Continual World Models

Continual World Models is a one-day NeurIPS 2026 workshop focused on the next step beyond offline generalization: world models that update, adapt, and improve after initial training. Current video, multimodal, and embodied models learn rich priors from static datasets, but they are still typically evaluated as frozen predictors. Physical intelligence requires systems that notice prediction failures, incorporate new observations and feedback, maintain memory, and revise their understanding of objects, scenes, agents, and dynamics over time. The workshop will bring together researchers from computer vision, robotics, reinforcement learning, generative modeling, multimodal learning, and cognitive science. Through invited talks, contributed papers, posters, breakout discussions, and a panel, the workshop will define key problems for continual world modeling, including adaptation mechanisms, memory and representation design, embodied data acquisition, evaluation protocols, and the distinction between genuine model revision and superficial test-time heuristics. Our goal is to build a cross-community research agenda for adaptive world models that keep learning from the worlds they observe, imagine, and act within.