Induction and Adaptation in Natural Language

A general AI agent cannot anticipate every environment it will encounter and must therefore continually adapt its understanding of the world based on observations made at deployment. We investigate the benefits of representing and compressing such beliefs in natural language, which, paired with a pre-trained language model, forms a highly flexible world model that can map states and actions to predicted next states. Unlike the scalar signals that drive traditional neural world models, natural language is interpretable, editable, and portable. We treat learning such a world model as inference over a natural-language representation of the world, performed by searching directly in this space. To guide this search, we use a language model as a proposal that generates hypotheses from a strong prior, grades them against observations, and refines them in response to prediction errors. We first evaluate how well our method induces the underlying transition function across a range of induction tasks, where it substantially outperforms in-context learning baselines on next-state prediction. We then measure how these induced world models can support downstream interactive decision-making on ARC-AGI-3, a benchmark that requires agents to explore complex environments and infer their dynamics to solve novel tasks, and find that they serve as effective representations that drive strong downstream task performance.