Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows. We appreciate the interest in this work and want to clarify several important points about what the paper does—and does not—claim.
The research aims to develop robust evaluation methods for long-horizon delegated and collaborative tasks. More broadly, this work reflects an ongoing effort to better understand the gap between strong benchmark performance and certain real-world tasks. Using a controlled evaluation methodology, we examine how well information is preserved across these extended workflows. Within this constrained setting, we observe that models can accumulate fidelity degradation over repeated edits. Note however, that current production systems can mitigate these effects through verification loops, orchestration, and domain-specific tooling.
Our goal is not to argue against the use of AI systems in professional workflows, but rather to identify where current systems need further research and engineering to help make them more trustworthy collaborators. This benchmark is intended as a diagnostic tool for examining delegation patterns, not a measure of overall model capability, task success, or user outcomes.
