AI 干活时总被自己旧计划带偏，这篇直接改它的记忆
大模型当智能体干活时，最大的坑不是不会做，而是被自己之前写下的旧计划、旧假设带偏——环境已经变了，它还在按老剧本演。现有做法是让模型预测环境下一步会怎样，但真实反馈就在眼前，预测工具输出意义不大。这篇换了个思路：不预测环境，直接判断当前动作是关键的、探索性的还是噪音，然后把噪音动作和它引发的错误推理从历史里改掉，让后续决策基于修正后的状态，而不是带着脏数据继续走。在搜索、终端、软件工程三类任务上，这套方法比最强基线平均高 3.2 到 6.7 分。它不是你明天能装进自己工具链的东西，但它指出了一个更本质的方向：与其让 AI 更会猜，不如让它更会清理自己脑子里的垃圾。
📄 原文摘要(英文)
Recent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent performance, existing language world models typically predict environment observations, yet reconstructing high-entropy, execution-dependent tool responses offers limited value when real feedback is available. Meanwhile, agents suffer from task-state contamination, where unsupported assumptions and outdated plans persist in history and distort subsequent decisions. We propose the Agent-Editing World Model (AEWM), which models how reasoning and actions shape future task progress rather than simulating tool responses. AEWM combines Action Judge to distinguish Critical, Exploratory, and Noisy decisions with State Revision to edit noisy reasoning--action continuations from the same observed history. EditAct integrates these capabilities with real execution, directly changing the state underlying subsequent decisions rather than merely providing critiques. We train AEWM across Search, Terminal, and Software Engineering through mid-training and supervised fine-tuning. AEWM achieves 70.5\% macro-F1 on our Action Judge benchmark, exceeding the strongest frontier baseline by 10.6 points. Across six benchmarks and three agent backbones, EditAct improves average scores by 3.2--6.7 points over the strongest baseline. Furthermore, rejection sampling fine-tuning on verified EditAct trajectories, termed AEWM-RFT, improves over Self-RFT by 2.2--2.6 points across three domains without online AEWM guidance.