AI RESEARCH
4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding
arXiv CS.CV
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ArXi:2605.05997v1 Announce Type: new Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vision-language models (VLMs). Prior approaches either verbalize spatial-temporal reasoning entirely as text, which is inherently verbose and imprecise for complex dynamics, or rely on external geometric modules that increase inference complexity without fostering intrinsic model capability.