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LLMs Corrupt Your Documents When You Delegate

arxiv.org

May 9, 2026

2 min read

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61/100

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.

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Community Sentiment

Mixed

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.