AI RESEARCH
GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking
arXiv CS.CV
•
ArXi:2602.17555v3 Announce Type: replace Video reasoning requires a fine-grained understanding of the temporal dependencies and event-level relations between objects and events in videos. Current Multimodal Large Language Models (MLLMs) are prone to severe temporal hallucinations in video reasoning. An underlying cause of these hallucinations is weak visual-temporal grounding and the lack of explicit structure for modelling event relations. Models often rely on auxiliary text, such as dense captions, rather than explicitly anchoring their reasoning in actual visual evidence.