Agent-Editing World Model: Rethinking World Modeling for LLM Agents

TL;DR - Agent-Editing World Model (AEWM) improves long-horizon LLM agents by revising contaminated reasoning and action histories instead of predicting complex tool responses. Its EditAct framework consistently boosts agent performance across search, terminal, and software-engineering tasks.

• AEWM classifies decisions as Critical, Exploratory, or Noisy, then edits noisy reasoning-action continuations before subsequent decisions.
• Its Action Judge achieves 70.5% macro-F1, 10.6 points above the strongest frontier baseline.
• Across six benchmarks and three agent backbones, EditAct improves average scores by 3.2–6.7 points over the strongest baseline.
• Training on verified EditAct trajectories improves results by 2.2–2.6 points over Self-RFT without requiring online AEWM guidance.