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>
This commit is contained in:
ruv
parent
4f7ab8e4f0
commit
046b2564b8
|
|
@ -1 +1,2 @@
|
|||
{"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"}
|
||||
|
|
|
|||
|
|
@ -41,14 +41,15 @@ def verify_chain():
|
|||
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 —", "be the first", "", "", ""]]
|
||||
results.sort(key=lambda r: r.get("pck20_all") or r.get("pck_all") or 0, reverse=True)
|
||||
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", "?"),
|
||||
r.get("tier", "?"),
|
||||
f"{(r.get('pck20_all') or r.get('pck_all') or 0):.4f}",
|
||||
(r.get("proof_sha256") or "")[:16],
|
||||
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]
|
||||
|
||||
|
||||
|
|
@ -107,16 +108,21 @@ rerun the scorer locally → understand why the rank is fair. That is the launch
|
|||
with gr.Blocks(title="AetherArena — Spatial-Intelligence Benchmark") as demo:
|
||||
gr.Markdown("# 📡 AetherArena (AA)\n## The Official Spatial-Intelligence Benchmark")
|
||||
gr.Markdown(FOUR_PART)
|
||||
gr.Markdown(
|
||||
"## 🏆 RuView sets new MM-Fi random-split SOTA for WiFi-CSI pose estimation — **81.63% torso-PCK@20**\n"
|
||||
"**81.63% vs MultiFormer 72.25%** (CSI2Pose 68.41%) — same MM-Fi `random_split` (0.8, seed 0), same torso-normalized PCK@20, 17 COCO keypoints. **+9.38 abs / +13.0% rel.**\n\n"
|
||||
"> ⚠️ **Controlled claim.** This is a *protocol-matched MM-Fi random-split* result — **not** solved real-world generalization. Random split contains temporal/subject-adjacency effects common to this benchmark family. Our leakage-free **cross-subject** result is far lower (~11–27%), and we treat cross-subject pose estimation as the real deployment frontier."
|
||||
)
|
||||
chain = gr.Markdown(verify_chain())
|
||||
|
||||
with gr.Tab("🏆 Leaderboard"):
|
||||
cat = gr.Dropdown(["all", "pose", "presence"], value="all", label="Category")
|
||||
tbl = gr.Dataframe(
|
||||
headers=["Submitter", "Model", "Tier", "Score", "Proof (sha256…)"],
|
||||
headers=["Submitter", "Model", "Benchmark / Protocol", "Metric", "Score", "Tier (vs SOTA)"],
|
||||
value=leaderboard("all"), interactive=False, wrap=True,
|
||||
)
|
||||
cat.change(leaderboard, cat, tbl)
|
||||
gr.Markdown("*Benchmark-first: the board starts empty. Every row is a real harness witness — no seeded numbers.*")
|
||||
gr.Markdown("*Benchmark-first: every row is a real, metric- and protocol-matched result — no seeded numbers. Integrity note: the headline 81.63% was self-corrected down from an inflated 91.86% (bbox metric) before publishing.*")
|
||||
|
||||
with gr.Tab("📤 Submit"):
|
||||
gr.Markdown(SUBMIT)
|
||||
|
|
|
|||
|
|
@ -1 +1,2 @@
|
|||
{"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"}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,125 @@
|
|||
# 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%.
|
||||
|
||||
## 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.
|
||||
Loading…
Reference in New Issue