RuView exceeds MultiFormer on MM-Fi WiFi-CSI pose: 81.63% torso-PCK@20 (random split) + Generalization Track

Result — controlled, protocol- & metric-matched claim

RuView's CSI-Transformer reaches 81.63% torso-PCK@20 on MM-Fi random_split, exceeding MultiFormer (72.25%) and CSI2Pose (68.41%) on the same protocol and metric. Absolute +9.38, relative +13.0%.

Match conditions (verified)

• Protocol: MM-Fi default random_split (ratio 0.8, seed 0) — from MM-Fi config.yaml.
• Metric: torso-PCK@20 (‖pred−gt‖ / ‖right_shoulder−left_hip‖ ≤ 0.2, 2D, 17 COCO kpts) — MultiFormer Table VII.
• Data: MM-Fi WiFi-CSI, 320,760 frames [3,114,10].
• Integrity: headline self-corrected from an inflated 91.86% (bbox metric) → 81.63% (torso) before publishing.

Proof / Replay / Witness

• Detailed gist (proof + replay + parser + trainer): https://gist.github.com/ruvnet/af2fbc1c7674dddf09c15509b3c7f785
• Witness: AetherArena append-only hash-chained ledger, row seq 1, row_hash 76598d8e…. Verify: python aether-arena/ledger/ledger_tools.py verify.
• Leaderboard (live): https://huggingface.co/spaces/ruvnet/aether-arena
• One-command replay: download MM-Fi → parse_mmfi_zips.py → train_tf_torso.py X Y split_random.npy (seed 0) → ~81.6%.

⚠️ Controlled claim (what this is NOT)

Protocol-matched random-split result — not solved real-world generalization. Random split has temporal/subject-adjacency effects common to this benchmark family. Our leakage-free cross-subject result is far lower (~11.6% torso) and is the real deployment frontier. Not a universal WiFi-pose SOTA claim (e.g. WiFlow's 97% is a separate 5-subject self-collected set).

Next: the RuView Generalization Track (two frontiers)

Frontier 1 — Benchmark (push the in-domain number, honestly): target 85%+ random-split torso-PCK; levers: skeleton-graph head (anatomical constraints, GraphPose-Fi style), temporal-consistency loss, multi-task action+pose, careful CSI augmentation, conv+transformer ensemble. Acceptance: beat 85% one seed, 5-seed mean ≥ 84%, per-joint error tables.

Frontier 2 — Deployment (the real hard problem): lift cross-subject torso-PCK from 11.6% → 25–30%+; levers: self-supervised CSI pretraining (masked/contrastive, phase-aware), supervised-contrastive subject-invariant-but-pose-preserving embedding (naive DANN already failed), physics-normalized CSI features, leave-one-subject-group-out validation.

The RuView differentiator — auditable RF perception that knows when it's wrong: gate pose confidence by channel coherence (mincut / spectral coherence as RF-integrity signals) → anti-hallucination for RF sensing.

Track targets

Next public milestone acceptance: 85% random + 25%+ cross-subject torso-PCK from one pipeline, one-command repro, per-joint tables.

🤖 Generated with claude-flow

Start by reading the linked detailed gist, then inspect the replay entry points parse_mmfi_zips.py, train_tf_torso.py, and split_random.npy. Use the ledger verification command and existing one-command replay to establish the baseline. Done means one pipeline reaches 85%+ random-split and 25%+ cross-subject torso-PCK with per-joint tables.