benchmark: AetherArena

kind: genesis

note: Official Spatial-Intelligence Benchmark — append-only signed ledger. Entries are real harness scores only; no seeded numbers.


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.

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)

protocol: random_split (ratio=0.8, seed=0)

reproduce: download MM-Fi -> parse_mmfi_zips.py -> train_tf_torso.py X.npy Y.npy split_random.npy (seed 0)

sota_ref: MultiFormer 72.25 (CSI2Pose 68.41)

submitter: ruvnet

tier: Gold


model_ref: RuView CSI-Transformer + skeleton-graph head + 3-ensemble + TTA

note: Best in-domain. Stacks attention-pooling + transformer + skeleton-graph refine + warmup + TTA + 3-model ensemble. Supersedes the 81.63 single-model entry.

protocol: random_split (0.8, seed 0)


model_ref: RuView CSI-Transformer

note: Leakage-free generalization to unseen people, shared rooms. Honest deployment-relevant number.

sota_ref: (no matched public ref)

tier: Silver


model_ref: RuView CSI-Transformer + CORAL domain alignment

note: The real deployment frontier (new room). CORAL transductive DG (+30% rel over control). Data-bound: MM-Fi has only 3 source rooms.

protocol: cross_environment (train E01-03 -> test E04, new room)

sota_ref: (hard frontier; control 13.52)

tier: Bronze