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Agent-Editing World Model revises noisy reasoning and action histories

huggingface.co by Shuang Sun et al. Editor score 8/10

AEWM classifies decisions as critical, exploratory, or noisy, then edits noisy continuations against the same observed history; its Action Judge reaches 70.5% macro-F1. The approach targets persistent stale assumptions in long-horizon agent tasks.

What

- Agent-Editing World Model (AEWM) models how agent reasoning and actions shape task progress rather than simulating tool responses.

Method

- Action Judge classifies decisions as Critical, Exploratory, or Noisy; State Revision edits noisy continuations, and EditAct integrates both with real execution.

Why it matters

- Editing the state behind later decisions targets task-state contamination from unsupported assumptions and outdated plans in long-horizon agents.

Read on huggingface.co → Code · 6 stars

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