Microsoft Research: LLMs Corrupt your files during delegated work
A recent study reveals that large language models (LLMs) can corrupt documents during delegated tasks. The research, conducted using a framework called DELEGATE-52, found that even advanced models can degrade document content by an average of 25%. This degradation is influenced by factors such as document size and interaction length, highlighting the unreliability of current LLMs in delegated workflows.
- ▪LLMs are increasingly used in delegated work, which requires trust in their accuracy.
- ▪The DELEGATE-52 study involved 19 LLMs and simulated workflows across 52 professional domains.
- ▪Current LLMs corrupt an average of 25% of document content during long workflows.
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| Original publisher | Microsoft Research |
| Canonical URL | https://www.microsoft.com/en-us/research/publication/llms-corrupt-your-documents-when-you-delegate/ |
| Publication time | Tue, 26 May 2026 22:21:10 +0000 |
| Retrieval time | 2026-05-26T22:37:54.885Z |
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LLMs Corrupt Your Documents When You Delegate Philippe Laban , Tobias Schnabel , Jennifer Neville April 2026 arXiv Download BibTex Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding). Delegation requires trust – the expectation that the LLM will faithfully execute the task without introducing errors into documents. We introduce DELEGATE-52 to study the readiness of AI systems in delegated workflows. DELEGATE-52 simulates long delegated workflows that require in-depth document editing across 52 professional domains, such as coding, crystallography, and music notation.
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