Chixiang Ma

Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning

The video reasoning ability of multimodal large language models (MLLMs) is crucial for downstream tasks like video question answering and temporal grounding. While recent approaches have explored text-based chain-of-thought (CoT) reasoning for MLLMs, these methods often suffer from limited cross-mod