Toward Causal Foundation World Models: From Representation to Decision-Making

Abstract My research lies at the intersection of causality, reinforcement learning, world models, and multi-agent systems. I aim to develop causal foundation world models that enable agents to interpret the past, reason about the future, and act reliably in dynamic, non-stationary, and open-ended environments. My work spans causal representation learning (e.g., CausalVAE), causal reasoning in large language models, and causality-driven exploration in open-ended worlds. These contributions have appeared in leading venues such as NeurIPS, ICML, ICLR, CVPR, and KDD, and have been recognized through over 770 citations and the Rising Star in AI award (2024). Looking forward, my agenda focuses on scalable, trustworthy causal world models for healthcare, robotics, scientific discovery, and digital systems.