Researchers have developed a novel LLM-based framework for holistic athlete profiling, designed to overcome the limitations of traditional manual or basic computer vision assessment methods. This agentic system, orchestrated by LangGraph, integrates computer vision for kinematic tracking with Vision-Language Models for semantic reasoning, specifically aligning with the Sports Authority of India's protocols. To manage computational demands and prevent hallucinations, the framework employs a temporal chunking strategy, an LLM-as-a-Judge self-correction loop, and a dual-persistence RAG pipeline using ChromaDB for natural language querying. AI
IMPACT This framework could significantly improve the objectivity and scalability of athlete talent identification and performance analysis.
RANK_REASON The cluster contains a research paper detailing a novel AI framework.
Read on arXiv cs.MA (Multiagent) →
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