LLMs Corrupt Your Documents When You Delegate
arxiv.org
May 9, 2026
2 min read
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67/100
Summary
Large Language Models (LLMs) can introduce errors into documents when tasks are delegated, raising concerns about trust in their execution. DELEGATE-52 is introduced to study the impact of LLMs on document integrity during delegated work.
Key Takeaways
- Current large language models (LLMs) corrupt an average of 25% of document content during long delegated workflows across 52 professional domains.
- The degradation of documents by LLMs is exacerbated by factors such as document size, length of interaction, and the presence of distractor files.
- Even advanced models like Gemini 3.1 Pro, Claude 4.6 Opus, and GPT 5.4 demonstrate significant reliability issues as delegates, introducing sparse but severe errors that silently corrupt documents.
- Agentic tool use does not improve the performance of LLMs in delegated workflows according to the DELEGATE-52 study.
Community Sentiment
Positives
- LLMs excel at compiling sparse knowledge into coherent outputs, making them valuable for organizing facts and findings in a structured manner.
- Using LLMs for independent markdown file creation allows for better searchability and organization of information, enhancing the overall research process.
Concerns
- Each pass through an LLM can degrade the original intent of a document, leading to a loss of nuance and precision, which is particularly concerning for scientific writing.
- The methodology of testing LLMs for tool usage lacks sophistication, raising doubts about the reliability of their findings regarding content degradation.
- Frequent users of LLMs are already aware that round-tripping long content often results in corruption, indicating a significant limitation in their usability.
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