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Large foundation models have accelerated progress toward general-purpose agents that interact with humans and other agents through language and multimodal signals. However, robust multi-agent decision-making requires reasoning about what other agents know, intend, and are likely to do under partial observability. Curre...
In an expert study with five Blender artists, materials generated by reflecting expert demonstrations were found to produce workflows requiring fewer edits, and more closely match professional design strategies than methods operating solely on static artifacts.
This work proposes RoboTALES, a single-stage framework that learns task-aligned simulated futures and uses them to train robot policies and introduces two key innovations: a hierarchical LLM-based planner that breaks complex tasks into a sequence of subgoals to guide the model's imagination and a VLM-based critic that...
This work introduces Latent Dynamics Reasoning (LDR), the first video world model that extrapolates learned dynamics beyond its training distribution and can even generalize under severe shift.
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