arXiv:2603. 20450v2 Announce Type: replace-cross Abstract: A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction of otherwise human-written reviews.
arXiv:2608. 03581v1 Announce Type: cross Abstract: AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are.
AI-assisted peer review is increasingly discussed and adopted as a tool to support the scientific publishing process, yet there is little systematic understanding of how publication venues regulate its use or of how capable current AI review systems are. We address these questions by first surveying reviewer-facing AI policies across 111 leading AI/NLP conferences and medical journals, revealing substantial regulation differences between the two communities.
arXiv:2607. 26066v1 Announce Type: cross Abstract: The growing volume of scientific submissions has motivated interest in using large language models (LLMs) to assist peer review.
arXiv:2608. 03659v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate scientific reviews, yet existing evaluations rarely examine whether different providers align with both conference decisions and human reviewing priorities within the same controlled setting.
arXiv:2608. 08975v1 Announce Type: cross Abstract: As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when reported scientific content is preserved and how these effects vary across evaluation conditions.
Large language models (LLMs) are widely used to assist writing, but this study shows they alter both tone and meaning of human text. A user study found that heavy LLM use increased neutral essays by nearly 70% and made writers feel less creative and less in their voice. Even when prompted to make only grammar edits, LLMs changed the semantic content of essays and produced AI-generated scientific reviews that were less focused on clarity and significance and scored higher on average.
As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when reported scientific content is preserved and how these effects vary across evaluation conditions. We construct a controlled corpus of 4,200 full-paper manuscripts derived from 120 anonymized ICLR 2026 submissions.
arXiv:2606. 02255v1 Announce Type: cross Abstract: Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and how the annotation process was controlled.
arXiv:2312.06315v2 Announce Type: replace-cross Abstract: Warning: This paper contains content that may be offensive or upsetting. There has been a significant increase in the usage of large language...
arXiv:2608.21374v1 Announce Type: new Abstract: Literature reviews are essential to scientific progress, but rigorously evaluating automatically generated reviews remains difficult because many aspec...
The article reports that in July 2025, 18 arXiv manuscripts contained hidden instructions designed to manipulate AI‑assisted peer review, such as covert commands to give only positive reviews. These prompts were concealed using white text and microscopic fonts, and the authors’ reactions ranged from withdrawal to defending the practice as a test of reviewer misuse of large language models. The study identifies four types of hidden prompts, critiques the ineffectiveness of honeypot defenses, and highlights inconsistent publisher policies while calling for controlled AI integration and harmonized guidelines in academic evaluation.