Manuscript ID: 2601.09182v1
Title: Position on LLM-Assisted Peer Review: Addressing Reviewer Gap through Mentoring and Feedback
Authors: Yun et al.
This position paper addresses the critical challenge of declining peer review quality in AI conferences, which the authors attribute to a "Reviewer Gap" comprising both volume constraints (too many submissions) and expertise constraints (insufficient qualified reviewers). Rather than advocating for LLM-generated reviews, the authors propose a human-centered approach using LLMs as educational tools through two complementary systems: (1) a mentoring system that trains reviewers through progressive learning stages, and (2) a feedback system that helps reviewers refine their draft reviews before submission. The framework is grounded in five foundational principles: fidelity, clarity, fairness, proportionality, and constructiveness.
Timely and Important Problem. The paper tackles a genuine crisis in academic publishing that resonates with the lived experience of conference organizers, reviewers, and authors. The framing of the "Reviewer Gap" as having both volume and quality dimensions is analytically useful and moves beyond simplistic capacity arguments.
Human-Centered Philosophy. The authors' explicit rejection of review automation in favor of reviewer augmentation represents a thoughtful ethical stance. The distinction between using LLMs to replace versus support human judgment is crucial and well-articulated. The optional refinement structure respects reviewer autonomy while offering assistance.
Structured Framework. The five foundational principles provide a clear rubric for evaluating review quality, and the dual-system architecture (mentoring + feedback) addresses both long-term capability development and immediate quality assurance. The progression from guided recognition through refinement practice to full simulation demonstrates pedagogical sophistication.
Acknowledgment of Critiques. The Discussion section engages substantively with potential objections regarding deskilling, bias amplification, and homogenization. The authors' responses—particularly the distinction between evaluating review form versus substantive judgments—are reasonable.
Absence of Empirical Validation. The paper is purely conceptual, offering no pilot implementation, user studies, or empirical evidence that the proposed system would achieve its stated goals. Would reviewers actually use such a system voluntarily? Would it improve review quality? Would junior reviewers develop expertise faster? These critical questions remain unanswered. Even a small-scale prototype evaluation would substantially strengthen the contribution.
Unvalidated Foundational Principles. While the five principles seem reasonable, the paper provides no justification for why these specific five are necessary and sufficient. Are they derived from empirical analysis of high- versus low-quality reviews? Validated through expert consensus? Tested for internal consistency or potential conflicts? The authors cite conference guidelines but don't demonstrate that these principles capture what actually distinguishes effective from ineffective reviews. For instance, research on peer review effectiveness (e.g., Bornmann & Daniel 2010) suggests inter-rater reliability and predictive validity as key quality metrics—dimensions not directly addressed by the proposed principles.
Insufficient Engagement with Peer Review Literature. The paper cites primarily recent AI/LLM papers but engages minimally with the substantial scholarly literature on peer review training and effectiveness. Galipeau et al. (2013) is cited but not deeply engaged with. Work on peer review training effectiveness, reviewer bias, and quality assessment (e.g., Jefferson et al. 2007; Bruce et al. 2016) could inform the system design and temper some claims.
Underspecified Implementation. Critical operational questions remain unaddressed: How would the LLM be trained to recognize principle violations? What datasets would be used? How would the system handle disciplinary variation in review norms? What threshold of "reliability" is required in the testing stage? How would false positives (incorrect feedback) be handled? The paper notes that expert meta-feedback could improve the system but doesn't explain the practical mechanics of this collaborative framework.
Questionable Incentive Structure. The voluntary certification system could create problematic dynamics. Would certified reviewers receive preferential assignments, potentially disadvantaging those who don't have time for training? Could certification become a signaling mechanism that pressures participation? The paper doesn't address how conferences would integrate certification into reviewer recruitment and assignment.
Overstated Virtuous Cycle Claims. The macro-level virtuous cycle depicted in Figure 2 makes sweeping claims about improved reviews leading to refined research, which advances AI technology, which improves review systems. This causal chain involves enormous leaps and ignores countervailing forces (e.g., competitive pressure for novelty claims, publication bias, incentives for sensational findings). The connection between review quality and research quality, while plausible, is empirically complex and contested.
Scope Limitations Acknowledged Too Late. The acknowledgment that the system doesn't handle code, datasets, or supplementary materials appears only in the Discussion. This is a significant limitation for AI research where reproducibility is paramount, and should be acknowledged earlier.
Unclear Cost-Benefit Analysis. While the paper critiques mandatory training as costly and burdensome, developing, deploying, and maintaining two complex LLM-based systems would also require substantial resources. No discussion of implementation costs, maintenance burden, or scalability challenges appears.
Detection Stage Details. The feedback system's "Detection and Cross-Verification" stage involves the LLM scanning "the entire review text" and cross-referencing the paper. For complex technical papers, this could be computationally expensive and error-prone. The reliability of current LLMs in detecting nuanced issues like disproportionate criticism or contextual fairness violations is questionable but not addressed.
Reviewer Certification Ambiguity. The paper states certification is "not a mandatory qualification" but rather a "positive signal." However, if conferences or area chairs begin to prefer certified reviewers, the voluntary nature becomes questionable. This tension deserves explicit discussion.
Language and Clarity. Generally well-written, but some passages are unnecessarily dense. The phrase "safe-to-fail learning ecosystem" (page 3) is jargon that could be simplified. Some citations are to in-press or preprint works where claims may not be finalized.
REVISE AND RESUBMIT
This paper makes a valuable contribution to an important debate about the future of peer review in an era of AI tools. The human-centered philosophy is commendable, and the dual-system framework offers a thoughtful alternative to simplistic automation approaches. However, the paper currently functions more as a vision statement than a research contribution with validated findings.
For acceptance, the authors should:
The core insight—that LLMs should augment rather than replace reviewer judgment—is valuable and timely. With empirical grounding and more rigorous analysis of practical implementation challenges, this could become a significant contribution to the literature on peer review reform.
This position paper addresses a genuine crisis in AI conference peer review and proposes a thoughtful, human-centered alternative to review automation. The framework is well-structured and the writing is generally clear. However, the complete absence of empirical validation is a significant weakness for a venue that values evidence-based contributions.
I recommend giving the authors an opportunity to revise with implementation results, even from a limited pilot. The topic is sufficiently important that a well-executed empirical study could make a strong contribution. Alternatively, if the journal accepts purely conceptual position papers, this could be acceptable with substantial strengthening of the theoretical foundation and engagement with existing literature.
The authors appear responsive to critique based on their Discussion section, and I believe they would benefit from the revision process. The work has potential but needs empirical grounding to move from vision to validated research contribution.
Suggested Action: Major Revision (Revise and Resubmit)
Expertise Level: High (I have substantial experience with peer review systems, AI conference reviewing, and LLM capabilities)
Confidence in Recommendation: High