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World-Model Simulation Planning(WMSP)
The agent maintains, or uses the LLM itself as, a world model that predicts the next state and consequence of each candidate action, then runs look-ahead rollouts ("imagine before acting") to score and select an action before executing anything in the real environment. This extends model-based reinforcement learning to language agents and matters most where actions are irreversible and backtracking on a live website or GUI is impossible. Distinct from scenario-planning, which reasons across several strategic futures with no action-consequence simulator, and from reflective-mcts, which searches over reasoning traces rather than simulated environment states.
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World-Model Simulation Planning(WMSP)
The agent maintains, or uses the LLM itself as, a world model that predicts the next state and consequence of each candidate action, then runs look-ahead rollouts ("imagine before acting") to score and select an action before executing anything in the real environment. This extends model-based reinforcement learning to language agents and matters most where actions are irreversible and backtracking on a live website or GUI is impossible. Distinct from scenario-planning, which reasons across several strategic futures with no action-consequence simulator, and from reflective-mcts, which searches over reasoning traces rather than simulated environment states.
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