AI RESEARCH
Clue Matters: Leveraging Latent Visual Clues to Empower Video Reasoning
arXiv CS.CV
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ArXi:2603.15008v1 Announce Type: new Multi-modal Large Language Models (MLLMs) have significantly advanced video reasoning, yet Video Question Answering (VideoQA) remains challenging due to its demand for temporal causal reasoning and evidence-grounded answer generation. Prevailing end-to-end MLLM frameworks lack explicit structured reasoning between visual perception and answer derivation, causing severe hallucinations and poor interpretability.