Authors: Philippe Laban, Tobias Schnabel, Jennifer Neville (Microsoft Research)
Categories: cs.CL (Computation and Language), cs.HC (Human-Computer Interaction)
Published: 2026-04-17
Repository: microsoft/DELEGATE52
Dataset: datasets/microsoft/DELEGATE52
Abstract
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. Our large-scale experiment with 19 LLMs reveals that current models degrade documents during delegation: even frontier models (Gemini 3.1 Pro, Claude 4.6 Opus, GPT 5.4) corrupt an average of 25% of document content by the end of long workflows, with other models failing more severely. Additional experiments reveal that agentic tool use does not improve performance on DELEGATE-52, and that degradation severity is exacerbated by document size, length of interaction, or presence of distractor files. Our analysis shows that current LLMs are unreliable delegates: they introduce sparse but severe errors that silently corrupt documents, compounding over long interaction.
Key Metrics
19 LLMs tested across 6 model families
310 work environments across 52 professional domains
Frontier models average ~25% degradation after 20 interactions
All-model average ~50% degradation after 20 interactions
Python is the only domain where most models (17/19) achieve "ready" status
Critical failures account for ~80% of total degradation
