arXiv AI
Ask the World Before Acting: Environment Probing for Calibrating Agent World Models
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arXiv AI
Jul 7
Ask the World Before Acting: Environment Probing for Calibrated Agent World Models
arXiv:2606. 31422v2 Announce Type: replace Abstract: Language agents acting over long horizons must maintain beliefs about tool states, object locations, graph edges, and subgoal dependencies.
arXiv AI
Jul 1
Ask the World Before Acting: Budgeted Environment Probing for World-Model Calibration
arXiv:2606. 31422v1 Announce Type: new Abstract: Long-horizon language agents do not only choose actions; they carry a private model of the world from one decision to the next.
arXiv AI
Jun 2
COMAP: Co-Evolving World Models and Agent Policies for LLM Agents
arXiv:2606. 02372v1 Announce Type: new Abstract: Equipping language agents with world models enables them to anticipate environment dynamics and evaluate candidate actions before execution.
arXiv AI
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CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents
CoMAP introduces a framework that jointly evolves textual world models and agent policies through a closed‑loop interaction. At each decision step the world model forecasts future state feedback for candidate actions, while the agent reflects on the reliability of this feedback to refine its action. The resulting on‑policy trajectories are used to self‑distill and update the world model, improving prediction accuracy and long‑horizon decision‑making across embodied planning, web navigation, and tool‑use benchmarks.
arXiv AI
Sep 24
Agent-Editing World Model: Rethinking World Modeling for LLM Agents
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
arXiv AI
Sep 2
Towards a Belief-Based World Model for LLM Agents
arXiv:2609.00455v1 Announce Type: new Abstract: Large language models (LLMs) are being used as policies for autonomous decision-making and planning in many domains. Despite their strong reasoning cap...