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This work examines both model behavior and internal representations, using the empirical neural tangent kernel (NTK) as the primary diagnostic tool, and develops practical guidance for designing DR schemes, selecting models, and detecting shortcut learning.
DiagGen is presented, an agentic framework that turns a single in-the-wild image into a simulation-ready deformable asset through a generate--simulate--diagnose--refine loop, and shows that diagnostics provides useful repair cues and can moderately improve the quality of generated deformable assets.
Adapting robots to new objects and tasks requires interaction experience that can be costly to obtain. We present WorldContact, a contact-centric world model for deformable-object manipulation, constructed from a limited set of high-quality trajectories to generate additional training data efficiently. It predicts obje...
It is indicated that formalizing tacit social norms into explicit, quantifiable principles can enable AI agents to achieve mutually beneficial coordination in dynamic interactions, supporting their more natural integration into human society.
Agentic Real2Sim is introduced, a framework for generalized physical world modeling with vision-language agents, converting a real-world recording of object-robot interaction into a simulatable episodic twin which preserves observations, geometries, robot interactions, and object states.
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