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

- 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>

docs/user-guide.md

@@ -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held-out temporal-triplet accuracy** (the older "100% presence" figure was measured on a single-class recording and has been retracted) and ~164k embeddings/sec on an Apple M4 Pro.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