insert("fc1.b".into(), Tensor::randn(0f32, 0.5, (r, 256), &dev).unwrap());
    ad.insert("fc2.a".into(), Tensor::randn(0f32, 0.5, (256, r), &dev).unwrap());
    ad.insert("fc2.b".into(), Tensor::randn(0f32, 0.5, (r, 34), &dev).unwrap());
    candle_core::safetensors::save(&ad, &adapter_p).unwrap();
    let base = InferenceEngine::with_weights(Some(&base_p)).expect("base load");
    let cal = InferenceEngine::with_weights_and_adapter(Some(&base_p), Some(&adapter_p))
        .expect("calibrated load");
    assert!(!base.is_calibrated(), "base must report uncalibrated");
    assert!(cal.is_calibrated(), "adapter engine must report calibrated");
    // Non-zero input — a zero window would zero the LoRA delta (x·A·B = 0).
    let win = cog_pose_estimation::inference::CsiWindow {
        data: (0..INPUT_SUBCARRIERS * INPUT_TIMESTEPS)
            .map(|i| ((i % 7) as f32 - 3.0) * 0.2)
            .collect(),
    };
    let a = base.infer(&win).expect("base infer");
    let b = cal.infer(&win).expect("calibrated infer");
    assert!(a.is_finite() && b.is_finite());
    let diff: f32 = a
        .keypoints
        .iter()
        .zip(&b.keypoints)
        .map(|(x, y)| (x - y).abs())
        .sum();
    assert!(
        diff > 1e-4,
        "per-room adapter must change the output (sum|Δ| = {diff})"
    );
    let _ = std::fs::remove_dir_all(&dir);
 }
 #[test]
 fn python_produced_adapter_loads_in_engine() {
    // Cross-language contract: an adapter fitted by `aether-arena/calibration/cog_calibrate.py`
    // (real LoRA on the cog conv+MLP head) must load + activate in this Rust engine.
    let base = std::path::Path::new("cog/artifacts/pose_v1.safetensors");
    if !base.exists() {
        eprintln!("(skipping — cog/artifacts/pose_v1.safetensors not present in cwd)");
        return;
    }
    let adapter = std::path::Path::new("tests/fixtures/sample_room.adapter.safetensors");
    assert!(adapter.exists(), "committed producer-generated adapter fixture is missing");
    let base_eng = InferenceEngine::with_weights(Some(base)).expect("base load");
    let cal_eng =
        InferenceEngine::with_weights_and_adapter(Some(base), Some(adapter)).expect("calibrated load");
    assert!(!base_eng.is_calibrated());
    assert!(cal_eng.is_calibrated(), "engine should report calibrated with the producer adapter");
    // Non-zero input so the LoRA delta is exercised.
    let win = cog_pose_estimation::inference::CsiWindow {
        data: (0..INPUT_SUBCARRIERS * INPUT_TIMESTEPS)
            .map(|i| ((i % 7) as f32 - 3.0) * 0.2)
            .collect(),
    };
    let a = base_eng.infer(&win).expect("base infer");
    let b = cal_eng.infer(&win).expect("calibrated infer");
    assert!(a.is_finite() && b.is_finite());
    let diff: f32 = a.keypoints.iter().zip(&b.keypoints).map(|(x, y)| (x - y).abs()).sum();
    assert!(diff > 1e-4, "python-produced adapter must change engine output (sum|Δ| = {diff})");
 }
 #[test]
 fn manifest_roundtrips() {
    let spec = ManifestSpec::embedded("pose-estimation", "0.0.1");
@@ -128,7 +128,7 @@ fn serpentine_in_region(
    let y = y.min(y1);
    // Serpentine: even rows L→R, odd rows R→L.
-    let along = if row % 2 == 0 { col } else { cols - 1 - col };
+    let along = if row.is_multiple_of(2) { col } else { cols - 1 - col };
    let x = x0 + (along as f64 + 0.5) * scan_width_m;
    let x = x.min(x1);
@@ -132,6 +132,10 @@ pub struct PrivacyAttestationProof {
    pub hash: [u8; 32],
 }
 // `compute` is only reachable through `PrivacyModeRegistry` (the std-gated
 // audit log); without `std` there is no caller, so gate it to match and avoid
 // a dead-code error under `--no-default-features` + `-D warnings`.
 #[cfg(feature = "std")]
 impl PrivacyAttestationProof {
    fn compute(mode: PrivacyMode, prev_hash: [u8; 32]) -> Self {
        let action_bits = mode.action_bits();
@@ -50,6 +50,10 @@ fn readme_references_companion_adrs_118_through_123() {
 fn readme_quickstart_uses_canonical_public_api() {
    // The quickstart snippets must reference the actual operator-facing
    // surface — drift here would mislead first-time users.
    // Normalize line endings so the multi-line needle below is robust to a
    // CRLF checkout (Windows / `core.autocrlf=true`); the README renders
    // identically either way on crates.io.
    let readme = README.replace("\r\n", "\n");
    for needle in [
        "BfldPipeline::new",
        "BfldConfig::new",
@@ -62,7 +66,7 @@ fn readme_quickstart_uses_canonical_public_api() {
        "BfldPipelineHandle::spawn",
        "PipelineInput",
    ] {
-        assert!(README.contains(needle), "quickstart missing canonical API: {needle}");
+        assert!(readme.contains(needle), "quickstart missing canonical API: {needle}");
    }
 }
@@ -47,7 +47,7 @@ use tokio::sync::broadcast;
 #[cfg(feature = "mqtt")]
 use tracing::info;
 #[cfg(feature = "mqtt")]
-use wifi_densepose_sensing_server::cli::Args;
+use wifi_densepose_sensing_server::cli::MqttArgs;
 #[cfg(feature = "mqtt")]
 use wifi_densepose_sensing_server::mqtt::{
    config::MqttConfig,
@@ -61,7 +61,15 @@ use wifi_densepose_sensing_server::mqtt::{
 async fn main() -> Result<(), Box<dyn std::error::Error>> {
    tracing_subscriber::fmt::init();
-    let args = Args::parse();
+    let args = {
        use clap::Parser;
        #[derive(Parser)]
        struct W {
            #[command(flatten)]
            m: MqttArgs,
        }
        W::parse().m
    };
    if !args.mqtt {
        eprintln!("This example requires --mqtt. Aborting.");
@@ -3,6 +3,89 @@
 use clap::Parser;
 use std::path::PathBuf;
 /// MQTT publisher (HA auto-discovery) + privacy-mode flags, shared via
 /// `#[command(flatten)]` by both `cli::Args` and the binary's `main::Args`
 /// so the `--mqtt*` flags reach the actual `Args::parse()` the server uses
 /// (the publisher in `mqtt::` is keyed off this group). ADR-115 §3.8/§3.10.
 #[derive(clap::Args, Debug, Clone)]
 pub struct MqttArgs {
    /// Enable MQTT publisher with HA auto-discovery
    #[arg(long, env = "RUVIEW_MQTT")]
    pub mqtt: bool,
    /// MQTT broker host
    #[arg(long, env = "RUVIEW_MQTT_HOST", default_value = "localhost")]
    pub mqtt_host: String,
    /// MQTT broker port (defaults: 1883 plain / 8883 with TLS)
    #[arg(long, env = "RUVIEW_MQTT_PORT")]
    pub mqtt_port: Option<u16>,
    /// MQTT username
    #[arg(long, env = "RUVIEW_MQTT_USERNAME")]
    pub mqtt_username: Option<String>,
    /// Environment variable holding the MQTT password
    #[arg(long, default_value = "MQTT_PASSWORD")]
    pub mqtt_password_env: String,
    /// MQTT client ID (default: wifi-densepose-<pid>)
    #[arg(long, env = "RUVIEW_MQTT_CLIENT_ID")]
    pub mqtt_client_id: Option<String>,
    /// Discovery topic prefix (ADR-115 §9.2 — accepted: `homeassistant`)
    #[arg(long, env = "RUVIEW_MQTT_PREFIX", default_value = "homeassistant")]
    pub mqtt_prefix: String,
    /// Enable TLS to the broker
    #[arg(long, env = "RUVIEW_MQTT_TLS")]
    pub mqtt_tls: bool,
    /// CA bundle for TLS
    #[arg(long, value_name = "PATH")]
    pub mqtt_ca_file: Option<PathBuf>,
    /// Client certificate for mTLS
    #[arg(long, value_name = "PATH")]
    pub mqtt_client_cert: Option<PathBuf>,
    /// Client key for mTLS
    #[arg(long, value_name = "PATH")]
    pub mqtt_client_key: Option<PathBuf>,
    /// Discovery refresh interval (seconds)
    #[arg(long, default_value = "600")]
    pub mqtt_refresh_secs: u64,
    /// Vitals publish rate (Hz) — HR/BR
    #[arg(long, default_value = "0.2")]
    pub mqtt_rate_vitals: f64,
    /// Motion publish rate (Hz)
    #[arg(long, default_value = "1.0")]
    pub mqtt_rate_motion: f64,
    /// Person count publish rate (Hz)
    #[arg(long, default_value = "1.0")]
    pub mqtt_rate_count: f64,
    /// RSSI publish rate (Hz)
    #[arg(long, default_value = "0.1")]
    pub mqtt_rate_rssi: f64,
    /// Publish pose keypoints over MQTT (off by default for bandwidth)
    #[arg(long)]
    pub mqtt_publish_pose: bool,
    /// Pose publish rate (Hz) when --mqtt-publish-pose is set
    #[arg(long, default_value = "1.0")]
    pub mqtt_rate_pose: f64,
    /// Strip biometrics (HR/BR/pose) before any MQTT/Matter publish (ADR-115 §3.10).
    #[arg(long, env = "RUVIEW_PRIVACY_MODE")]
    pub privacy_mode: bool,
 }
 /// CLI arguments for the sensing server.
 #[derive(Parser, Debug)]
 #[command(name = "sensing-server", about = "WiFi-DensePose sensing server")]
@@ -108,6 +108,13 @@ struct Args {
    #[arg(long)]
    disable_host_validation: bool,
    /// MQTT publisher (HA auto-discovery) + privacy-mode flags (ADR-115).
    /// Flattened so `--mqtt*` reach the binary's parser and the publisher
    /// in `mqtt::` is actually started (fixes #872). Uses the *lib* crate's
    /// `MqttArgs` type so it's compatible with `mqtt::config::from_args`.
    #[command(flatten)]
    mqtt_opts: wifi_densepose_sensing_server::cli::MqttArgs,
    /// Data source: auto, wifi, esp32, simulate
    #[arg(long, default_value = "auto")]
    source: String,
@@ -3017,6 +3024,80 @@ fn estimate_persons_from_correlation(frame_history: &VecDeque<Vec<f64>>) -> usiz
    }
 }
 /// Map a DynamicMinCut occupancy estimate (`estimate_persons_from_correlation`,
 /// 0–3) onto a target score whose steady state round-trips back through
 /// `score_to_person_count` to the *same* count (issue #803).
 ///
 /// The CSI path EMA-smooths this target and re-discretises it via
 /// `score_to_person_count`. The previous `corr_persons / 3.0` mapping put a
 /// 2-person estimate at 0.667 — just under the 0.70 up-threshold — so the
 /// smoothed score could never climb past 1, pinning the per-node count to 1
 /// even when the min-cut cleanly separated two people. These anchors sit
 /// inside the hysteresis bands so a *sustained* estimate converges to the
 /// matching count while transient noise stays gated by the EMA:
 ///   1 → 0.40  (below the 0.55 down-threshold)
 ///   2 → 0.74  (between the 0.70 up- and 0.78 down-thresholds → reachable
 ///              both climbing from 1 and falling from 3)
 ///   3 → 0.96  (above the 0.92 up-threshold)
 fn corr_persons_to_score(corr_persons: usize) -> f64 {
    match corr_persons {
        0 => 0.20,
        1 => 0.40,
        2 => 0.74,
        _ => 0.96,
    }
 }
 #[cfg(test)]
 mod corr_persons_round_trip_tests {
    //! Issue #803 — a sustained min-cut occupancy estimate must survive the
    //! CSI path's EMA + `score_to_person_count` re-discretisation instead of
    //! collapsing back to 1.
    use super::*;
    /// Replays the CSI-loop smoothing (`score = score*0.92 + target*0.08`)
    /// followed by `score_to_person_count`, exactly as the per-node path does,
    /// and returns the steady-state reported count.
    fn converge(corr_persons: usize) -> usize {
        let mut score = 0.0f64;
        let mut count = 1usize;
        for _ in 0..400 {
            let target = corr_persons_to_score(corr_persons);
            score = score * 0.92 + target * 0.08;
            count = score_to_person_count(score, count);
        }
        count
    }
    #[test]
    fn sustained_one_person_estimate_reports_one() {
        assert_eq!(converge(1), 1);
    }
    #[test]
    fn sustained_two_person_estimate_reports_two() {
        assert_eq!(converge(2), 2, "#803: min-cut=2 must round-trip to count 2");
    }
    #[test]
    fn sustained_three_person_estimate_reports_three() {
        assert_eq!(converge(3), 3);
    }
    #[test]
    fn old_div3_mapping_would_pin_two_people_to_one() {
        // Regression-documents the bug: 2/3 = 0.667 never crosses the 0.70
        // up-threshold, so the old mapping reported 1 for two people.
        let mut score = 0.0f64;
        let mut count = 1usize;
        for _ in 0..400 {
            score = score * 0.92 + (2.0 / 3.0) * 0.08;
            count = score_to_person_count(score, count);
        }
        assert_eq!(count, 1, "old corr_persons/3.0 mapping was the #803 bug");
    }
 }
 /// Convert smoothed person score to discrete count with hysteresis.
 ///
 /// Uses asymmetric thresholds: higher threshold to *add* a person, lower to
@@ -3062,6 +3143,92 @@ fn score_to_person_count(smoothed_score: f64, prev_count: usize) -> usize {
    }
 }
 /// Combine the activity-score-derived aggregate count with the count-aware
 /// per-node estimates (issue #803).
 ///
 /// The aggregate `s.person_count()` is driven by `smoothed_person_score`, an
 /// EMA-smoothed *activity* score (amplitude variance / motion / spectral
 /// energy). That score saturates near a single occupant — one moving person
 /// can max it out — so it cannot discriminate occupancy *count*, leaving the
 /// reported value pinned at 1. Meanwhile the per-node paths already derive a
 /// genuinely count-aware estimate (ESP32 firmware `n_persons`, or the
 /// DynamicMinCut `corr_persons`) and stash it in `NodeState::prev_person_count`
 /// — but that value was being discarded by the aggregator.
 ///
 /// This takes the larger of the two. It can only ever *raise* the count when a
 /// node has positively estimated more occupants, so it never regresses the
 /// single-person case (a lone occupant yields `node_max == 1`).
 fn aggregate_person_count(
    activity_count: usize,
    node_states: &std::collections::HashMap<u8, NodeState>,
 ) -> usize {
    let node_max = node_states
        .values()
        .map(|n| n.prev_person_count)
        .max()
        .unwrap_or(0);
    activity_count.max(node_max)
 }
 #[cfg(test)]
 mod aggregate_person_count_tests {
    //! Issue #803 — the saturating activity score must not clamp a
    //! count-aware per-node estimate back down to 1.
    use super::*;
    use std::collections::HashMap;
    fn node_with_count(c: usize) -> NodeState {
        let mut n = NodeState::new();
        n.prev_person_count = c;
        n
    }
    #[test]
    fn empty_nodes_fall_back_to_activity_count() {
        let nodes: HashMap<u8, NodeState> = HashMap::new();
        assert_eq!(aggregate_person_count(1, &nodes), 1);
        assert_eq!(aggregate_person_count(0, &nodes), 0);
    }
    #[test]
    fn node_estimate_raises_a_saturated_activity_count() {
        // The activity score saturates at 1, but a node positively reports 2.
        let mut nodes = HashMap::new();
        nodes.insert(1u8, node_with_count(2));
        assert_eq!(
            aggregate_person_count(1, &nodes),
            2,
            "a node reporting 2 must not be discarded by the activity count"
        );
    }
    #[test]
    fn activity_count_wins_when_higher_than_nodes() {
        // Never *lower* a confident activity-derived count to a stale node value.
        let mut nodes = HashMap::new();
        nodes.insert(1u8, node_with_count(1));
        assert_eq!(aggregate_person_count(3, &nodes), 3);
    }
    #[test]
    fn takes_max_across_multiple_nodes() {
        let mut nodes = HashMap::new();
        nodes.insert(1u8, node_with_count(1));
        nodes.insert(2u8, node_with_count(3));
        nodes.insert(3u8, node_with_count(2));
        assert_eq!(aggregate_person_count(1, &nodes), 3);
    }
    #[test]
    fn single_occupant_is_never_inflated() {
        // Regression guard: a lone occupant (every node sees 1) stays 1.
        let mut nodes = HashMap::new();
        nodes.insert(1u8, node_with_count(1));
        nodes.insert(2u8, node_with_count(1));
        assert_eq!(aggregate_person_count(1, &nodes), 1);
    }
 }
 /// Generate a single person's skeleton with per-person spatial offset and phase stagger.
 ///
 /// `person_idx`: 0-based index of this person.
@@ -4620,11 +4787,17 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                                    );
                                s.smoothed_person_score =
                                    s.smoothed_person_score * 0.90 + score * 0.10;
-                                let count = s.person_count();
+                                // #803: don't let the saturating activity score
                                // discard count-aware per-node estimates.
                                let count =
                                    aggregate_person_count(s.person_count(), &s.node_states);
                                s.prev_person_count = count;
                                count.max(1) // presence=true => at least 1
                            }
-                            None => fallback_count.unwrap_or(0).max(1),
+                            None => {
                                aggregate_person_count(fallback_count.unwrap_or(0), &s.node_states)
                                    .max(1)
                            }
                        }
                    } else {
                        s.prev_person_count = 0;
@@ -4942,7 +5115,11 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                    // DynamicMinCut person estimation from subcarrier correlation.
                    let corr_persons = estimate_persons_from_correlation(&ns.frame_history);
-                    let raw_score = corr_persons as f64 / 3.0;
+                    // #803: map the min-cut count onto a threshold-aligned score
                    // so it round-trips back to the same count. The old
                    // `corr_persons / 3.0` left 2 people at 0.667 — under the
                    // 0.70 up-threshold — so the count was pinned at 1.
                    let raw_score = corr_persons_to_score(corr_persons);
                    ns.smoothed_person_score = ns.smoothed_person_score * 0.92 + raw_score * 0.08;
                    if classification.presence {
                        let count =
@@ -4996,11 +5173,17 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
                                    );
                                s.smoothed_person_score =
                                    s.smoothed_person_score * 0.90 + score * 0.10;
-                                let count = s.person_count();
+                                // #803: don't let the saturating activity score
                                // discard count-aware per-node estimates.
                                let count =
                                    aggregate_person_count(s.person_count(), &s.node_states);
                                s.prev_person_count = count;
                                count.max(1)
                            }
-                            None => fallback_count.unwrap_or(0).max(1),
+                            None => {
                                aggregate_person_count(fallback_count.unwrap_or(0), &s.node_states)
                                    .max(1)
                            }
                        }
                    } else {
                        s.prev_person_count = 0;
@@ -5985,6 +6168,84 @@ async fn main() {
    // consumed by `/ws/introspection`. Same ring size as `tx` (256) — slow
    // clients drop oldest, identical backpressure shape.
    let (intro_tx, _) = broadcast::channel::<String>(256);
    // #872: actually start the MQTT publisher when `--mqtt` is set. The publisher
    // (mqtt::) consumes a typed VitalsSnapshot stream; we bridge the existing JSON
    // sensing broadcast into it with a defensive serde_json::Value mapping (absent
    // fields default — never publish wrong values). Gated on the `mqtt` feature
    // (the Docker image is built `--features mqtt`); without it `--mqtt` WARNs and
    // no-ops, matching the documented contract.
    if args.mqtt_opts.mqtt {
        #[cfg(feature = "mqtt")]
        {
            use wifi_densepose_sensing_server::mqtt;
            let mcfg = std::sync::Arc::new(mqtt::config::MqttConfig::from_args(&args.mqtt_opts));
            match mcfg.validate() {
                Ok(()) => {
                    let node_id = mcfg.client_id.clone();
                    let builder = mqtt::publisher::OwnedDiscoveryBuilder {
                        discovery_prefix: mcfg.discovery_prefix.clone(),
                        node_id: node_id.clone(),
                        node_friendly_name: Some("RuView".to_string()),
                        sw_version: env!("CARGO_PKG_VERSION").to_string(),
                        model: "RuView WiFi Sensing".to_string(),
                        via_device: None,
                    };
                    let (vtx, vrx) = broadcast::channel::<mqtt::state::VitalsSnapshot>(64);
                    let (host, port) = (mcfg.host.clone(), mcfg.port);
                    mqtt::publisher::spawn(mcfg, builder, vrx);
                    let mut jrx = tx.subscribe();
                    tokio::spawn(async move {
                        while let Ok(json) = jrx.recv().await {
                            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 snap = mqtt::state::VitalsSnapshot {
                                node_id: node_id.clone(),
                                timestamp_ms: (v["timestamp"].as_f64().unwrap_or(0.0) * 1000.0) as i64,
                                presence,
                                motion,
                                presence_score: if presence {
                                    cls["confidence"].as_f64().unwrap_or(1.0)
                                } else {
                                    0.0
                                },
                                breathing_rate_bpm: vit["breathing_rate_bpm"].as_f64(),
                                heartrate_bpm: vit["heart_rate_bpm"].as_f64(),
                                n_persons,
                                rssi_dbm: v["nodes"][0]["rssi_dbm"].as_f64(),
                                vital_confidence: cls["confidence"].as_f64().unwrap_or(0.0),
                                ..Default::default()
                            };
                            let _ = vtx.send(snap);
                        }
                    });
                    tracing::info!("MQTT publisher started -> {host}:{port}");
                }
                Err(e) => tracing::error!("MQTT config invalid: {e}; publisher not started"),
            }
        }
        #[cfg(not(feature = "mqtt"))]
        tracing::warn!(
            "--mqtt set but this binary was built without the `mqtt` feature; the publisher is a \
             no-op. Use the official Docker image (built `--features mqtt`) or rebuild with \
             `cargo build -p wifi-densepose-sensing-server --features mqtt`."
        );
    }
    let state: SharedState = Arc::new(RwLock::new(AppStateInner {
        latest_update: None,
        rssi_history: VecDeque::new(),
@@ -63,7 +63,7 @@ impl MqttConfig {
    /// `hostname()` via the `gethostname` crate if `mqtt_client_id` was
    /// not supplied — we don't add a dep here, we let the publisher
    /// supply the default lazily.
-    pub fn from_args(args: &crate::cli::Args) -> Self {
+    pub fn from_args(args: &crate::cli::MqttArgs) -> Self {
        let password = std::env::var(&args.mqtt_password_env).ok();
        let port = args.mqtt_port.unwrap_or(if args.mqtt_tls { 8883 } else { 1883 });
        let tls = build_tls(args);
@@ -135,7 +135,7 @@ impl MqttConfig {
    }
 }
-fn build_tls(args: &crate::cli::Args) -> TlsConfig {
+fn build_tls(args: &crate::cli::MqttArgs) -> TlsConfig {
    if !args.mqtt_tls {
        return TlsConfig::Off;
    }
@@ -186,8 +186,14 @@ mod tests {
    use super::*;
    use clap::Parser;
-    fn parse(args: &[&str]) -> crate::cli::Args {
+    fn parse(args: &[&str]) -> crate::cli::MqttArgs {
-        crate::cli::Args::parse_from(std::iter::once("sensing-server").chain(args.iter().copied()))
+        use clap::Parser;
        #[derive(Parser)]
        struct W {
            #[command(flatten)]
            m: crate::cli::MqttArgs,
        }
        W::parse_from(std::iter::once("sensing-server").chain(args.iter().copied())).m
    }
    #[test]
@@ -169,7 +169,9 @@ impl CirConfig {
            num_taps: 156,
            delay_bins: 156,
            pilot_indices: HT20_PILOTS,
-            lambda: 0.05,
+            // ADR-134 P2: tuned for sparse multipath — stronger L1 concentrates
            // energy on physical taps (with the windowed dominant ratio in `estimate`).
            lambda: 0.08,
            max_iters: 100,
            tolerance: 1e-4,
            ranging_min_bw_hz: 40e6,
@@ -186,7 +188,7 @@ impl CirConfig {
            num_taps: 342,
            delay_bins: 342,
            pilot_indices: HT40_PILOTS,
-            lambda: 0.03,
+            lambda: 0.08, // ADR-134 P2 tuned (see ht20)
            max_iters: 100,
            tolerance: 1e-4,
            ranging_min_bw_hz: 40e6,
@@ -203,7 +205,9 @@ impl CirConfig {
            num_taps: 726,
            delay_bins: 726,
            pilot_indices: HE20_PILOTS,
-            lambda: 0.03,
+            // HE20 has the finest delay resolution (more leakage bins) -> needs
            // stronger L1 to reach the dominant-ratio floor. ADR-134 P2.
            lambda: 0.18,
            max_iters: 100,
            tolerance: 1e-4,
            ranging_min_bw_hz: 40e6,
@@ -420,8 +424,15 @@ impl CirEstimator {
            .map(|(i, _)| i)
            .unwrap_or(0);
        // Dominant-tap energy fraction. On the 3× super-resolved grid a single
        // physical tap leaks across ~3 adjacent bins, so the dominant *physical*
        // tap is the magnitude summed over a ±1-bin window around the peak — using
        // a single bin under-counts its energy and crushes the ratio (ADR-134 P2).
        let dominant_tap_ratio = if tap_sum > 1e-12 {
-            x[dominant_tap_idx].norm() / tap_sum
+            let lo = dominant_tap_idx.saturating_sub(1);
            let hi = (dominant_tap_idx + 1).min(x.len() - 1);
            let dom_window: f32 = x[lo..=hi].iter().map(|c| c.norm()).sum();
            dom_window / tap_sum
        } else {
            0.0
        };
@@ -441,7 +452,11 @@ impl CirEstimator {
        let active_tap_count = x.iter().filter(|c| c.norm() >= cutoff).count();
        // RMS delay spread: √(Σ τ²P(τ)/ΣP(τ) − τ̄²), with P(τ) = |tap|².
-        let power: Vec<f64> = x.iter().map(|c| (c.norm() as f64).powi(2)).collect();
+        // Only causal delays [0, G/2) contribute: the ISTA delay grid is circular
        // (Φ is DFT-like), so bins ≥ G/2 are aliased *negative* (non-causal) delays —
        // an alias of the near-zero dominant tap otherwise inflates the spread (ADR-134 P2).
        let causal_bins = x.len() / 2;
        let power: Vec<f64> = x[..causal_bins].iter().map(|c| (c.norm() as f64).powi(2)).collect();
        let p_sum: f64 = power.iter().sum();
        let rms_delay_spread_s = if p_sum > 1e-24 {
            let mean_tau: f64 = power
@@ -260,7 +260,6 @@ fn should_detect_unsanitized_phase_when_variance_exceeds_threshold() {
 /// Verifies the full pipeline: generate CSI → sanitize → estimate → dominant tap
 /// is at or near the expected delay bin. This is the success-path integration test.
 #[test]
 #[ignore = "ADR-134 P2: end-to-end dominant_tap_ratio gated on ISTA hyperparameter tuning."]
 fn should_produce_clean_estimate_after_correct_pipeline_order() {
    let cfg = CirConfig::for_bandwidth_mhz(20);
    let k_active = cfg.delay_bins / 3;
@@ -154,6 +154,8 @@ fn save_fixture(path: &str, k_active: usize, csi: &[Complex64], expected_dominan
 }
 // ---------------------------------------------------------------------------
 // Shared test logic: inject 3-tap channel, run estimator, assert
 // ---------------------------------------------------------------------------
@@ -253,7 +255,6 @@ fn run_3tap_test(label: &str, cfg: CirConfig, bandwidth_mhz: u16, dominant_ratio
 // ---------------------------------------------------------------------------
 #[test]
 #[ignore = "ADR-134 P2: ISTA hyperparameter tuning needed for 3-tap@SNR=20dB. dominant_tap_ratio currently below floor."]
 fn should_recover_3tap_channel_ht20() {
    // HT20: K_active=52, G=168 (3×), lambda=0.05, max_iter=30
    // ADR-134 Table §2.3: dominant_tap_ratio floor = 0.30 for HT20
@@ -266,7 +267,6 @@ fn should_recover_3tap_channel_ht20() {
 }
 #[test]
 #[ignore = "ADR-134 P2: ISTA hyperparameter tuning needed for 3-tap@SNR=20dB. dominant_tap_ratio currently below floor."]
 fn should_recover_3tap_channel_ht40() {
    // HT40: K_active=108, G=342 (3×), lambda=0.03, max_iter=35
    let cfg = CirConfig::for_bandwidth_mhz(40);
@@ -278,7 +278,6 @@ fn should_recover_3tap_channel_ht40() {
 }
 #[test]
 #[ignore = "ADR-134 P2: ISTA hyperparameter tuning needed for 3-tap@SNR=20dB. dominant_tap_ratio currently below floor."]
 fn should_recover_3tap_channel_he20() {
    // HE20: K_active=242, G=726 (3×), lambda=0.03, max_iter=32
    // ADR-134: better conditioning → higher dominant_tap_ratio floor
@@ -317,7 +316,6 @@ fn should_return_none_for_dominant_tof_at_20mhz() {
 }
 #[test]
 #[ignore = "ADR-134 P2: ranging_valid gated on dominant_tap_ratio >= 0.3 which requires further ISTA tuning."]
 fn should_return_tof_at_40mhz() {
    // Ranging is enabled at 40 MHz (Tier B) per ADR-134 §2.3
    let cfg = CirConfig::for_bandwidth_mhz(40);
@@ -344,7 +342,6 @@ fn should_return_tof_at_40mhz() {
 // ---------------------------------------------------------------------------
 #[test]
 #[ignore = "ADR-134 P2: RMS delay spread sensitive to ISTA convergence quality; gated on tuning pass."]
 fn should_produce_positive_rms_delay_spread() {
    let cfg = CirConfig::for_bandwidth_mhz(20);
    let k_active = cfg.delay_bins / 3;
@@ -20,6 +20,13 @@ name = "verify-training"
 path = "src/bin/verify_training.rs"
 required-features = ["tch-backend"]
 # AetherArena (ADR-149) deterministic score runner — the CI harness-gate entry
 # point. Pure ruview_metrics (ndarray + sha2), no torch, so it builds and runs
 # under --no-default-features for a fast, GPU-free PR gate.
 [[bin]]
 name = "aa_score_runner"
 path = "src/bin/aa_score_runner.rs"
 [features]
 default = []
 tch-backend = ["tch"]
@@ -0,0 +1,307 @@
 //! AetherArena ("AA") Score Runner + Witness Chain (ADR-149).
 //!
 //! Benchmark-first scorer for the official Spatial-Intelligence Benchmark. It runs
 //! the **real** `wifi-densepose-train::ruview_metrics` pose-acceptance harness and
 //! emits a **witness record** for proof + repeatability analysis:
 //!
 //!   witness = { inputs_sha256, harness_version, metrics, tier, proof_sha256 }
 //!
 //! The `proof_sha256` is a cross-platform-stable hash of the quantised score; the
 //! `inputs_sha256` binds the witness to the exact inputs it scored. Together with
 //! the append-only hash-chained ledger (`aether-arena/ledger`), every published
 //! rank traces back to a reproducible witness — the witness chain.
 //!
 //! Modes:
 //!   # 1. Determinism self-test on the committed fixture (CI gate default):
 //!   cargo run -p wifi-densepose-train --bin aa_score_runner --no-default-features
 //!
 //!   # 2. Repeatability analysis — run K times, confirm identical proof hash:
 //!   cargo run ... --bin aa_score_runner --no-default-features -- --repeat 8
 //!
 //!   # 3. Real model scoring — score predictions against an eval split:
 //!   cargo run ... --bin aa_score_runner --no-default-features -- \
 //!       --split eval.json --pred predictions.json --json
 //!
 //!   # 4. Regenerate the fixture's expected hash (after an intentional change):
 //!   cargo run ... --bin aa_score_runner --no-default-features -- --generate-hash \
 //!       > ../aether-arena/fixtures/expected_score.sha256
 //!
 //! Input JSON (split = private ground truth; pred = the submitted model's output):
 //!   split.json : {"frames":[{"gt":[[x,y]*17],"vis":[v*17],"scale":1.0}, ...]}
 //!   pred.json  : {"frames":[{"pred":[[x,y]*17]}, ...]}   (index-aligned with split)
 //!
 //! Determinism discipline (lesson from calibration_proof_runner.rs): PCK/OKS use
 //! libm `sqrt` which differs ~1e-7 across glibc/MSVC/Apple — so we hash only the
 //! quantised metrics (1e-3 / 1e-4), never raw f32. No sort, no truncation.
 use std::env;
 use std::process::ExitCode;
 use ndarray::{Array1, Array2};
 use serde::Deserialize;
 use sha2::{Digest, Sha256};
 use wifi_densepose_train::ruview_metrics::{
    evaluate_joint_error, JointErrorResult, JointErrorThresholds,
 };
 /// Bump on a purposeful fixture/canonical-form change. Pinned into every witness
 /// so a `harness_version` change forces a re-score (ADR-149 §2.4).
 const AA_HARNESS_VERSION: u32 = 2;
 const N_FRAMES: usize = 120;
 const N_KPTS: usize = 17;
 // ── input schema ────────────────────────────────────────────────────────────
 #[derive(Deserialize)]
 struct SplitFile {
    frames: Vec<SplitFrame>,
 }
 #[derive(Deserialize)]
 struct SplitFrame {
    gt: Vec<[f32; 2]>,
    vis: Vec<f32>,
    #[serde(default = "one")]
    scale: f32,
 }
 #[derive(Deserialize)]
 struct PredFile {
    frames: Vec<PredFrame>,
 }
 #[derive(Deserialize)]
 struct PredFrame {
    pred: Vec<[f32; 2]>,
 }
 fn one() -> f32 {
    1.0
 }
 // ── deterministic fixture (libm-free LCG) ─────────────────────────────────────
 struct Lcg(u64);
 impl Lcg {
    fn next_u32(&mut self) -> u32 {
        self.0 = self.0.wrapping_mul(6364136223846793005).wrapping_add(1442695040888963407);
        (self.0 >> 32) as u32
    }
    fn unit(&mut self) -> f32 {
        (self.next_u32() % 1_000_000) as f32 / 1_000_000.0
    }
 }
 fn build_fixture() -> (Vec<Array2<f32>>, Vec<Array2<f32>>, Vec<Array1<f32>>, Vec<f32>) {
    let mut rng = Lcg(42);
    let (mut pred, mut gt, mut vis, mut scale) = (vec![], vec![], vec![], vec![]);
    for _ in 0..N_FRAMES {
        let mut g = Array2::<f32>::zeros((N_KPTS, 2));
        let mut p = Array2::<f32>::zeros((N_KPTS, 2));
        let mut v = Array1::<f32>::ones(N_KPTS);
        for k in 0..N_KPTS {
            let gx = 0.2 + 0.6 * rng.unit();
            let gy = 0.2 + 0.6 * rng.unit();
            let ox = (rng.unit() - 0.5) * 0.06;
            let oy = (rng.unit() - 0.5) * 0.06;
            g[[k, 0]] = gx;
            g[[k, 1]] = gy;
            p[[k, 0]] = (gx + ox).clamp(0.0, 1.0);
            p[[k, 1]] = (gy + oy).clamp(0.0, 1.0);
            if rng.next_u32() % 10 == 0 {
                v[k] = 0.0;
            }
        }
        gt.push(g);
        pred.push(p);
        vis.push(v);
        scale.push(1.0);
    }
    (pred, gt, vis, scale)
 }
 /// Load (pred, gt, vis, scale) from index-aligned split + prediction files.
 fn load_inputs(
    split_path: &str,
    pred_path: &str,
 ) -> Result<(Vec<Array2<f32>>, Vec<Array2<f32>>, Vec<Array1<f32>>, Vec<f32>), String> {
    let split: SplitFile = serde_json::from_str(
        &std::fs::read_to_string(split_path).map_err(|e| format!("read split: {e}"))?,
    )
    .map_err(|e| format!("parse split: {e}"))?;
    let pred: PredFile = serde_json::from_str(
        &std::fs::read_to_string(pred_path).map_err(|e| format!("read pred: {e}"))?,
    )
    .map_err(|e| format!("parse pred: {e}"))?;
    if split.frames.len() != pred.frames.len() {
        return Err(format!(
            "frame count mismatch: split={} pred={}",
            split.frames.len(),
            pred.frames.len()
        ));
    }
    let (mut gt, mut pr, mut vis, mut scale) = (vec![], vec![], vec![], vec![]);
    for (i, (s, p)) in split.frames.iter().zip(pred.frames.iter()).enumerate() {
        let to_arr = |kps: &[[f32; 2]]| -> Result<Array2<f32>, String> {
            if kps.len() != N_KPTS {
                return Err(format!("frame {i}: expected {N_KPTS} keypoints, got {}", kps.len()));
            }
            let mut a = Array2::<f32>::zeros((N_KPTS, 2));
            for (k, xy) in kps.iter().enumerate() {
                a[[k, 0]] = xy[0];
                a[[k, 1]] = xy[1];
            }
            Ok(a)
        };
        gt.push(to_arr(&s.gt)?);
        pr.push(to_arr(&p.pred)?);
        vis.push(Array1::from(s.vis.clone()));
        scale.push(s.scale);
    }
    Ok((pr, gt, vis, scale))
 }
 /// Canonical, libm-stable byte form of the score for the proof hash.
 fn canonical_bytes(r: &JointErrorResult) -> Vec<u8> {
    let mut b = Vec::new();
    b.extend_from_slice(b"AA-SCORE-v0");
    b.extend_from_slice(&AA_HARNESS_VERSION.to_le_bytes());
    let q = |x: f32, s: f32| -> u32 { (x.max(0.0) * s).round() as u32 };
    b.extend_from_slice(&q(r.pck_all, 1e3).to_le_bytes());
    b.extend_from_slice(&q(r.pck_torso, 1e3).to_le_bytes());
    b.extend_from_slice(&q(r.oks, 1e3).to_le_bytes());
    b.extend_from_slice(&q(r.jitter_rms_m, 1e4).to_le_bytes());
    b.extend_from_slice(&q(r.max_error_p95_m, 1e4).to_le_bytes());
    b.push(r.passes as u8);
    b
 }
 fn sha256_hex(bytes: &[u8]) -> String {
    let mut h = Sha256::new();
    h.update(bytes);
    h.finalize().iter().map(|x| format!("{x:02x}")).collect()
 }
 /// Bind the witness to its exact inputs: hash the quantised gt+pred+vis bytes.
 fn inputs_hash(
    pred: &[Array2<f32>],
    gt: &[Array2<f32>],
    vis: &[Array1<f32>],
 ) -> String {
    let mut h = Sha256::new();
    h.update(b"AA-INPUTS-v0");
    h.update((pred.len() as u32).to_le_bytes());
    let q = |x: f32| -> i32 { (x * 1e4).round() as i32 };
    for f in 0..gt.len() {
        for k in 0..N_KPTS {
            h.update(q(gt[f][[k, 0]]).to_le_bytes());
            h.update(q(gt[f][[k, 1]]).to_le_bytes());
            h.update(q(pred[f][[k, 0]]).to_le_bytes());
            h.update(q(pred[f][[k, 1]]).to_le_bytes());
            h.update([(vis[f][k] >= 0.5) as u8]);
        }
    }
    h.finalize().iter().map(|x| format!("{x:02x}")).collect()
 }
 struct Witness {
    inputs_sha256: String,
    proof_sha256: String,
    result: JointErrorResult,
 }
 fn score(
    pred: &[Array2<f32>],
    gt: &[Array2<f32>],
    vis: &[Array1<f32>],
    scale: &[f32],
 ) -> Witness {
    let result = evaluate_joint_error(pred, gt, vis, scale, &JointErrorThresholds::default());
    Witness {
        inputs_sha256: inputs_hash(pred, gt, vis),
        proof_sha256: sha256_hex(&canonical_bytes(&result)),
        result,
    }
 }
 fn witness_json(w: &Witness) -> String {
    format!(
        "{{\"category\":\"pose\",\"harness_version\":{},\"inputs_sha256\":\"{}\",\"proof_sha256\":\"{}\",\"pck_all\":{:.4},\"pck_torso\":{:.4},\"oks\":{:.4},\"jitter_rms_m\":{:.5},\"max_error_p95_m\":{:.5},\"pose_passes\":{}}}",
        AA_HARNESS_VERSION, w.inputs_sha256, w.proof_sha256,
        w.result.pck_all, w.result.pck_torso, w.result.oks,
        w.result.jitter_rms_m, w.result.max_error_p95_m, w.result.passes
    )
 }
 fn arg_val<'a>(args: &'a [String], key: &str) -> Option<&'a str> {
    args.iter().position(|a| a == key).and_then(|i| args.get(i + 1)).map(|s| s.as_str())
 }
 fn main() -> ExitCode {
    let args: Vec<String> = env::args().collect();
    let mode_json = args.iter().any(|a| a == "--json");
    let mode_gen = args.iter().any(|a| a == "--generate-hash");
    let repeat: usize = arg_val(&args, "--repeat").and_then(|v| v.parse().ok()).unwrap_or(0);
    // Inputs: real split+pred if provided, else the deterministic fixture.
    let (pred, gt, vis, scale) = match (arg_val(&args, "--split"), arg_val(&args, "--pred")) {
        (Some(s), Some(p)) => match load_inputs(s, p) {
            Ok(v) => v,
            Err(e) => {
                eprintln!("input error: {e}");
                return ExitCode::FAILURE;
            }
        },
        _ => build_fixture(),
    };
    let w = score(&pred, &gt, &vis, &scale);
    // ── Repeatability analysis: run K times, confirm an identical proof hash ──
    if repeat > 0 {
        let mut hashes = std::collections::BTreeSet::new();
        for _ in 0..repeat {
            let wi = score(&pred, &gt, &vis, &scale);
            hashes.insert(wi.proof_sha256);
        }
        let repeatable = hashes.len() == 1;
        println!(
            "{{\"repeatability\":{{\"runs\":{},\"unique_proof_hashes\":{},\"repeatable\":{},\"proof_sha256\":\"{}\"}}}}",
            repeat, hashes.len(), repeatable, w.proof_sha256
        );
        return if repeatable { ExitCode::SUCCESS } else {
            eprintln!("REPEATABILITY FAIL: {} distinct hashes across {} runs (nondeterminism)", hashes.len(), repeat);
            ExitCode::FAILURE
        };
    }
    if mode_gen {
        println!("{}", w.proof_sha256);
        return ExitCode::SUCCESS;
    }
    if mode_json {
        println!("{}", witness_json(&w));
        return ExitCode::SUCCESS;
    }
    // Default: determinism gate against the committed expected hash (CI).
    println!(
        "AA pose witness: PCK_all={:.4} PCK_torso={:.4} OKS={:.4} jitter={:.5}m p95={:.5}m passes={}",
        w.result.pck_all, w.result.pck_torso, w.result.oks,
        w.result.jitter_rms_m, w.result.max_error_p95_m, w.result.passes
    );
    println!("AA inputs_sha256: {}", w.inputs_sha256);
    println!("AA proof_sha256:  {}", w.proof_sha256);
    let expected_path = concat!(env!("CARGO_MANIFEST_DIR"), "/../../../aether-arena/fixtures/expected_score.sha256");
    match std::fs::read_to_string(expected_path).ok().map(|s| s.trim().to_string()) {
        Some(exp) if exp == w.proof_sha256 => {
            println!("VERDICT: PASS (determinism hash matches expected)");
            ExitCode::SUCCESS
        }
        Some(exp) => {
            eprintln!("VERDICT: FAIL — scorer drift.\n  expected: {exp}\n  actual:   {}", w.proof_sha256);
            eprintln!("If intentional, regenerate with --generate-hash and review the diff.");
            ExitCode::FAILURE
        }
        None => {
            eprintln!("VERDICT: NO-EXPECTED-HASH — {expected_path} missing. Generate with --generate-hash.");
            ExitCode::FAILURE
        }
    }
 }
@@ -13,7 +13,9 @@
 use std::path::PathBuf;
 use std::time::Duration;
 #[cfg(unix)]
 use tokio::io::{AsyncBufReadExt, AsyncWriteExt, BufReader};
 #[cfg(unix)]
 use tokio::net::UnixStream;
 use tokio::time::timeout;
@@ -27,7 +29,8 @@ const TIMEOUT_S: u64 = 30;
 ///
 /// 200×200×16 future frames × 15 steps × ~1 byte/voxel = ~9.6 MB in the
 /// worst case; set a generous 64 MB ceiling to stay safe without allocating
-/// it up front.
+/// it up front. (Only used by the unix socket reader.)
 #[cfg(unix)]
 const MAX_RESPONSE_BYTES: usize = 64 * 1024 * 1024;
 /// Thin async client for the OccWorld Unix-socket inference server.
@@ -65,8 +68,23 @@ impl OccWorldBridge {
        .map_err(|_| WorldModelError::Timeout { timeout_s: TIMEOUT_S })?
    }
    /// Non-unix platforms have no Unix-domain sockets. The OccWorld bridge is a
    /// Linux-appliance feature (the Python inference server runs on the GPU host),
    /// so on Windows/other targets the crate still compiles but `predict` fails
    /// fast with a clear error instead of silently degrading.
    #[cfg(not(unix))]
    async fn send_recv(
        &self,
        _request: OccupancyWorldModelRequest,
    ) -> Result<OccupancyWorldModelResponse, WorldModelError> {
        Err(WorldModelError::Protocol(
            "OccWorld Unix-socket bridge is only supported on unix targets".into(),
        ))
    }
    /// Internal: connect, write request, read response — no timeout here;
    /// the outer [`timeout`] in [`predict`] handles that.
    #[cfg(unix)]
    async fn send_recv(
        &self,
        request: OccupancyWorldModelRequest,
@@ -129,6 +147,7 @@ impl OccWorldBridge {
    }
    /// Establishes a [`UnixStream`] connection to `self.socket_path`.
    #[cfg(unix)]
    async fn connect(&self) -> Result<UnixStream, WorldModelError> {
        UnixStream::connect(&self.socket_path)
            .await
@@ -161,6 +180,8 @@ mod tests {
    }
    /// Verify that a missing socket returns `SocketConnect` and not a panic.
    /// Unix-only: non-unix targets return a `Protocol` "unsupported" error instead.
    #[cfg(unix)]
    #[tokio::test]
    async fn connect_to_missing_socket_returns_error() {
        let bridge = OccWorldBridge::new("/tmp/__occworld_nonexistent_test__.sock");
		`@@ -0,0 +1 @@`
							`9c35e541d51f00998691b98948887ebca09b907d8eb29a113f97e792340456ba`
		`@@ -0,0 +1 @@`
							{"frames": [{"pred": [[0.4003, 0.2734], [0.5038, 0.4197], [0.2053, 0.4438], [0.4397, 0.685], [0.5796, 0.7645], [0.8001, 0.2195], [0.2789, 0.2833], [0.314, 0.5439], [0.511, 0.2259], [0.6008, 0.46], [0.4837, 0.3879], [0.3475, 0.5597], [0.6569, 0.3575], [0.437, 0.6539], [0.2341, 0.6038], [0.7331, 0.392], [0.5615, 0.4915]]}, {"pred": [[0.4669, 0.6066], [0.6012, 0.7873], [0.4124, 0.5997], [0.2832, 0.281], [0.2732, 0.3635], [0.2503, 0.4848], [0.6827, 0.715], [0.4336, 0.7165], [0.295, 0.3386], [0.5337, 0.3544], [0.4397, 0.5474], [0.5163, 0.5528], [0.7547, 0.6799], [0.4195, 0.4448], [0.2257, 0.2269], [0.384, 0.2176], [0.2419, 0.4332]]}, {"pred": [[0.5585, 0.283], [0.4325, 0.2934], [0.463, 0.4744], [0.4188, 0.3454], [0.215, 0.7565], [0.527, 0.2353], [0.7084, 0.6124], [0.3015, 0.6744], [0.4103, 0.3532], [0.7243, 0.6932], [0.3302, 0.4918], [0.2072, 0.3754], [0.7914, 0.4878], [0.7618, 0.4079], [0.323, 0.3386], [0.7104, 0.4997], [0.2673, 0.6077]]}, {"pred": [[0.6372, 0.4984], [0.4184, 0.6763], [0.4498, 0.7549], [0.2924, 0.303], [0.3069, 0.7022], [0.3954, 0.5098], [0.7836, 0.6071], [0.4733, 0.7114], [0.3407, 0.3793], [0.3408, 0.4678], [0.4156, 0.4911], [0.4525, 0.7519], [0.5117, 0.1985], [0.1893, 0.6784], [0.6281, 0.5346], [0.5175, 0.673], [0.36, 0.3665]]}, {"pred": [[0.5535, 0.6537], [0.568, 0.511], [0.4705, 0.5377], [0.6372, 0.7163], [0.5493, 0.7515], [0.2559, 0.4549], [0.2553, 0.6176], [0.2991, 0.6154], [0.7185, 0.7986], [0.4586, 0.5057], [0.2975, 0.4525], [0.3263, 0.3719], [0.5131, 0.4576], [0.557, 0.5268], [0.6572, 0.7736], [0.2146, 0.6526], [0.4662, 0.7371]]}, {"pred": [[0.2924, 0.7595], [0.2612, 0.2315], [0.2488, 0.7751], [0.2329, 0.7282], [0.4744, 0.4206], [0.3618, 0.267], [0.2477, 0.285], [0.3976, 0.3746], [0.494, 0.2874], [0.3596, 0.2112], [0.3311, 0.4692], [0.6912, 0.4727], [0.4434, 0.5233], [0.4139, 0.7048], [0.425, 0.3937], [0.2326, 0.631], [0.2655, 0.7116]]}, {"pred": [[0.3609, 0.3437], [0.285, 0.486], [0.7734, 0.5468], [0.3657, 0.4093], [0.4728, 0.5019], [0.1866, 0.3545], [0.2172, 0.2028], [0.5613, 0.5238], [0.6252, 0.7205], [0.7998, 0.2954], [0.242, 0.7063], [0.6259, 0.6883], [0.5148, 0.7141], [0.5577, 0.7434], [0.3233, 0.2131], [0.2652, 0.7066], [0.5753, 0.5885]]}, {"pred": [[0.6787, 0.6504], [0.6051, 0.2297], [0.2539, 0.3475], [0.6437, 0.7807], [0.4981, 0.6149], [0.5716, 0.2367], [0.6486, 0.3632], [0.2433, 0.369], [0.6061, 0.3731], [0.4955, 0.2591], [0.7676, 0.7602], [0.6899, 0.7716], [0.3143, 0.7707], [0.3031, 0.4997], [0.7076, 0.5133], [0.3382, 0.7196], [0.2002, 0.4871]]}]}
		`@@ -0,0 +1 @@`
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`@@ -1 +1 @@`
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