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Beyond-SOTA Validation, Test & Benchmark Methodology

Series: docs/research/ruview-beyond-sota/ · Document 03 Date: 2026-06-09 Scope: How RuView proves (and gates) beyond-SOTA claims using the verification infrastructure that already exists in this repository. Every number below is sourced from a cited file in this repo; nothing is invented.

1. The Layered Validation Pyramid

Six layers, cheapest/most-deterministic at the bottom, most expensive/most-credible at the top. A beyond-SOTA claim must survive every layer below it before it may be published from the layer it lives at.

Layer What it proves Tooling Frequency Determinism
L0 Unit/integration tests Code correctness cargo test --workspace --no-default-features + pytest per commit exact
L1 Deterministic proof + witness bundle Pipeline is real, unchanged, reproducible archive/v1/data/proof/verify.py, scripts/generate-witness-bundle.sh per merge / release exact (SHA-256)
L2 Criterion micro-benchmarks Compute latency only — never quality (ADR-149 §2) 15 bench targets across v2/crates/*/benches/ nightly / pre-release statistical
L3 Dataset-level accuracy eval Pose/presence/vitals quality vs published SOTA MM-Fi / Wi-Pose (ADR-015), ruview_metrics.rs tiers, ADR-145 ablation harness per model release seeded
L4 Hardware-in-loop Real CSI on real ESP32, no mocks COM9 (S3) / COM12 (C6) protocol, witness firmware hashes per firmware release A/B controlled
L5 Field trials / live capture End-to-end behavior in a real room live-session captures (e.g. benchmark_baseline.json) campaign statistical

1.1 L0 — Workspace tests (current counts)

  • ADR-028 audit (2026-03-01): 1,031 passed, 0 failed, 8 ignored for cargo test --workspace --no-default-features (docs/adr/ADR-028-esp32-capability-audit.md §2).
  • Current CHANGELOG.md (Unreleased, cross-platform fix entry): 2,682 workspace tests pass / 0 fail on Windows — the suite has more than doubled since the audit.
  • CLAUDE.md pre-merge gate still cites "1,031+ passed, 0 failed" as the floor.

Rule: the post-change test count may never be lower than the pre-change count, and failures must be 0. The witness bundle records the full log (test-results/rust-workspace-tests.log) and an aggregated summary.txt (scripts/generate-witness-bundle.sh step 3).

1.2 L1 — Deterministic proof ("Trust Kill Switch") + witness bundle

archive/v1/data/proof/verify.py (header comment): feeds 1,000 synthetic CSI frames (seed=42, sample_csi_data.json) through the production CSIProcessor (src/core/csi_processor.py), hashes the first 100 frames' feature output (VERIFICATION_FRAME_COUNT = 100), and compares against archive/v1/data/proof/expected_features.sha256.

  • Current published hash (file contents, verified during this investigation): f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a
  • The hash is environment-coupled and has been legitimately regenerated before: ADR-028 §5.3 recorded 8c0680d7… under numpy 2.4.2/scipy 1.17.1; CHANGELOG.md (#560 fix) recorded 667eb054… after 6-decimal quantization + single-thread BLAS pinning (OMP_NUM_THREADS=1 etc.). Each regeneration must follow the documented procedure: python verify.py --generate-hash then python verify.py → VERDICT: PASS.

scripts/generate-witness-bundle.sh packages: witness log + ADR-028, the Python proof (verify.py + expected hash + reference-signal metadata), full Rust test log + summary, the ADR-134 CIR proof, firmware source/binary SHA-256s, crate version manifest, npm tarball SHA-256, and a recipient-side VERIFY.sh.

Accuracy note on check counts: CLAUDE.md describes the recipient verification as "7/7 PASS"; the current VERIFY.sh embedded in the script performs 10 check() assertions (witness log, ADR, proof-hash file, tests, firmware hashes, crate manifest, npm manifest, Python proof, CIR proof, CIR hash file) but prints a hardcoded "ALL CHECKS PASSED (8/8)" string (generate-witness-bundle.sh line 293). The hardcoded count is stale relative to the actual check list — fix it to print ${PASS_COUNT}/${PASS_COUNT+FAIL_COUNT} so the verdict can never silently desynchronize from the check inventory.

1.3 L2 — Criterion micro-benchmark inventory (all 15 targets)

All bench sources read directly. Per ADR-149 §2 these are latency regression gates only, never quality evidence.

Bench target Crate Benchmark functions / groups What it measures Recorded value or in-source target (citation)
engine_cycle.rs wifi-densepose-engine process_cycle_4nodes_56sc One full StreamingEngine::process_cycle (fuse + quality + calibration provenance + privacy gate + WorldGraph node), 4-node/56-subcarrier ESP32-S3 HT20 mesh Budget: 50 ms (20 Hz) — bench header
signal_bench.rs wifi-densepose-signal CSI Preprocessing, Phase Sanitization, Feature Extraction, Motion Detection, Full Pipeline SOTA signal stages (ADR-014) at varying frame sizes no recorded baseline
cir_bench.rs wifi-densepose-signal cir_estimate (HT20/HT40/HE20/HE40), cir_estimate_12link, cir_estimator_new ADR-134 CirEstimator::estimate() per tier; 12-link multistatic amortization; cold-start no recorded baseline
calibration_bench.rs wifi-densepose-signal bench_recorder_record, bench_recorder_finalize, bench_deviation, bench_record_600, bench_to_bytes (K=52/114/242/484) ADR-135 empty-room baseline recorder + deviation scoring no recorded baseline
aether_prefilter_bench.rs wifi-densepose-signal aether_search_d…_n…_k… (search vs prefilter) ADR-084 Pass-2: EmbeddingHistory::search_prefilter vs brute force, prefilter_factor=8 Pass: ≥4× at n=1024 — bench header
sketch_bench.rs wifi-densepose-ruvector compare_d128/256/512 × float_l2/float_cosine/sketch_hamming ADR-084 sketch-vs-float per-pair compare cost (AETHER 128-d, spectrogram 256-d) Pass: sketch ≥8× faster at every dim (ADR-084 threshold 8×–30×) — bench header
crv_bench.rs wifi-densepose-ruvector gestalt_classify_single/batch_100, sensory_encode_single, pipeline_full_session, convergence_two_sessions, crv_session_create, crv_embedding_dimension_scaling (32/128/384), crv_stage_vi_partition CRV integration throughput no recorded baseline
inference_bench.rs wifi-densepose-nn tensor_ops (relu/sigmoid/tanh), densepose_inference, translator_inference, mock_inference, batch_inference NN forward-pass cost by input/batch size no recorded baseline; mock_inference group must never be quoted as a pipeline number (§6)
training_bench.rs wifi-densepose-train interp_114_to_56_batch32, interp_scaling, compute_interp_weights_114_56, synthetic_dataset_get, synthetic_epoch, config_validate, PCK over 100 samples Training preprocessing + metrics hot paths; fixtures fully deterministic (no rand) — header no recorded baseline
detection_bench.rs wifi-densepose-mat breathing_detection, heartbeat_detection, movement_classification, detection_pipeline, localization (triangulation/depth), alert generation MAT survivor-detection algorithms at varying signal lengths / noise no recorded baseline
transport_bench.rs wifi-densepose-hardware beacon_serialize_16byte/28byte_auth/quic_framed, auth_beacon_verify, replay_window, framed_message encode/decode, secure_tdm_cycle (manual vs QUIC) TDM beacon crypto + transport no recorded baseline
mqtt_throughput.rs wifi-densepose-sensing-server discovery::build_*, state::*, rate_limiter::allow_*, privacy::decide_*, semantic::bus_tick_all_10_primitives ADR-115 MQTT hot path Targets (header): discovery <5 µs, state encode <2 µs, rate limit <100 ns, privacy <50 ns, bus tick <10 µs
swarm_bench.rs ruview-swarm marl_actor_inference, rrt_apf_100iter, multiview_fusion_3drones, demo_coverage_estimate, ppo_update_64transitions ADR-148 swarm control-loop compute Measured: 3.3 µs / 43 µs / 54–58.5 ns / 100 ps / 248 µs (ADR-149 §4.3; CHANGELOG.md Performance section)
pipeline_throughput.rs nvsim pipeline_run (sample-count sweep), witness::run vs run_with_witness NV-diamond sim throughput + witness overhead Acceptance: ≥1 kHz simulated samples/s on Cortex-A53-class CPU — bench header
state_machine.rs homecore set first/warm/no-op, get hit/miss, all_snapshot, all_by_domain_light_20_of_100, broadcast_fan_out HOMECORE state-machine hot paths no recorded baseline

Honest gap — benchmark_baseline.json is not a criterion baseline. The repo-root benchmark_baseline.json (369.9 KB) contains 1,566 live-capture samples from a 2-node session (fields: tick, n_nodes, variance, motion, presence, confidence, est_persons, n_persons_rendered, kp_spread, rssi) plus a summary block — it records field-trial telemetry (L5), not micro-benchmark latencies. No file in the repo references it (grep -rn benchmark_baseline → 0 hits outside the file itself); its producer must be identified and committed (§5.3). Summary values (all from the file's summary object):

Metric Baseline value
total_frames 1,566
presence_ratio 0.9336 (1,462/1,566 frames presence-true)
confidence_mean 0.6433
variance_mean / variance_std 109.36 / 154.13
kp_spread_mean / kp_spread_std 86.73 / 4.52
person_count_changes 10

Criterion latencies that have been recorded live in ADR documents instead (ADR-147-benchmark-proof.md, ADR-149 §4.3, CHANGELOG Performance) — §5 below defines how to consolidate them into a real machine-readable criterion baseline.

1.4 L3 — Dataset-level accuracy evaluation

  • Datasets (ADR-015): primary MM-Fi (40 subjects × 27 actions × ~320K frames, 1TX×3RX, 114 subcarriers @100 Hz, 17-keypoint COCO + DensePose UV, CC BY-NC 4.0); secondary Wi-Pose (12 volunteers × 12 actions × 166,600 packets, 3×3, 30 subcarriers). 114→56 subcarrier interpolation via subcarrier.rs; validation split = subjects 33–40 held out (ADR-015 Phase 1).
  • Acceptance tiers: wifi-densepose-train/src/ruview_metrics.rs — PCK@0.2 / OKS / MOTA / vitals rolled into RuViewTier (Fail/Bronze/Silver/Gold) (ADR-145 §1.1).
  • Ablation harness (ADR-145): 6-variant matrix (csi_only, cir_only, csi_plus_cir, plus_doppler, plus_bfld, plus_uwb-skipped), each variant producing acceptance tier + SpecMetrics (presence ≥0.90, localization ≤0.50 m, activity ≥0.70, FP ≤0.05, FN ≤0.10), LatencyProfile (p95 ≤100 ms), and PrivacyLeakage (MIA leakage_score ≤0.05), SHA-256-pinned per variant under PROOF_SEED=42 (ADR-145 §2.2–2.6). Built at commit 0f336b7d3 (ADR-145 implementation status); CLI auto-mode wiring is pending.
  • Cross-environment: ADR-027 MERIDIAN CrossDomainEvaluator (wifi-densepose-train/src/eval.rs) — domain_gap_ratio, extended by ADR-145 cross_room_degradation() with a 17-joint PCK-delta heatmap.

1.5 L4 — Hardware-in-loop

  • Real CSI nodes: ESP32-S3 on COM9, ESP32-C6 + MR60BHA2 on COM12 (CLAUDE.md hardware table). ADR-018 binary frame protocol over UDP:5005 (ADR-028 §3.2/§3.4).
  • ADR-145 Tier-4 test (gated, #[cfg(feature = "hardware-test")]): replay a live 30 s COM9 capture through csi_only and csi_plus_cir; assert no presence regression and p95 < 100 ms.
  • A/B board protocol precedent (CHANGELOG.md #987): fixed vs unmodified control board against Apple-Watch ground truth (control pegged 40–49 BPM; fixed 88–91 vs 87 GT) — this fixed-board/control-board + external ground-truth pattern is the required design for all hardware vital-sign claims.
  • Witness bundle pins firmware: per-file SHA-256 of all sources + release binaries (generate-witness-bundle.sh step 5).

1.6 L5 — Field trials

Live multi-node sessions captured as JSONL/JSON with summary statistics — benchmark_baseline.json (§1.3) is the existing exemplar. ADR-149 §6 adds the seeded evals/ episode harness (Stage 1 kinematic full-matrix, Stage 2 Gazebo/PX4 SITL on the 3 median seeds) for the swarm domain.

2. Beyond-SOTA Acceptance Criteria per Capability Axis

A claim is "beyond SOTA" only with: a named external baseline, an exact metric and protocol match, the dataset/split named, the threshold pre-registered, and the statistical procedure of §3 followed. Current axes with measured status:

Axis Metric (exact) Dataset / protocol SOTA baseline Beyond-SOTA threshold Measured status (cited)
In-domain pose accuracy torso-PCK@20: ‖pred−gt‖ ≤ 0.2·‖R-shoulder−L-hip‖ MM-Fi random_split (ratio 0.8, seed 0) MultiFormer 72.25% (Table VII); CSI2Pose 68.41% > 72.25% with 95% CI lower bound above it Flagship 83.59%; micro (75,237 params) 74.30% (docs/benchmarks/wifi-pose-efficiency-frontier.md)
Edge efficiency frontier torso-PCK@20 at deployed precision + params + batch-1 latency same MultiFormer 72.25% at full size Pareto-dominance: smaller and above 72.25% at the deployed precision int8 73.5 KB 74.70%; int4-QAT 36.7 KB 74.46%; shipped int4 verified 74.08%, 0.135 ms 1-thread x86 (same file)
Cross-subject generalization torso-PCK@20, official MM-Fi cross-subject split (256,608 train / 64,152 test) leakage-free split own zero-shot baseline 63.99% ADR-150 §4 gate: +≥6 pts cross-subject without losing >2 pts random-split Best zero-shot 64.92% (mixup+TTA+3-seed); gate judged unreachable without new capture (ADR-150 §3.2)
Few-shot calibration (deployment) PCK@20 after K labeled in-room samples; adapter size MM-Fi cross-subject & cross-environment splits zero-shot (64% / 10.6%) SOTA-level (≳72%) from ≤200 samples with ≤~11 KB per-room adapter cross-subject ~72% @100–200 samples (3 seeds); cross-env 10.6→73.1% @200, 60.1% @5 (ADR-150 §3.5–3.6)
Swarm SAR localization CEP50/CEP95 (m), GDOP-stratified seeded episode distribution (ADR-149 §6), not single geometry Wi2SAR 5 m (arxiv 2604.09115, paper-to-paper) CEP50 < 5 m, IQM over ≥10 seeds, 95% CI excluding 5 m 1.732 m single synthetic geometry — graded Low–Medium, not yet claimable (ADR-149 §7)
Swarm coverage coverage-rate@240 s; time-to-95% episode rollouts Wi2SAR 160k m²/13.5 min rollout (not analytic) mean+CI beating baseline 223 s is an analytic estimate — graded Low (ADR-149 §7)
Control-loop latency criterion wall-clock local hardware, named 10 ms / 100 Hz budget all stages ≪ budget 3.3 µs MARL / 43 µs RRT-APF / 54 ns fusion / 248 µs PPO (ADR-149 §4.3)
World-model trajectory MDE (m) at 5-frame horizon RuView CSI-derived occupancy pre-fine-tune random-weight baseline 9.49 m MDE ≤1.0 m (2.0 vox) at 5-frame horizon (ADR-147 §5 target, cited in benchmark-proof §4) 9.49 m / FDE 16.23 m random weights; 208.45 ms median latency on real CSI (ADR-147-benchmark-proof §4, §7)
Privacy leakage MIA leakage_score = 2·(AUC−0.5) fixed replay, fixed-seed shadow classifier chance (0) ≤ 0.05 (attacker AUC ≤ 0.525) gate defined, harness built (ADR-145 §2.3)
Vitals (hardware) BPM error vs wearable ground truth live A/B board protocol control board behavior within physiological agreement of ground truth, stable spread 88–91 BPM vs 87 GT, spread 59→0 (CHANGELOG #987)

Claim-language discipline (from ADR-149 §7 grading)

Evidence Permitted language
Single run / single geometry / analytic estimate "directional", never "beats SOTA"
Seeded multi-run with CIs vs paper baseline "exceeds the published X result paper-to-paper"
Same metric, same split, same protocol, CI excludes baseline "beyond SOTA on /"
No public leaderboard exists (swarm CSI-SAR) never claim "leaderboard standing" (ADR-149 §3)

3. Statistical Procedure for Honest Claims

Adopted from ADR-149 §5 (Agarwal 2021 / Gorsane 2022 standard) and the practices already used in ADR-150/efficiency-frontier measurements:

  1. Seeds. ≥10 independent seeds for RL/episodic claims (ADR-149 §5); ≥3 seeds minimum for supervised dataset evals (ADR-150 §3.5 used 3 seeds; report all). Training seeds, eval seeds, and split files are versioned and committed.
  2. Aggregate. IQM (not mean/median) for episodic metrics + performance profiles; for dataset accuracy report mean across seeds with each seed's value listed.
  3. Confidence intervals. 95% stratified bootstrap, 1,000 resamples (ADR-149 §5; reference impl: rliable).
  4. Paired comparisons. When comparing model A vs B (e.g. csi_plus_cir vs csi_only, or ours vs a reproduced baseline), evaluate both on the identical frozen test frames and use a paired bootstrap over per-sample correctness (PCK hit/miss is per-joint binary — pair at the joint-sample level). For paper-to-paper comparisons where the baseline cannot be re-run, state so explicitly ("paper-to-paper", ADR-149 §2) and require the CI lower bound to clear the published point value.
  5. Pre-registration. The threshold lives in an ADR before the run (precedent: ADR-150 §4 gate written before §3.2 measurements; the measurements honestly reported the gate as not met).
  6. Negative results are recorded. ADR-150 §1/§3.2 keeps DANN-failed, capacity-hurts, and KD-didn't-help results in the record — required practice.
  7. Eval episodes (swarm): 50 fixed, versioned episodes per policy (10 victim layouts × 5 CSI-noise levels), ≥3 baselines (random walk, boustrophedon+triangulation, IPPO) (ADR-149 §5).
  8. GDOP stratification for any localization claim, so geometry artifacts cannot produce the headline (ADR-149 §6.3).

4. Regression-Gate Design (CI Enforcement)

4.1 Three gate classes, three tolerances

Gate class Source of truth Tolerance On breach
Determinism hashes expected_features.sha256, expected_cir_features.sha256, expected_calibration_features.sha256, future expected_ablation_<slug>.sha256 exact (0%) exit 1 = FAIL; exit 2 = SKIP only for placeholder hashes (proof.rs 0/1/2 convention, ADR-145 §2.4)
Accuracy / quality metrics per-variant canonical bytes, quantized 1e-3 (ADR-145 §2.6) exact after quantization FAIL CI; tier change requires ADR amendment
Latency / throughput criterion estimates JSON % tolerance per scale (below) FAIL on regression beyond tolerance; trend everything

4.2 Criterion baseline file (replaces the current gap)

Today criterion numbers live in prose (ADR-147-benchmark-proof, ADR-149 §4.3, CHANGELOG). Formalize:

  1. cargo bench --workspace -- --save-baseline main on a named, fixed runner (ADR-147 used RTX 5080 / specific host; record host + toolchain in the file).
  2. Export target/criterion/*/estimates.json point estimates into a committed v2/benchmarks/criterion-baseline.json: {bench_id, crate, p50_ns, host, commit}.
  3. CI compares new runs against it with scale-aware tolerance — wall-clock noise is proportionally larger at small magnitudes:
Magnitude Tolerance Rationale
< 1 µs (e.g. fusion 54 ns, privacy decide <50 ns target) ±25% timer/jitter dominated
1 µs – 1 ms (MARL 3.3 µs, RRT-APF 43 µs, PPO 248 µs) ±15% criterion CI typically <5%, leave CI-runner headroom
> 1 ms (engine cycle vs 50 ms budget, OccWorld ~209 ms) ±10% and absolute budget (50 ms / 500 ms ADR-147 §6) budgets are the contract
  1. Hard in-source acceptance thresholds remain authoritative regardless of baseline: sketch ≥8× (sketch_bench.rs), prefilter ≥4× (aether_prefilter_bench.rs), nvsim ≥1 kHz (pipeline_throughput.rs), MQTT header targets, ADR-145 p95 ≤100 ms.
  2. Latency stays out of determinism hashes (ADR-145 §2.6) but in the trended summary.json, so sub-threshold drift is visible (ADR-145 §3.2 mitigation).

4.3 Live-capture baseline gate (benchmark_baseline.json)

Adopt the file as the L5 regression anchor with documented provenance, then gate a re-capture of the same scenario (same 2-node placement, same room class) against the summary block:

Field Baseline Suggested gate
presence_ratio 0.9336 ≥ 0.90 for an occupied-room session
confidence_mean 0.6433 within ±0.10
kp_spread_std 4.52 ≤ 2× baseline (skeleton stability)
person_count_changes 10 / 1,566 frames ≤ 2× baseline (count flapping — see CHANGELOG #803/#894 clamp bugs this metric would have caught)

Field-trial gates are soft (warn + require human sign-off), never auto-merge blockers — environments differ; the gate exists to force an explanation.

4.4 Wiring

Pre-merge (CLAUDE.md checklist): L0 + L1. Nightly: L2 criterion + ADR-145 Tier-3 ablation matrix (minutes-scale, ADR-145 §3.2). Release: full witness bundle + VERIFY.sh + L4 on real COM-port hardware (CLAUDE.md firmware rule 6/7).

5. Reproducibility & External-Witness Requirements

Anyone outside the project must be able to re-run every claimed result:

  1. One command per layer. cargo test --workspace --no-default-features; python archive/v1/data/proof/verify.py; bash scripts/generate-witness-bundle.sh then bash VERIFY.sh inside the bundle; per ADR-150 §4 every accuracy result needs "one-command reproduction" (efficiency frontier publishes its exact command: python aether-arena/staging/train_efficiency_pareto.py npy/X.npy npy/Y.npy npy/split_random.npy).
  2. Pinned numerical environment. The Python proof requires single-threaded BLAS (OMP_NUM_THREADS=1, OPENBLAS_NUM_THREADS=1, MKL_NUM_THREADS=1, VECLIB_MAXIMUM_THREADS=1, NUMEXPR_NUM_THREADS=1) and 6-decimal quantization (HASH_QUANTIZATION_DECIMALS=6) — the #560 fix in CHANGELOG.md; Rust proof runners use coarse u16 quantization at 1e-3 in natural order (calibration_proof_runner.rs pattern, ADR-145 §2.6) for libm portability.
  3. Seeds are constants, committed: PROOF_SEED=42, MODEL_SEED=0 (proof.rs, ADR-015 Phase 5); dataset splits committed as .npy (split_random.npy); swarm configs as versioned YAML with all seeds (ADR-149 §5).
  4. Artifacts carry hashes. Published model artifacts include SHA-256 (HuggingFace pose_micro_int4.npz, sha256 c03eeb… — efficiency-frontier doc); witness bundle has a MANIFEST.sha256 over every file; provenance fields (replay_sha256, model_sha256, calibration_version, privacy_mode) are bound into ablation proof hashes (ADR-145 §2.7) so a metric cannot be quoted without its exact model + calibration + privacy decision.
  5. Hardware claims name the hardware. ADR-147 records RTX 5080 / CUDA 12.8 / PyTorch 2.10.0; nvsim states the Cortex-A53 scaling caveat in the bench header; efficiency-frontier flags ARM validation as pending. Copy this discipline.
  6. Witness rows. Every new proof gains rows in docs/WITNESS-LOG-028.md (ADR-145 §5.3 adds W-39…W-41) and the bundle's source-hashes.txt.
  7. Secret hygiene in evidence. Bundle logs pass through scripts/redact-secrets.py (ADR-110 wave-5 incident note in generate-witness-bundle.sh step 4) — external evidence must never embed .env.

6. Known Measurement Pitfalls (WiFi-sensing specific)

# Pitfall Repo evidence Mitigation in this methodology
1 Subject leakage / split optimism. In-domain random_split has temporal/subject-adjacency effects; the same model family scores 83.6% random-split but ~11.6% torso-PCK on the leakage-free cross-subject split efficiency-frontier "Controlled claim" footnote; ADR-150 §1, §3.2 Always report the split name; publish random-split and cross-subject numbers side by side; cross-subject claims only on the official split
2 Per-environment overfitting. Zero-shot cross-environment collapses to 10.6%; subject-scaling saturates ~63.7% past 16–20 subjects because the residual is room/device shift ADR-150 §3.3, §3.6 Cross-room degradation + 17-joint heatmap in every ablation (ADR-145 §2.5); claim deployment accuracy only with the calibration protocol stated (K samples, adapter size)
3 Mock-mode contamination. Mock firmware missed a real Kconfig threshold bug; the nn crate ships a mock_inference criterion group that must never be quoted as pipeline performance CLAUDE.md firmware rule 7; inference_bench.rs bench_mock_inference L4 mandatory before firmware release ("Always test with real WiFi CSI, not mock mode"); label mock benches in reports; ADR-147 §7 re-ran the benchmark on real CSI explicitly "no mocks"
4 Single-run point estimates. 1.732 m localization from one synthetic geometry; 223 s coverage from an analytic formula ADR-149 §1, §7 §3 seed/CI protocol; evidence-grade table before publication
5 Random-weight / untrained baselines read as results. OccWorld MDE 9.49 m is a pre-fine-tuning random-weight reading ADR-147-benchmark-proof §4 Label baseline-vs-target explicitly; never aggregate untrained-model numbers into capability claims
6 Latency conflated with quality. Criterion µs numbers prove no compute bottleneck, nothing about accuracy ADR-149 §2, §4.3 L2 is gate-only; quality claims live in L3+
7 Floating-point nondeterminism breaking proofs. SciPy FFT SIMD reordering + multithreaded BLAS produced different hashes across CI microarchitectures CHANGELOG #560; calibration_proof_runner.rs lines 1–13 (cited in ADR-145 §2.3) Quantize before hashing; pin thread env vars; exclude wall-clock from hashes
8 Hash churn without procedure. Three distinct historical values of the proof hash exist (8c0680d7… ADR-028, 667eb054… CHANGELOG #560, f8e76f21… current file) cited files Every regeneration via --generate-hash + re-verify + CHANGELOG entry + witness bundle refresh
9 Aggregation bugs masking accuracy. Person count clamped to 1 by EMA mapping; eigenvalue path leaking counts up to 10; both invisible to unit tests for months CHANGELOG #803, #894 L5 summary gates on person_count_changes/count distributions; convergence tests replaying the live loop
10 Stale verification claims. VERIFY.sh prints hardcoded "(8/8)" over 10 actual checks; CLAUDE.md says "7/7" generate-witness-bundle.sh line 293; CLAUDE.md Compute the verdict count; audit doc claims against scripts each release
11 Licensing limits on the eval set. MM-Fi is CC BY-NC — weights trained solely on it cannot back commercial claims ADR-015 Consequences Track dataset license alongside every published number

7. Gap List (what must be built to fully execute this methodology)

Gap Owner layer Source
Machine-readable criterion baseline (v2/benchmarks/criterion-baseline.json) + CI comparison job L2 §4.2 (numbers currently only in ADR prose)
Provenance + producer script for benchmark_baseline.json; soft-gate job L5 §1.3, §4.3 (zero code references today)
ruview-cli --ablation mode=auto wiring + expected_ablation_<slug>.sha256 (currently placeholders → exit 2) L3 ADR-145 implementation status
Seeded swarm evals/ harness + evals/RESULTS.md internal leaderboard L3/L5 ADR-149 §6, §8 open issues
Fix VERIFY.sh hardcoded verdict count; reconcile CLAUDE.md "7/7" L1 §1.2
Curated paired room-A/room-B labeled replay set (frozen, SHA-pinned, never trained on) L3 ADR-145 §3.2
ARM/edge on-device latency validation for the int4 model (x86-only today) L4 efficiency-frontier doc ("Pi fleet pending")
Bench validation of the antenna-placement matrix on real hardware L4 PRODUCTION-ROADMAP.md Tier 2.3

Update — falsifiable occupancy benchmark implemented

wifi-densepose-train::occupancy_bench (added this branch) makes the presence/person-count claim falsifiable in code, directly enforcing the L3 discipline above. It grades predictions vs ground truth and gates a SOTA claim behind a single claim_allowed invariant that requires all of:

  1. DataProvenance::Measured — synthetic/mock data is scorable for regression but never claimable (anti-mock-contamination; the CLAUDE.md Kconfig-bug lesson made structural).
  2. A leak-free EvalSplit — validate() refuses any split where a subject or environment id appears in both train and test (subject leakage / per-env overfitting).
  3. n_test ≥ min_test_samples (small-N guard).
  4. Presence F1 whose bootstrap-CI lower bound (deterministic splitmix64, seeded) clears the threshold — not the point estimate.
  5. Count MAE within threshold.

The claim string is unreadable except through the gate (returns NO_CLAIM otherwise) — same discipline as the ruview-gamma acceptance gate. 10 tests cover each refusal path. What remains is data, not method: feed it a frozen, SHA-pinned, subject/environment-disjoint measured replay set (the curated room-A/room-B item above) and the "beyond SOTA" claim becomes a passing or failing test, not a slogan.

All values cited from: benchmark_baseline.json, v2/crates/*/benches/*.rs (15 files), docs/adr/ADR-147-benchmark-proof.md, docs/adr/ADR-149-swarm-benchmarking-evaluation-methodology.md, docs/adr/ADR-145-ablation-eval-harness-privacy-leakage.md, docs/adr/ADR-028-esp32-capability-audit.md, docs/adr/ADR-015-public-dataset-training-strategy.md, docs/adr/ADR-150-rf-foundation-encoder.md, docs/benchmarks/wifi-pose-efficiency-frontier.md, scripts/generate-witness-bundle.sh, archive/v1/data/proof/verify.py, archive/v1/data/proof/expected_features.sha256, CHANGELOG.md, CLAUDE.md, docs/research/sota-2026-05-22/PRODUCTION-ROADMAP.md.