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Existing protein geometry models typically represent molecular surfaces using local geometric features such as sampled points, normals, and curvature. While effective for capturing exposed molecular shape, these representations do not explicitly model the volumetric organization beneath the surface or provide a consist...
This work presents WorldAgent, an agentic framework for verification-guided physical world construction from a single natural-language prompt, without iterative user debugging, and introduces AgenticSimBench, on which WorldAgent achieves the best scores among the evaluated agent-based methods on five of seven metrics.
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...
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.
This work validates NeuralActuator on a 5-DoF OpenManipulator-X, a 6-DoF SO-101 from LeRobot, and a 7-DoF Franka Emika Panda, spanning three actuator families and costs from approximately \$500 to more than \$30{,}000.
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