The authors introduce the Agent-Editing World Model (AEWM), a language-based world model for LLM agents that focuses on editing the agent's own reasoning state instead of simulating tool outputs.
Editing agent reasoning history boosts long-horizon task performance
The Agent-Editing World Model (AEWM) classifies actions as critical, exploratory, or noisy and revises the reasoning state, achieving 70.5% macro-F1 on action classification and improving benchmarks by 3–7 points.
Industry
Shuang Sun · Guoxin Chen · Fanzhe Meng · Jia Deng · Huatong Song · Jinhao Jiang · +3 more
Gaoling School of Artificial Intelligence, Renmin University of China
Research Digest··2 min read
The authors propose AEWM, a world model that edits agent reasoning and action histories to remove noisy or outdated state, rather than predicting environment observations.
Why this paper
From Gaoling School of Artificial Intelligence, Renmin University of China · Part of World Model Planning for Agents, now 19 papers
In one line
Agent-Editing World Model improves long-horizon agent performance by editing noisy decisions to fix task-state contamination.
What we could check
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