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Research questionHow can language agents adapt textual world models to evolving behavior in interactive environments without external rewards?A textual world model trained on earlier behavior can become inaccurate as an agent explores new state-action patterns. In realistic interactive environments, improving the agent is also difficult when external rewards or verifiers are unavailable.
AI
AI Agents
Evaluation & Benchmarks
Machine Learning
Reasoning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.CoMAP: Co-Evolving World Models and Agent Policies for LLM AgentsApplies to LLM agents performing embodied task planning, web navigation, and tool use with textual world models. The source reports benchmark evidence of improved world-model prediction accuracy and long-horizon decision-making, but does not establish broader deployment behavior.research paper · Sep 3, 2026
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