Incentivizing Vision Language Models to Search for Long Video Question Answering
We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-turn retrieval process. VSeek utilizes a natural language-driven search to identify relevant context within long videos and is post-trained with reinforcement learning (RL) to jointly formulate targeted search queries and reason over retrieved clips for LVQA. While RL post-training has revolutionized reasoning in symbolic domains such as mathematics and code, its application to long-video understanding remains hindered by a lack of verified rewards. To ensure that the retrieved context is relevant, we propose a novel neuro-symbolic approach...
| Authors | Harsh Goel, S P Sharan, Sahil Shah et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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