PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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CARE-ECG unifies ECG representation learning, causal graph-based diagnosis, and counterfactual assessment in an agentic LLM pipeline to improve accuracy and explanation faithfulness.
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Physically Interpretable World Models via Weakly Supervised Representation Learning
PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation
CARE-ECG unifies ECG representation learning, causal graph-based diagnosis, and counterfactual assessment in an agentic LLM pipeline to improve accuracy and explanation faithfulness.