Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making
Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties supervision to what a transition happens to reveal, which may omit the dynamics most relevant to the agent's current decision. To bridge this gap, we propose Agent-Authored World Modeling (AAWM), a training procedure that constructs supervision from the policy's own decision needs. Specifically, at each state, the agent identifies what it needs to understand about the environment before acting. These needs drive the retrieval of relevant transition evidence across trajectories, which is then synthesized into training...
| Authors | Guangfeng Cai, Kaibing Yang, Shuo He et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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