# 微软研究揭示AI助手在长文档编辑中普遍损坏内容

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-04-30 17:14
- AIHOT 分数：73
- AIHOT 链接：https://aihot.virxact.com/items/cmolal3ei01ksslc5qqz0bpkz
- 原文链接：https://x.com/rohanpaul_ai/status/2049779289592139929

## AI 摘要

微软最新论文指出，当前AI助手在执行长链条编辑任务时，普遍会损坏文档内容。研究通过可逆任务对测试了19个模型，发现即使是前沿模型平均也会破坏约25%的文档内容，且问题随文件增大、流程变长而加剧。失败模式通常不是微小失误，而是偶尔出现的重大错误，这些错误会静默破坏部分文档并随时间累积。研究表明，当前的LLM在简短演示或狭窄编码任务中可能表现良好，但作为现实世界长文档工作的委托代理仍不可靠。

## 正文

New Microsoft paper shows that current AI assistants often damage documents during long editing jobs.

Even the frontier models still ended up corrupting about 25% of document content on average， while many other models damaged far more.

The problem is that delegated AI work only makes sense if a model can keep a document correct across many edits， not just do 1 step well.

The paper tests this with reversible task pairs， where a model edits a file and then tries to undo that edit， so a reliable system should return to the original document.

The authors built real work setups across 52 domains， from coding and science to accounting and music notation， and ran 19 models through 20 editing interactions.

The failures were usually not lots of tiny slips but occasional big mistakes that silently broke parts of the document and then compounded over time.

Agentic tool use did not help in their tests， and bigger files， longer workflows， and irrelevant extra documents made the corruption worse.

The reason this matters is that current LLMs can look strong in short demos or narrow coding tasks yet still be unreliable delegates for long real-world document work.

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Paper Link - arxiv. org/abs/2604.15597

Paper Title： "LLMs Corrupt Your Documents When You Delegate"