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feat(aether-arena): publish RuView MM-Fi SOTA result + ADR-150 RF Foundation Encoder
- Ledger witness row (seq 1, Gold): RuView CSI-Transformer 81.63% torso-PCK@20 on MM-Fi random_split, exceeding MultiFormer 72.25% (CSI2Pose 68.41%) — protocol- and metric-matched, self-corrected from inflated 91.86% bbox. Hash-chained, verifiable. - HF Space updated with the controlled SOTA claim + caveat (cross-subject is the frontier). - Proof/replay/witness gist: gist.github.com/ruvnet/af2fbc1c7674dddf09c15509b3c7f785 - Tracking issue #876 (result + Generalization Track roadmap). - ADR-150: RuView RF Foundation Encoder — pose-preserving, subject/room/device-invariant SSL embedding (masked CSI + pose-contrast-across-subjects + coherence head); the principled attack on the cross-subject frontier. DANN failed; this is the corrected design. Co-Authored-By: claude-flow <ruv@ruv.net>
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# ADR-150: RuView RF Foundation Encoder — pose-preserving, subject/room/device-invariant CSI embedding
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| Field | Value |
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|-------|-------|
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| **Status** | Proposed |
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| **Date** | 2026-05-30 |
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| **Deciders** | ruv |
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| **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) |
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| **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) |
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---
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## 1. Context
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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.
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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.
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**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.
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### 1.1 Why DANN failed (and the corrected rule)
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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:
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> **Remove subject identity only after preserving pose geometry.** Supervised *pose-contrast across subjects* beats naïve adversarial identity removal.
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The frontier objective is **not** `same-subject = positive`. It is:
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> **same pose across different subjects = positive; different pose = negative.**
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## 2. Decision
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**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.
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### 2.1 What the embedding must keep / remove
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| Signal | Action | Why |
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|--------|--------|-----|
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| Pose geometry | **Keep** | target signal |
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| Limb-motion deltas | **Keep** | strong temporal cue |
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| Subject identity | **Remove** (post-pose) | causes overfit |
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| Static room multipath | **Remove** | breaks transfer |
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| Device-specific phase artifacts | **Remove** | breaks cross-hardware |
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| Antenna-layout quirks | **Normalize** | deployment portability |
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| Channel instability | **Expose separately** | confidence gating / anti-hallucination |
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### 2.2 Architecture
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```
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CSI frame sequence
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→ physics normalization (antenna geometry, subcarrier stability, phase-unwrap quality, room-impulse structure)
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→ masked CSI encoder (SSL: learn channel structure from unlabeled CSI — 150k home + 320k MM-Fi frames)
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→ temporal contrastive encoder (motion continuity)
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→ skeleton-aware pose decoder (graph head — anatomical constraints, GraphPose-Fi style, arXiv 2511.19105)
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→ confidence + coherence head (mincut / spectral coherence as RF-integrity signal)
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```
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### 2.3 Training objectives (loss stack)
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```
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L_total = L_pose
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+ 0.20 · L_masked_csi # learn channel structure (unlabeled)
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+ 0.10 · L_temporal_contrast # motion continuity
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+ 0.20 · L_pose_contrast # same-pose-across-subjects = positive ← the frontier
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+ 0.05 · L_subject_decorrelation # remove identity only where it conflicts with pose
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+ 0.10 · L_coherence # predict when RF evidence is weak
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```
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Invariant target:
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```
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embedding ≈ pose + motion + channel-coherence
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embedding ≠ subject-identity + static-room-signature + device-artifact
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```
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### 2.4 The RuView differentiator — auditable RF perception that knows when it's wrong
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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.)
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## 3. Experiment plan — three variants, frozen-decoder test
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Same split, same decoder, same seed set; only the embedding changes.
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| Variant | Description | Success threshold (cross-subject torso-PCK) |
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|---------|-------------|----------------------------------------------|
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| **E1** | Masked CSI pretrain | **+3** |
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| **E2** | Pose-contrastive across subjects | **+6** |
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| **E3** | Physics-normalized SSL + skeleton head | **+10** |
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### 3.1 Expected gains (estimate)
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| Method | cross-subject torso-PCK gain |
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|--------|------------------------------|
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| Naïve embedding | 0–2 |
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| DANN adversarial | 0–3 (high collapse risk) — *empirically ~0* |
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| Masked CSI pretrain | +3–8 |
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| Pose-contrastive | +5–12 |
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| Physics-norm + SSL + graph decoder | +10–20 |
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| + more subject-diverse paired data | +20 |
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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%.
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## 4. Acceptance Test
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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.
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## 5. Consequences
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**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.
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**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.
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**Neutral:** the in-domain head is unchanged; the encoder slots in front of the existing pose decoder.
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## 6. Alternatives Considered
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1. **Bigger model only** — tested; *hurts* cross-subject (overfits seen subjects).
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2. **Naïve DANN subject-adversarial** — tested; no gain, collapse risk; entangles pose evidence.
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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.
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## 7. Open Questions
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1. Physics-normalization spec — exact antenna/subcarrier/phase terms, validated to preserve pose deltas.
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2. Masked-CSI SSL on the production feature pipeline (resampling match — see ADR-149).
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3. Where the coherence/mincut integrity signal is computed (reuse ADR-135 coherence gate vs new head).
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4. CIR (ADR-134) / BFLD fusion into the same encoder — phase 3.
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