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REVIEW 4 major objections 5 minor 41 references

AI-written research proposals pass human review, but AI reviewers favor AI-authored text by about one point.

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2026-08-01 01:13 UTC pith:XMOCQVYB

load-bearing objection A transparent, well-designed controlled study whose central parity claim leans on a human reviewer panel made up entirely of co-authors; fix that and the paper has real value. the 4 major comments →

arxiv 2607.25881 v1 pith:XMOCQVYB submitted 2026-07-28 cs.CL astro-ph.COastro-ph.IMcs.HCgr-qc

AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology II: Project Planning and Proposal Evaluation

Jia Liu , Veena Krishnaraj , Kateryna Vovk , Kosuke Aizawa , Adrian E. Bayer , Linda Blot , Jessica Cowell , Suyog Garg
show 13 more authors
Jonathan Gr\'ee Anamaria Hell Ben Horowitz Masaya Ichikawa Kanyuni Iemoto Keigo Kondo Zacharie Lorsin Kevin McCarthy Jamie Robinson Miguel Ruiz-Granda Leander Thiele Ievgen Vovk Mingshen Zhou
This is my paper
classification cs.CL astro-ph.COastro-ph.IMcs.HCgr-qc
keywords large language modelsscientific proposalsproject planningproposal evaluationpeer review biasauthorship detectionphysics researchAI-assisted research
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper asks whether large language models can plan real research projects and whether they can be trusted to review such plans. Eight expert-conceived projects in physics, astrophysics, and cosmology were each turned into four one-page proposals: one by a human expert, three by mid-2025 LLMs. Four human reviewers, blind to origin, rated the human and AI proposals statistically indistinguishable overall, while two frontier AI reviewers gave AI-written proposals roughly one point higher on a five-point scale. Human reviewers identified authorship about three-quarters of the time; the AI reviewers did so perfectly. The paper concludes that LLMs can already produce competitive short research plans, but that deploying AI as reviewer carries a systematic pro-AI bias that would disadvantage human applicants.

Core claim

The central discovery is a three-part asymmetry. First, capability: when blinded expert reviewers score one-page research plans on a four-aspect rubric, AI-generated plans are rated no worse than plans written by human experts, with mean totals around 3.5 out of 5 for both. Second, detectability: human experts can tell AI from human authorship about 72–79% of the time, while the two most capable AI reviewers classified all 32 proposals correctly, 100%. Third, bias: every AI reviewer tested scored AI-written proposals about one point higher than human-written ones, a pro-AI preference absent from the human panel; human-written proposals received nearly identical scores from every evaluator, s

What carries the argument

The controlled corpus of 32 one-page proposals: eight expert-conceived project seeds (title, background, goal) each expanded by one human expert and three LLMs under an identical four-section template and fixed prompt, plus the same four-aspect scoring rubric (clarity and structure, appropriateness of methods, resource and tool planning, feasibility/timeline/risk) and binary origin-judgment task given to all reviewers. The design isolates content from stylistic tells — uniform formatting, grammar normalization of human text — so that observed differences in scores and origin judgments can be attributed to author type rather than formatting.

Load-bearing premise

The valid comparison assumes the four human reviewers — co-authors of this paper — judge the proposals exactly as an independent grant panel would, with no recognition of their colleagues' writing and no stake in the outcome; if they recognized or favored particular proposals, both the human/AI parity and the reported detection rates could be inflated.

What would settle it

Run the identical 32-proposal, blinded review with eight reviewers from outside the author list who have never seen the proposals, and check whether human-rated AI-vs-human means remain within 0.2 points and origin-detection accuracy lands near 72/79%. A second decisive test: give both AI reviewers the same proposals with instructions to ignore style and score only scientific substance; if the roughly one-point pro-AI gap vanishes, the bias is stylistic rather than substantive.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If short structured proposals are representative, researchers can use LLMs to draft competitive project plans without losing quality in human review.
  • AI-reviewer pro-AI bias means incorporating LLM evaluators into grant review without calibration would systematically disadvantage human-written proposals.
  • Perfect AI authorship detection on this corpus suggests current frontier models can identify AI-generated planning text at high accuracy, though the paper cautions against generalizing from 32 proposals.
  • The tight anti-correlation between AI advantage and human-plan quality implies AI assistance may be most valuable where the human baseline plan is weak, and least valuable where it is already strong.
  • Per-aspect scores show AI plans are weakest on feasibility, timeline, and risk awareness under both human and AI reviewers, so AI assistance is least reliable for realistic scheduling and contingency planning.

Where Pith is reading between the lines

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

  • The near-constant vertical offset between human and AI panels suggests a simple calibration: shifting AI-reviewer scores down by about one point for AI-flagged proposals would align their project-level rankings with the human panel's; this is testable on the paper's own data.
  • Because the pro-AI bias appeared across all four AI models and both vendors, it likely stems from stylistic regularities of LLM output — template-like five-phase structure, exhaustive tool lists, round-number timelines — rather than any single model's idiosyncrasy, so the bias may persist even as models improve unless review instructions explicitly penalize those markers.
  • The perfect AI detection rate may be an artifact of the constrained one-page template, since LLM proposals in this corpus share a detectable 'too clean' structure; running the same origin-judgment task on full-length, unconstrained proposals would test whether accuracy collapses.
  • A decisive external check the authors did not run: repeat the review with non-author reviewers who have no stake in the outcome, to rule out recognition effects on the 72/79% human detection rates and the human parity ratings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper reports a controlled, blinded study of AI-assisted scientific project planning and proposal evaluation. Eight expert-written project seeds in physics, astrophysics, and cosmology were each expanded into one human-written and three LLM-written one-page proposals, yielding 32 proposals. Four human reviewers (all co-authors) and two frontier LLM reviewers evaluated all proposals on a four-aspect rubric and also made a binary human-versus-AI authorship judgment. The reported results are: human reviewers rated human- and AI-written proposals similarly overall; both AI reviewers scored AI-written proposals roughly one point higher than human-written ones; AI reviewers classified all 32 proposals correctly, while human reviewers averaged 72% and 79% correct on human- and AI-written proposals, respectively; and the AI-human score gap is strongly anti-correlated with the quality of the human-written proposal. The authors conclude that LLMs can currently produce project plans comparable to human ones in the eyes of human reviewers, but that AI reviewers exhibit a systematic pro-AI bias that warrants caution in deploying LLMs in proposal preparation and review. The manuscript is transparent about the small sample, the rapid model turnover, and the fact that the human reviewers are authors, and it includes appendices with project seeds, prompts, and sample proposals.

Significance. If the results hold, this is a useful and timely controlled contribution to the growing literature on LLMs in scientific workflows. The study design has real strengths: a fixed template and common starting point for human and AI proposals, uniform anonymized formatting, explicit disclosure of evaluator identity, and rich qualitative material on the cues used for authorship judgments. The finding of a systematic preference by AI reviewers for AI-written proposals, if robust, has direct policy relevance for grant review, and the authors appropriately cite agency guidelines that restrict AI use in peer review. However, the paper's central quantitative claims rest on a small sample with no inferential statistics, and the human-reviewer panel consists entirely of co-authors, which is a serious independence risk. The human-written condition is also contaminated by a ChatGPT grammar-correction pass, and one of the paper's key project-level correlations is partly mechanical. These issues are substantive but addressable through re-analysis and more careful framing, so the paper is best treated as a strong pilot study requiring major revision rather than a definitive measurement.

major comments (4)
  1. [Sec. II B / III B] The human panel that grounds the parity claim consists of four co-authors. The manuscript discloses this but does not control for or test the obvious recognition risk: reviewers may recognize the human planners' prose or project identities, which could inflate the human-written proposals' quality ratings and contaminate the 72%/79% origin-detection rates. No inter-rater reliability statistic is reported; the paper even notes that the four reviewers 'often disagreed' (Sec. III B). With eight projects per condition, one or two non-independent judges can determine the aggregate parity result. This is load-bearing because the abstract's first claim rests entirely on this panel. Please report per-reviewer scores and agreement (e.g., ICC or Fleiss' kappa), and either add independent external reviewers or re-frame the human-panel component as a pilot with the independence threat as a central li
  2. [Sec. II C] The 'human-written' proposals were post-processed by ChatGPT 4o. Thus the human condition is not purely human-authored; AI stylistic normalization may introduce the very template-like, polished features that the reviewers (human and AI) report using to flag AI text (Sec. III A). This can inflate both the AI reviewers' 100% classification and the human reviewers' perception of parity. The manuscript should quantify the edit distance or amount of rewriting, justify that 'minimal changes' indeed preserved content and style, and preferably include a no-AI-touch control arm, or at least treat the condition as 'human-written then AI-normalized' in all claims.
  3. [Sec. III B, Tables IV and V] The central quantitative claims—that human reviewers rate human and AI proposals similarly and that AI reviewers give AI proposals about one point more—are supported only by group means and standard deviations across n=8 projects. No confidence intervals, p-values, or effect sizes are given, and the small sample means the mean difference can be dominated by one project (e.g., GW, which is called out in Sec. III C). Please provide per-project data, bootstrapped or permutation confidence intervals for the gaps, and ideally a mixed-effects model with reviewer and project as random effects. Without this, the 'similarly overall' and 'one point higher' phrasing overstates the precision of the measurements.
  4. [Sec. III C, Fig. 4] The reported r=-0.95 between the AI-human score gap and the human-written proposal score is largely mechanical, because the gap subtracts the human score from itself. Even if the AI-written score were statistically independent of the human-written score, a strong negative correlation would appear; the reported value is therefore not evidence that the AI advantage is concentrated in projects with weak human plans. The correct diagnostic is a scatter plot of mean AI score versus human score with a fitted slope, or a formal model. Please re-analyze before drawing the conclusion that the parity and bias are 'driven by the same handful of projects.'
minor comments (5)
  1. [Abstract / Sec. III A] The abstract says 'two AI reviewers' but Table III also lists Codex 5.5 Pro and Claude Sonnet 4.6; clarify which are primary and which are supplementary, and make the table caption consistent with the main text.
  2. [Sec. II D / Appendix B] The AI evaluation prompt is not included; Appendix B gives proposal-generation prompts only. Since the AI reviewers' behavior depends on the exact instruction, please include the reviewer prompt in the appendix or a supplement.
  3. [Table II] Minor typo in the rubric header: 'W eak' should be 'Weak'.
  4. [Reproducibility] No data/code availability statement appears. Since the study is empirical and based on LLM outputs, depositing de-identified proposals, reviewer scores, and prompts would substantially strengthen reproducibility.
  5. [Sec. IV / References] Reference [20] is listed as arXiv:2607.xxxx with a placeholder; this should be updated to the actual companion paper ID. Also, the paper's concluding caveats do not mention the ChatGPT grammar-correction pass on human proposals or the co-author reviewer bias; these belong in the limitations paragraph.

Circularity Check

0 steps flagged

No significant circularity: the study is an empirical measurement, not a derivation, and no reported finding reduces by construction to its inputs.

full rationale

The paper reports a controlled empirical comparison: fixed expert-provided seeds (title, background, goal) are given to human and AI writers, and the resulting proposals are scored by human and AI reviewers. The central claims—human/AI parity, AI reviewers' pro-AI bias, and near-perfect AI authorship detection—are measured outcomes, not quantities constructed from the inputs. No parameter is fitted and then renamed as a prediction; no equation sets a reported result equal to an input; the rubric and template are shared inputs rather than derived outputs. The cited companion Paper I [20] merely notes that the same project seeds are used there and does not carry any load-bearing justification; it is a normal companion reference, not a circularity. The paper's disclosed limitation that four human reviewers are co-authors (Sec. II B) is a genuine external-validity concern, since those reviewers might recognize human-written plans or have conflicts of interest, but this is not circularity: it does not make the human-parity rating equal by construction to the study design. The paper's own caveats—small sample, reviewer disagreement, rapidly changing AI models, and evaluation limited to planning rather than novelty—further show that the findings are contingent empirical results rather than tautologies.

Axiom & Free-Parameter Ledger

1 free parameters · 6 axioms · 0 invented entities

This is an empirical study with no mathematical derivation. The central claims rest on assumptions about reviewer representativeness, the blindness of evaluation, the effect of the grammar-normalization pass, and the equal weighting of rubric aspects. No new physical or conceptual entities are introduced.

free parameters (1)
  • Equal rubric aspect weights = w = 1/4 for each of four aspects
    The total score used in Figure 2 and Table IV is an unweighted average of clarity, methods, resources, and feasibility scores. No sensitivity analysis is provided, and the parity/bias conclusions depend on this equal weighting (Section II D, Tables IV-V).
axioms (6)
  • domain assumption Human expert rubric scores are the validity standard for proposal quality
    The study treats the four reviewers' ratings as the ground truth for whether AI and human proposals are comparable (Section II D, Section III B).
  • domain assumption The four human reviewers, though co-authors, evaluate proposals impartially and without recognizing the human planners
    Reviewers are four of this paper's authors (Section II B). The analysis does not test whether familiarity with the human-written proposals influenced scores or origin judgments.
  • domain assumption Blind formatting plus a light ChatGPT grammar pass removes superficial writing cues without changing content
    Human-written proposals were passed through ChatGPT 'to correct typos and grammatical mistakes... minimal changes' (Section II C). No verification confirms content equivalence, and this affects both the human-vs-AI comparison and detection results.
  • domain assumption One-page templated proposals are a representative proxy for research project planning
    The authors explicitly limit the claim to short structured proposals and leave full-length proposals to future work (Section II C).
  • domain assumption The four rubric criteria are the relevant dimensions of proposal quality and are equally important
    Table II defines the rubric; no independent validation or weighting evidence is provided.
  • domain assumption AI reviewers were not exposed to the true origin labels or authors' identities during evaluation
    Anonymization is described (Section II D), but the paper does not verify the reviewers' access or fully rule out leakage through tool access.

pith-pipeline@v1.3.0-alltime-deepseek · 16287 in / 17153 out tokens · 167091 ms · 2026-08-01T01:13:40.347272+00:00 · methodology

0 comments
read the original abstract

We investigate how well large language models (LLMs) can assist scientific project planning and proposal evaluation. One-page project plans were independently generated for eight expert-conceived research projects in physics, astrophysics, and cosmology by human researchers and three contemporary LLMs (ChatGPT, Claude, and DeepSeek; mid-2025 models, used with their default tool access). The resulting 32 proposals were blindly evaluated by four human reviewers and two newer frontier LLMs (Claude Opus 4.8 and ChatGPT Pro 5.5) using a four-aspect evaluation rubric. Reviewers were also asked to identify whether each proposal was written by a human or an AI. Human reviewers rated human- and AI-written proposals similarly overall, whereas both AI reviewers scored AI-written proposals about one point higher (on a five-point scale) than human-written proposals. Human reviewers correctly identified human- and AI-written proposals 72% and 79% of the time, respectively, while both AI reviewers correctly classified all 32 proposals (100%). These results suggest that current LLMs can produce project plans comparable to human-written ones in the eyes of human reviewers, but that AI reviewers show a systematic preference for AI-generated proposals. Our results suggest caution when deploying LLMs widely in proposal preparation and evaluation.

Figures

Figures reproduced from arXiv: 2607.25881 by Adrian E. Bayer, Anamaria Hell, Ben Horowitz, Ievgen Vovk, Jamie Robinson, Jessica Cowell, Jia Liu, Jonathan Gr\'ee, Kanyuni Iemoto, Kateryna Vovk, Keigo Kondo, Kevin McCarthy, Kosuke Aizawa, Leander Thiele, Linda Blot, Masaya Ichikawa, Miguel Ruiz-Granda, Mingshen Zhou, Suyog Garg, Veena Krishnaraj, Zacharie Lorsin.

Figure 1
Figure 1. Figure 1: FIG. 1. Overview of the study design. Each expert-provided [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. Mean total proposal score (averaged over the four [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. Mean proposal score broken down by the four rubric aspects, grouped by evaluator (human reviewers, Claude Opus 4.8, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Project-level breakdown of the AI [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

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    Correct typo and grammar mis- takes, with minimal change to the content: [text]

    Methodology: Break down the work into major steps or phases (e.g., data preparation, modeling, analysis). Include theoretical, computational, or experimental techniques if relevant. Include the approximate time for each step. No more than 5 steps and keep it under 300 words. Use formal scientific tone appropriate for a grant or academic setting. Your prop...