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CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents
Equipping language agents with world models enables them to anticipate environment dynamics and evaluate candidate actions before execution. However, existing textual world models are typically fixed after training, preventing them from adapting to the on-policy state-action distributions induced by
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