World Model
An agent's internal representation of the state of its environment—including objects, relationships, and expected consequences of actions—that enables it to reason about hypothetical futures without executing them.
A world model allows an agent to mentally simulate action outcomes before committing to them. In classical robotics and game-playing AI (AlphaZero, MuZero), world models are explicit learned simulators. In LLM-based agents, the world model is implicit—encoded in the model's weights as learned associations between actions and their likely consequences.
The quality of an LLM's implicit world model depends heavily on training data coverage. Models have strong world models for well-documented domains (software engineering, writing, mathematics) and weaker models for physical processes, niche domains, or events after the training cutoff. Grounding via tool calls partially compensates by importing ground-truth state at query time.
Ongoing research aims to build explicit world models alongside LLMs—separate modules that track environment state and constraint violations—to enable more reliable long-horizon planning. Systems like OpenAI's o-series models and Anthropic's extended thinking suggest that additional inference-time computation can improve effective world-model quality without separate architecture changes.