Archived
2026-09-24Agent-Editing World Model: Rethinking World Modeling for LLM Agents
Shuang Sun, Guoxin Chen, Fanzhe Meng, Jia Deng, Huatong Song, Jinhao Jiang, et al.
arXiv:2609.28416v1Large language models are getting better at using tools, but they still make a familiar kind of mistake: they carry stale assumptions forward and keep acting as if an old plan is still true. This paper tackles that problem with a new world model for agents, called the Agent-Editing World Model. Instead of trying to predict every tool response in detail, it learns which actions matter, which ones are just noise, and how to revise the agent’s internal state after each step. The key idea is to edit the reasoning trace itself, so the model can remove contamination and keep the task state aligned with reality. That matters because many long-horizon failures in agents come from bad memory, not bad intent. In experiments across search, terminal use, and software engineering, the approach improves performance over strong baselines. For anyone building reliable AI assistants, this is a practical step toward agents that stay grounded as tasks get longer and messier.
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