VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization
- Published
- Source
- arXiv
- Paper number
- 295
- Field
- Computer Vision
- arXiv ID
- 2606.02564
Key points
- State-of-the-art VGM delivers strong visual quality, but it often struggles to understand and follow task-specific rules, which leads to logical failures across diverse reasoning scenarios.
- VLMs are difficult as solvers, but they have strong perceptual abilities that allow them to evaluate whether process constraints are satisfied and whether the final goal is achieved.
- These results show that integrating VLMs as test-time teachers offers a promising paradigm for achieving generalizable video reasoning.
Paper links
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