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docs: results+proof links, capabilities-proof rebuttal, fix stale claims

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- README: replace retracted "100% presence" claim with honest 82.3%
  held-out temporal-triplet; correct stale "pose model not in this
  release" (now live at ruvnet/wifi-densepose-mmfi-pose, 82.69%
  torso-PCK@20 SOTA); add a Results & proof table (HF models,
  AetherArena, benchmark study, deterministic verify.py proof, witness).
- user-guide: same 100%->82.3% correction in two places; add Results &
  proof pointers and the SOTA pose model + AetherArena links.
- docs/proof-of-capabilities.md (new): evidence-first rebuttal to the
  "fake / misleading" claims. Concedes what was fair (over-stated early
  metrics, AI-doc tone), refutes the category errors (simulate-mode
  mistaken for fraud; missing weights mistaken for missing pipeline),
  and gives copy-paste "prove it yourself" steps (verify.py VERDICT:
  PASS + published SHA-256, cargo test, HF model pull, ESP32 CSI).
  Emphasizes built-in-public history (git, 96 ADRs, CHANGELOG, issues
  incl. #803/#872 bug->fix arcs) as the anti-facade evidence.
- aether-arena/VERIFY.md: cross-link the whole-platform proof doc.

Verified: python archive/v1/data/proof/verify.py -> VERDICT: PASS
(hash ca58956c...9199 matches published expected_features.sha256).

Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-05-31 10:29:28 -04:00
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README.md
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@@ -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