Skip to content

Writing study: Position: Stop Automating Peer Review Without Rigorous Evaluation

← ICML 2026 oral writing corpus

Joachim Baumann, Jiaxin Pei, Sanmi Koyejo, Dirk Hovy · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Position & research agenda (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF
32 physical pages
Before references / appendix
≈ 9.11 page-equivalents
Main figures / tables
6 / 1
Attached appendix figures / tables
6 / 6
Attached appendix span
≈ 17.91 page-equivalents
Before references / appendix: ≈ 28.47%References / intervening material: ≈ 15.56%Attached appendix: ≈ 55.97%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Source-bound correction: Join wrapped printed heading text using adjacent source lines; original heading start coordinates are unchanged.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level headingStarts on PDF pageEstimated spanShare of entire PDF
1 Introduction1≈ 0.95 pp.2.97%
2 Background: AI in peer review2≈ 1.44 pp.4.5%
3 The AI reviewer hivemind effect3≈ 2.18 pp.6.81%
4 Paper laundering: Gaming AI reviews is trivial5≈ 1.36 pp.4.25%
5 Alternative views7≈ 0.96 pp.3%
6 Call to action: Toward a science of peer review automation8≈ 1.42 pp.4.44%
7 Conclusions9≈ 0.26 pp.0.81%
Acknowledgements9≈ 0.23 pp.0.72%
References10≈ 4.98 pp.15.56%
A Limitations15≈ 0.39 pp.1.22%
B Implementation details15≈ 6.3 pp.19.69%
C AI reviewer score correlations21≈ 0.58 pp.1.81%
D Common templates used in AI reviews22≈ 0.32 pp.1%
E What laundering changes22≈ 1.16 pp.3.62%
F Length statistics and embedding robustness23≈ 0.46 pp.1.44%
G Ablations24≈ 1.48 pp.4.63%
H AI-generated reviews25≈ 7.23 pp.22.59%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Context and Related Work

    States two necessary but explicitly insufficient conditions for peer-review automation.

  2. 3 AI Reviewer Hivemind; 4 Paper Laundering

    Uses observational review data and controlled rewriting experiments to challenge those conditions.

  3. 5 Alternative Views; 6 Call to Action; 7 Conclusions

    Answers objections and proposes evaluation, stakeholder, interaction and incentive research rather than a deployment recipe.

Problem, claim and contribution

A boxed pair of criteria makes the normative argument testable while the adjacent note explicitly denies that satisfying them alone would justify automation.

Introduction criteria box, p.1 · Main paper

Argument and section order

Empirical sections correspond to the two opening conditions, followed by counterarguments and four distinct research-agenda pillars.

Sections 3-6, pp.3-9 · Main paper

Figures, captions and visual explanation

The paired-score plot includes a zero-change baseline, confidence intervals and separate encodings for prompt and rewriter; a stacked outcome plot retains unchanged and worsened papers.

Figures 4-5, p.6 · Main paper

Tables, comparisons and evidence

The paper distinguishes detector-labeled reviews in the wild from a 60-paper controlled simulation, and reports effect sizes alongside significance rather than only large-sample p-values.

Section 3.1, p.3; Figures 2-3 and Section 3.5, p.5 · Main paper

Appendix and reproducibility

The limitations identify similarity as an imperfect proxy for viewpoint diversity and acknowledge detector labels, fixed prompts and one-venue sampling; manual rewrite inspection documents fabricated additions.

Appendix A, p.15; Appendix E.1, p.22 · Main paper

Clarity and writing conventions

Alternative Views narrows the thesis to unevaluated judgment automation and distinguishes validated assistance from replacing human assessment, avoiding a blanket prohibition on every AI use.

Sections 5.3-5.4 and 6.1, pp.7-8 · Main paper

What to reuse—and what not to infer

Transferable lesson: Anchor a position in explicit criteria, test those criteria with complementary evidence, and distinguish necessary conditions from sufficient justification.

Boundary: Embedding similarity is not direct argumentative diversity, acceptance prediction is not ground-truth scientific quality, and simulated-review score changes should not become manuscript-writing tactics.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked
2026-10-07
Reading status
Writing-focused manuscript scan + selected visuals inspected
Main text
Writing-oriented scan of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 5, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
Appendix / supplement
Appendix organization and main-text pointers were checked; focused text inspection of physical pages 15, 22, 25. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
1, 6

This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.