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arxiv: 2604.15597 · v1 · submitted 2026-04-17 · 💻 cs.CL · cs.HC

Recognition: unknown

LLMs Corrupt Your Documents When You Delegate

Philippe Laban , Tobias Schnabel , Jennifer Neville
Authors on Pith no claims yet

Pith reviewed 2026-05-10 09:44 UTC · model grok-4.3

classification 💻 cs.CL cs.HC
keywords LLMsdocument editingdelegationbenchmarkcontent corruptionAI reliabilityknowledge workworkflow degradation
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The pith

Large language models corrupt an average of 25% of document content during long delegated editing workflows.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to test whether LLMs can serve as reliable delegates for document-based knowledge work. Delegation assumes the model will edit documents without introducing unintended changes, but the authors show this trust is misplaced. They created DELEGATE-52, a set of tasks simulating long workflows in 52 fields including coding and music notation. Tests on 19 models find that even the strongest ones alter 25 percent of the content on average by the workflow's end. The corruption gets worse with bigger documents or more steps and happens even when models use tools.

Core claim

Current LLMs are unreliable delegates that degrade documents by introducing sparse but severe errors during long interaction sequences. In the DELEGATE-52 benchmark, which covers in-depth editing tasks across 52 professional domains, frontier models corrupt an average of 25% of the document content. Agentic tool use fails to reduce this degradation, which intensifies with larger document sizes, longer interactions, and the presence of distractor files. The errors compound silently over time rather than appearing all at once.

What carries the argument

DELEGATE-52, a benchmark simulating long delegated document editing workflows across 52 domains to quantify content corruption.

If this is right

  • Agentic tool use does not reduce document corruption in these workflows.
  • Corruption levels rise with increasing document size and interaction length.
  • Additional distractor files in the workflow increase error rates.
  • Errors remain sparse but severe and accumulate across multiple steps.
  • LLMs cannot be trusted to faithfully execute delegated document tasks without oversight.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Breaking long tasks into shorter sessions could limit the compounding of errors.
  • Monitoring mechanisms that flag content changes might be needed before full delegation becomes practical.
  • The benchmark points to reliability issues that short single-prompt tests miss.
  • Domains requiring precise formatting, such as music notation or crystallography, may show even higher vulnerability.

Load-bearing premise

The DELEGATE-52 tasks and the metric used to detect corruption accurately reflect real delegated document work and that the observed changes are unintended mistakes.

What would settle it

Running the DELEGATE-52 workflows with human experts and finding that they introduce comparable rates of content changes that domain specialists accept as normal variations would undermine the claim that LLMs corrupt documents.

Figures

Figures reproduced from arXiv: 2604.15597 by Jennifer Neville, Philippe Laban, Tobias Schnabel.

Figure 1
Figure 1. Figure 1: Illustrative examples of how LLMs corrupt documents over long workflows in the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The backtranslation round￾trip primitive. In DELEGATE-52 we simulate long workflows that could be part of a knowledge worker’s tasks. A workflow consists of seed documents along with other content that are transformed via a sequence of complex editing tasks, mirroring the iterative nature of delegated work. We now introduce the framework that allows us to (i) perform evaluation automatically and (ii) scale… view at source ↗
Figure 3
Figure 3. Figure 3: DELEGATE-52 includes work environments from 52 professional domains in five categories: Science & Engineering, Code & Configuration, Creative & Media, Structured Records, and Everyday. in natural language a transformation of the seed document and its inverse (σ, σ −1 ). First, an LLM applies a forward instruction to the seed document, producing a transformed document t = σ(s) = LLM(s; x→). Second, the LLM … view at source ↗
Figure 4
Figure 4. Figure 4: Example work environment from the accounting domain in [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Top: Domains in DELEGATE-52 implement a parsing function that converts text documents into a structured representation which is then used by a similarity function to score two parsed instances. Bottom: concrete example for the recipe domain. This flexibility allows for a domain-appropriate weighing of various components of the scoring function. For instance a small surface-level change in an ingredient (e.… view at source ↗
Figure 6
Figure 6. Figure 6: A round-trip relay: a se￾quence of 10 consecutive round-trip tasks, total: 20 interactions. Experimental Setup. Our main experiment is a round-trip relay with N = 10 consecutive round-trips per environment, simulating 20 delegated interactions. In each interaction, the model receives all work environment documents as text in its context window in a single turn (unless stated otherwise in the agentic experi… view at source ↗
Figure 7
Figure 7. Figure 7: Decomposition of degradation into deletion (missing elements) and corruption (present but incorrect). Deletion vs. Corruption (Appendix F). So far, the pa￾per primarily discusses overall degradation that occurs during simulation. Yet, degradation can be caused by several underlying phenomena. To explore this further, we decompose model degradation into two components: deletion of content vs. corruption of … view at source ↗
Figure 8
Figure 8. Figure 8: Cohen’s d effect sizes for docu￾ment characteristics on scores. Document Characteristics (Appendix G). We analyzed how various document characteristics affect model performance, finding that models perform better in programmatic domains (Python, DBSchema) compared to natural language domains (e.g., Recipe, Fiction). Performance is also higher in domains with high repetitiveness and structural density (e.g.… view at source ↗
Figure 9
Figure 9. Figure 9: Operation difficulty: point-biserial cor [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces DELEGATE-52, a benchmark for long delegated document-editing workflows across 52 professional domains. Experiments with 19 LLMs show that 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 multi-turn interactions, with degradation worsening by document size and interaction length; agentic tool use provides no improvement.

Significance. If the corruption measurements prove robust, the work is significant for NLP and HCI because it supplies large-scale empirical data on LLM unreliability in an emerging delegation paradigm for knowledge work. The breadth of models and domains tested offers a useful reference point for future agent and workflow research.

major comments (3)
  1. [§3] §3 (Benchmark and metric definition): The degradation metric underlying the central 25% corruption figure is not specified in enough detail (e.g., whether it uses string overlap, token edit distance, semantic similarity, or expert judgment) to separate unintended errors from task-appropriate edits such as rephrasing or cross-reference updates. This distinction is load-bearing for interpreting the result as 'corruption' rather than normal workflow variation.
  2. [§4] §4 (Results): No human baseline, inter-annotator agreement, or semantic validation of changes is reported for the 25% figure on frontier models. Without these, it is impossible to determine whether the measured degradation exceeds acceptable human variation in long editing sessions.
  3. [§4.2] §4.2 (Statistical controls): The abstract and results mention multiple conditions and 19 models but provide no details on run-to-run variance, statistical significance testing, or controls for prompt sensitivity, which are necessary to support the claim that degradation is systematic.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'vibe coding' is introduced without definition or reference.
  2. [Figures/Tables] Figure and table captions: Several captions are terse and do not fully describe the axes, error bars, or exact conditions shown.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important areas for improving the clarity and rigor of our presentation. We address each major comment point by point below, indicating the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Benchmark and metric definition): The degradation metric underlying the central 25% corruption figure is not specified in enough detail (e.g., whether it uses string overlap, token edit distance, semantic similarity, or expert judgment) to separate unintended errors from task-appropriate edits such as rephrasing or cross-reference updates. This distinction is load-bearing for interpreting the result as 'corruption' rather than normal workflow variation.

    Authors: We appreciate the referee drawing attention to this. Section 3 defines the degradation metric as the fraction of document content altered in ways that introduce factual errors, omissions, or inconsistencies not justified by the delegated task instructions, computed via a hybrid approach: sentence-level embedding cosine similarity (thresholded at 0.85 for potential issues) followed by targeted string matching on domain-specific entities and manual review on a 10% sample. To address the concern about distinguishing corruption from appropriate edits, we will revise §3 to add an explicit taxonomy with examples (e.g., changing a crystallography lattice parameter is corruption; updating a cross-reference after content insertion is not), the precise formula, and inter-rater reliability for the manual component. This will make the 25% figure more interpretable. revision: yes

  2. Referee: [§4] §4 (Results): No human baseline, inter-annotator agreement, or semantic validation of changes is reported for the 25% figure on frontier models. Without these, it is impossible to determine whether the measured degradation exceeds acceptable human variation in long editing sessions.

    Authors: We agree this would provide valuable context. The original experiments prioritized breadth across 19 models and 52 domains rather than human comparison. In the revision we will add a human baseline subsection in §4, reporting degradation rates from professional editors performing analogous delegated workflows on a stratified 8-domain subset (with the same interaction length and document sizes). We will also report inter-annotator agreement (Cohen's kappa) for the semantic validation labels on the frontier-model outputs and include a direct comparison showing that LLM degradation exceeds the human baseline by a statistically notable margin. revision: yes

  3. Referee: [§4.2] §4.2 (Statistical controls): The abstract and results mention multiple conditions and 19 models but provide no details on run-to-run variance, statistical significance testing, or controls for prompt sensitivity, which are necessary to support the claim that degradation is systematic.

    Authors: We acknowledge the need for greater statistical transparency. Although the experiments used fixed prompt templates across all models, we will expand §4.2 in the revision to report: (i) run-to-run variance (each model-condition pair was executed three times with different random seeds; we will add mean ± standard deviation), (ii) statistical significance (paired t-tests and ANOVA results comparing frontier vs. other models and across document sizes), and (iii) prompt-sensitivity controls (we tested two paraphrased prompt variants on a 5-domain pilot and observed <4% variation in corruption rates, which we will document). These additions will substantiate that the degradation pattern is systematic rather than artifactual. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical measurement study

full rationale

The paper introduces DELEGATE-52 as a benchmark for delegated document workflows and reports direct experimental measurements of document degradation across 19 LLMs. No derivation chain, equations, fitted parameters, predictions, or self-citations are present in the provided text. The central claim (25% average corruption) is an observed average from model runs on the benchmark tasks, not a quantity derived from or reduced to prior inputs by construction. The study is self-contained as an empirical evaluation with no load-bearing theoretical steps that could exhibit circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that DELEGATE-52 tasks faithfully represent delegated professional work and that the authors' definition of 'corruption' corresponds to practically harmful changes.

axioms (1)
  • domain assumption DELEGATE-52 tasks accurately simulate real professional document editing workflows across domains.
    Invoked to generalize experimental results to practical delegation scenarios.

pith-pipeline@v0.9.0 · 5504 in / 1177 out tokens · 59841 ms · 2026-05-10T09:44:29.264583+00:00 · methodology

discussion (0)

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Reference graph

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