arXiv:2606.09646v1 Announce Type: cross Abstract: We study whether pretrained video foundation models encode intuitive-physics information in their frozen representations, and how this information varies across model families, layers, and probe types. Using frozen-feature probing on IntPhys2 and Minimal Video Pairs (MVP), we compare predictive joint-embedding models (V-JEPA), masked reconstruction models (VideoMAE), and a diffusion-based video generator (LTX-Video). V-JEPA achieves the strongest overall results across benchmarks, especially with probes that model temporal dynamics, while Video
The proliferation of advanced video foundation models necessitates deeper understanding of their capabilities, specifically intuitive physics, for developing more robust and generalizable AI.
Understanding how video foundation models encode intuitive physics is crucial for developing AI agents that can interact with the physical world effectively and reliably.
The ability to quantitatively assess and compare video foundation models' understanding of intuitive physics pushes the field closer to developing truly intelligent and context-aware AI systems.
- · AI researchers
- · Robotics companies
- · Generative AI developers
- · Video analytics platforms
- · AI models lacking intuitive physics understanding
- · Companies relying on brittle, rules-based AI for physical tasks
Improved performance of AI agents in tasks requiring physical reasoning and interaction within virtual and real environments.
Accelerated development of general-purpose humanoid robots and autonomous systems capable of complex manipulation and navigation.
Enhanced AI capabilities leading to new forms of content generation, industrial automation, and scientific discovery.
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Read at arXiv cs.LG