Microsoft Research: LLMs corrupt documents through iterative delegation — 19–34 % fidelity degradation over 20 iterations

The Microsoft Research team of Philippe Laban, Tobias Schnabel and Jennifer Neville published on May 15, 2026 the blog post “Further Notes on Our Recent Research on AI Delegation and Long-Horizon Reliability” — a follow-up to their original paper that dramatically signaled a serious reliability gap in contemporary agentic systems.

What did the original paper reveal?

The original work “LLMs Corrupt Your Documents When You Delegate” demonstrated that iterative delegation of document editing corrupts content through successive AI iterations. The team measured a fidelity score — how well the quality, accuracy and coherence of a document is preserved across delegation cycles — and found that models systematically lose information through iterations, even when each individual iteration looks reasonable.

What exact numbers does the paper provide?

Across 20 delegation iterations, fidelity degradation reaches 19–34 % depending on task type and the specific model. The figure is significant because it converts the problem from “sometimes the model makes a mistake” into “a systemic degradation signal that accumulates exponentially”. After 20 iterations a document is no longer a reliable representation of the original content — which is precisely the iteration count that long-running agentic workflows typically exceed.

What does the follow-up blog post clarify?

The team published a follow-up because the original paper triggered significant discussion and the authors wanted to “clarify several important points about what the paper does — and does not — claim”. The blog post addresses:

• Generality of the problem — does this apply to a specific model or a systemic class of issues?
• Mitigation strategies — which approaches help reduce degradation?
• Implications for production agents — which workflows are most severely affected?

What does this finding mean for agentic workflows?

Long-horizon agentic workflows are hit hardest. Typical examples: research agents that generate, edit and forward drafts; multi-step document automation where a single document passes through dozens of transformations; continuous summarization cycles where an agent reduces a large corpus through iterative summarization.

The work implicitly refutes the popular notion that agent reliability is a problem solvable solely through a better model — the degradation pattern is sufficiently systemic to suggest a need for architectural solutions: ground truth retention, periodic verification against the original, explicit revision review before an agent forwards content.

The approach builds on 2026’s week of dramatic safety/reliability papers — arXiv:2605.13825 History Anchors (14.5.), arXiv:2605.12474 Reward Hacking Rubric (13.5.), arXiv:2605.11882 FATE Safety (13.5.). The Microsoft Research contribution alongside that arXiv wave signals the maturation of agentic reliability research as a distinct discipline.