AI RESEARCH
SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition
arXiv CS.CV
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ArXi:2511.22433v3 Announce Type: replace Recent advances in skeleton-based action recognition increasingly leverage semantic priors from Large Language Models (LLMs) to enrich skeletal representations. However, the LLM is typically queried in isolation from the recognition model and receives no performance feedback. As a result, it often fails to deliver the targeted discriminative cues critical to distinguish similar actions.