The paper's authors issued a follow — up explaining that their study used the DELEGATE‑52 benchmark as a controlled

The authors of “LLMs Corrupt Your Documents When You Delegate” published a detailed clarification noting the paper is a controlled stress test of long‑horizon delegated workflows and that its core result — substantial semantic degradation in some evaluated settings — identifies a vulnerability in multi‑step automated editing rather than a universal failure across all deployments. This clarification matters because it targets the specific behavior of delegated execution chains and helps builders and researchers prioritize verification and engineering steps where fidelity loss is most likely to appear.

Their evaluation centered on a benchmark called DELEGATE‑52 and a sequence of chained transformation‑and‑inversion tasks designed to test whether semantic content survives extended rounds of delegated edits. Methodologically, the team used domain‑specific semantic parsing to detect meaningful changes to artifacts and explicitly excluded superficial formatting changes from the corruption metric; they also did not use task completion or user satisfaction as part of that metric. The authors emphasize that DELEGATE‑52 was constructed as a stress test with limited human verification between steps, intended to surface delegation patterns that compromise semantic fidelity.

Quantitatively, the clarification reports that state‑of‑the‑art models in the evaluated settings exhibited roughly 19 — 34% degradation in artifact fidelity after 20 delegated iterations, while Python‑based workflows proved far more robust, averaging under 1% degradation. The authors present these figures as outcomes of the constrained evaluation environment, not as universal failure rates for all models or tasks. this harness reduced but did not eliminate observed degradation and is not claimed to represent production‑grade, workflow‑optimized systems.

To place the findings in context, the follow‑up notes that many deployed AI systems layer models with verification loops, orchestration, and domain‑specific tooling that can mitigate fidelity loss seen in a stress test. The paper’s primary claim is methodological: a benchmark’s strength on short‑horizon tasks does not automatically guarantee dependable long‑horizon delegated execution, and DELEGATE‑52 should be used diagnostically to reveal delegation failure modes rather than to make blanket assertions about all production systems.

Practically, the authors conclude that reliable long‑horizon delegation remains an open research and engineering challenge. They do not argue against using AI in professional workflows; instead they identify where further research, verification design, and specialized engineering are needed to close the fidelity gap. The follow‑up also directs readers to a related Research Forum event series with on‑demand episodes for ongoing community discussion and deeper technical engagement.