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The paper evaluates six onboarding strategies for federated wearable models on five datasets using a leakage‑controlled protocol that fixes source checkpoints and separates calibration from evaluation. Results show that while average accuracy is high, person‑level performance can drop significantly, with some methods causing negative transfer for certain users. The study highlights that mean accuracy alone is insufficient and provides an auditable benchmark and failure map for future development.
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.
arXiv:2607.12176v2 Announce Type: replace Abstract: Ambivalence and hesitancy (A/H) undermine digital behaviour-change interventions, and recognizing them automatically from video is the goal of the...
arXiv:2606.15992v2 Announce Type: replace Abstract: Analyzing tennis technique from monocular RGB video could make coaching more accessible, but prior methods usually rely on hand-trimmed strokes, te...
arXiv:2609.11987v1 Announce Type: cross Abstract: An agentic coding system couples a language model to a harness: the tools, prompts and control flow that turn a chat model into an autonomous softwar...
The paper introduces VideoRouteAudit, a controlled experiment that isolates the effects of frame selection, evidence injection, and expert routing in long‑video question answering. On a large Video‑MME benchmark, no frame selector outperforms uniform sampling, and the gains from evidence injection or model swapping are largely attributable to the specific model or data ordering rather than the selection strategy itself. The study also corrects earlier results, showing that ASR transcripts can either help or hurt performance depending on placement, and that swapping the answering model yields the largest performance contrast.