The paper investigates how newer GPT models alter scientific text, a phenomenon termed defensive writing, where the models either retract or weaken authors’ claims. Experiments show that as GPT versions advance, defensive writing becomes more pronounced, especially when the model is prompted to anticipate reviewer concerns. The study finds that this effect is largely driven by the anticipated-review explanation rather than mere correction of overclaiming, and that while AI reviewers rate such rewrites favorably, human readers perceive them as harder to read and less confident.
By Junchi Liao
arXiv:2610.11355v2 Announce Type: replace
Abstract: Researchers increasingly use ChatGPT to revise their papers, and when asked to make a paper hold up under review, recent GPT versions often narrow...
By Junchi Liao, Xiao Huang, Yucheng Liu, Jiawen Deng, Fuji Ren
The paper introduces a harness designed to curb over‑defensive writing by agents, which occurs when agents add stronger claims than the source material and then retract them. The harness separates the reviewer, filter, and writer components, allowing the writer to focus only on problems selected by an independent filter. Evaluation on 25 business‑project drafts shows a 96.61% drop in defensive propositions and an increase in source‑claim retention from 80.35% to 93.12%.
By Xiaochen Chang
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.
By Ming Li, Chenguang Wang, Xirui Li, Xinyue Zeng, Dianqi Li, Peng Shi, Dawei Zhou, Tianyi Zhou
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. 10159v1 Announce Type: cross Abstract: AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assistance to editorial triage.
By Lin Li, Qi Zhang, Xander Davies, Jianing Qiu, Yarin Gal