# Corruption in the delegation
We are entering a recent era of AI where interaction turns into… work delegation. Users don’t just talk to AI that answers their questions: they increasingly delegate long-term tasks, from editing source code to formatting professional text and even managing accounting records. That’s why they trust AI systems at an unprecedented level to maintain the integrity of files like documents across multiple interactions.
However, a recent study revealed a problem. By delegating tasks to large language model (LLM)may silently damage documents given to him. To understand this issue, scientists from this studywhose findings we summarize, created a exacting evaluation framework called “DELEGATE-52”. This benchmark covers 52 professional domains: from legal text to Python coding, music notation, and crystallography.
The authors tested a total of 19 different LLMs using an smart simulation method based on a round-trip approach, asking the AI to perform a specific edit and then the exact opposite undo instruction. In an ideal scenario, the model would deliver the original document unchanged – completely intact. Reality check: even the smartest models such as Gemini Pro, Claude Opus and GPT-5 are capable of corrupting 25% of the original document content after 20 interactions; weaker models can get closer to 50%.
# Why models ruin your documents
Let’s analyze some reasons why the previously explained phenomenon of structural content loss may occur. Scientists have discovered several reasons for this:
// 1. Error relationship
Like the conventional “telephone game,” compact mistakes made by LLM can quietly accumulate and become insidiously significant. A single edit may add a few sporadic, localized errors, but a sequence of elaborate edits can create a cascade of problems in the long run, causing the quality of the document to deteriorate dramatically over time.
// 2. Feeble models delete, smart models hallucinate
In testhighlighted a striking change in the way different types of models fail. Weaker models tend to drop: accidentally dropping content, which makes the problem become noticeable after a few interactions due to the obvious reduction in the overall content of the document. However, in borderline LLMs, the main problem is not deletion but corruption: they retain the overall “look and feel” of the documents, even keeping the word count almost intact, but silently mistype, modify, or replace factual information with fabrications that still sound credible. Here’s the irony: the smarter the model, the harder it is to detect its destructive behavior, because the final result still looks plausible at first glance.
// 3. Context overload and distracting attachments
In a disordered state—with a lot of contextual information or a lot of attached documents—models have difficulty keeping the information structurally intact. As the document size increases or more “scattering files” are included in the tooltip context, the severity and impact of degradation increases dramatically, losing control of the exact details and filling in the gaps based on predictive logic. The model no longer sticks to the source text, it is easier for it to simply guess.
// 4. The importance of domain knowledge
The final reason why models tend to degrade document quality in elaborate interactions involving delegation has to do with the nature of the utilize case and the model’s knowledge of it.
Not all files degrade to the same extent in delegation-based tasks. According to the study, LLMs perform well in highly structured programming domains such as Python source code. It’s when they’re forced to perform purely natural language tasks or niche spatial formatting that they quickly lose the tight sense of internal logic needed to keep files intact.
# Does agentic AI assist?
Even if LLMs are modernized by equipping them with agent-based tools—such as the ability to execute code or directly read and write files—the problem of document corruption and destruction as a result of delegation persists. In fact, agent add-ons do little or nothing to prevent the problem that exists in the core transformer architecture underlying LLM. There is a need to rethink how long-term AI tasks should be verified. Until then, using LLMs as completely unsupervised document editors remains risky.
Ivan Palomares Carrascosa is a thought leader, writer, speaker and advisor in the fields of Artificial Intelligence, Machine Learning, Deep Learning and LLM. Trains and advises others on the utilize of artificial intelligence in the real world.