arXiv AI By Xinyuan Zhang, Handong Mo, Pengfei Wen, Shuang Liang, Jichang Yang, Yan Zeng, Zhongrui Wang, Han Wang

Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decoding

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arXiv AI
Aug 24

Aggregate, Don't Adapt: Subject-Level Posterior Aggregation and Transductive Calibration for Cross-Site Parkinsonian Gait Severity

The paper reports the winning solution to the MoCha 2026 Parkinsonian Gait Benchmark, achieving a macro‑F1 score of 0.6945 on unseen clinical sites. The approach relies on a frozen public motion encoder followed by a single 4×512 linear layer, and gains are largely attributed to three key steps: exact replication of the benchmark’s head recipe, averaging per‑walk posteriors at the subject level, and a label‑free transductive calibration of feature means and decision thresholds. Extensive ablation studies show that fine‑tuning the encoder or using alternative encoders does not improve performance, and the subject‑level aggregation is identified as the primary contributor to the top score.

By Junlong Shen