Thinking in Frames: How Visual Context and Test-Time Scaling Empower Video Reasoning
🤖 AI Summary
This work proposes a novel video-generation-based paradigm for visual reasoning, addressing the limited capability of existing vision-language models in fine-grained spatial understanding and sequential action planning—key requirements for complex visual reasoning. By treating generated intermediate video frames as explicit reasoning steps from an initial state to a target solution, the method enables zero-shot reasoning on tasks such as maze navigation and tangram puzzle solving. Through explicit control of visual context and dynamic adjustment of generation length at test time, the approach achieves strong out-of-distribution generalization without fine-tuning, while maintaining high visual consistency and significantly improving performance on complex path-planning challenges.
Application Category
📝 Abstract
Vision-Language Models have excelled at textual reasoning, but they often struggle with fine-grained spatial understanding and continuous action planning, failing to simulate the dynamics required for complex visual reasoning. In this work, we formulate visual reasoning by means of video generation models, positing that generated frames can act as intermediate reasoning steps between initial states and solutions. We evaluate their capacity in two distinct regimes: Maze Navigation for sequential discrete planning with low visual change and Tangram Puzzle for continuous manipulation with high visual change. Our experiments reveal three critical insights: (1) Robust Zero-Shot Generalization: In both tasks, the model demonstrates strong performance on unseen data distributions without specific finetuning. (2) Visual Context: The model effectively uses visual context as explicit control, such as agent icons and tangram shapes, enabling it to maintain high visual consistency and adapt its planning capability robustly to unseen patterns. (3) Visual Test-Time Scaling: We observe a test-time scaling law in sequential planning; increasing the generated video length (visual inference budget) empowers better zero-shot generalization to spatially and temporally complex paths. These findings suggest that video generation is not merely a media tool, but a scalable, generalizable paradigm for visual reasoning.
Problem
Research questions and friction points this paper is trying to address.
visual reasoning
video generation
spatial understanding
action planning
vision-language models
Innovation
Methods, ideas, or system contributions that make the work stand out.
video generation
visual reasoning
test-time scaling
zero-shot generalization
visual context
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