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
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 heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1 Introduction | 1 | ≈ 0.95 pp. | 2.97% |
| 2 Background: AI in peer review | 2 | ≈ 1.44 pp. | 4.5% |
| 3 The AI reviewer hivemind effect | 3 | ≈ 2.18 pp. | 6.81% |
| 4 Paper laundering: Gaming AI reviews is trivial | 5 | ≈ 1.36 pp. | 4.25% |
| 5 Alternative views | 7 | ≈ 0.96 pp. | 3% |
| 6 Call to action: Toward a science of peer review automation | 8 | ≈ 1.42 pp. | 4.44% |
| 7 Conclusions | 9 | ≈ 0.26 pp. | 0.81% |
| Acknowledgements | 9 | ≈ 0.23 pp. | 0.72% |
| References | 10 | ≈ 4.98 pp. | 15.56% |
| A Limitations | 15 | ≈ 0.39 pp. | 1.22% |
| B Implementation details | 15 | ≈ 6.3 pp. | 19.69% |
| C AI reviewer score correlations | 21 | ≈ 0.58 pp. | 1.81% |
| D Common templates used in AI reviews | 22 | ≈ 0.32 pp. | 1% |
| E What laundering changes | 22 | ≈ 1.16 pp. | 3.62% |
| F Length statistics and embedding robustness | 23 | ≈ 0.46 pp. | 1.44% |
| G Ablations | 24 | ≈ 1.48 pp. | 4.63% |
| H AI-generated reviews | 25 | ≈ 7.23 pp. | 22.59% |
All detected figure and table locations
Main text
Argument and organization
- 1 Introduction; 2 Context and Related Work
States two necessary but explicitly insufficient conditions for peer-review automation.
- 3 AI Reviewer Hivemind; 4 Paper Laundering
Uses observational review data and controlled rewriting experiments to challenge those conditions.
- 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.