MLLMs drop from over 85% accuracy on action presence to under 50% on matched action-denial videos, exposing a causal verification gap that causal graph prompts partially close.
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A port-Hamiltonian Gaussian Process model integrates Cosserat rod theory and Hamiltonian structure with data-driven inference to learn energy-consistent dynamics of planar soft robots.
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Learning to Deny: Action Denial in Multimodal Large Language Models
MLLMs drop from over 85% accuracy on action presence to under 50% on matched action-denial videos, exposing a causal verification gap that causal graph prompts partially close.
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Learning-Based Modeling of Soft Robots via Cosserat Rod Theory
A port-Hamiltonian Gaussian Process model integrates Cosserat rod theory and Hamiltonian structure with data-driven inference to learn energy-consistent dynamics of planar soft robots.