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arXiv AI

greCAPTCHA: Assessing Understanding as Evidence of Research Authorship Under Generative AI

The paper introduces greCAPTCHA, a proctored assessment designed to gauge authors’ understanding of their own research manuscripts by measuring their capacity to verify content. It generates questions at multiple levels of comprehension and produces an evaluative report based on responses. A prototype study with 31 researchers showed that automated scores could predict authorship with an AUC of 0.90, and participants reported positive experiences and constructive feedback for future deployment.

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arXiv Computation and Language
Sep 16

PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress

PaperDoctor is an agent framework that provides evidence‑grounded, actionable feedback for scientific papers before submission. It evaluates writing, layout, references, code, theory, prior work, and experiments through a three‑layer hierarchical system, linking each critique to specific evidence and revision suggestions. The system selectively rebuilds and reruns experiments to uncover reproducibility gaps, and an interactive interface lets authors explore findings tied to their manuscript.

By Kevin Qinghong Lin, Siyuan Hu, Pan Lu, Yu Chen, Yanzhe Chen, Owen Queen, Yupeng Chen, Jialin Yu, Junchi Yu, Zifeng Ding, Yuanfeng Ji, Sheng Liu, Jindong Gu, Linjie Li, Mike Zheng Shou, Philip Torr, James Zou
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arXiv AI
Jul 8

Prompt-to-Paper: Agentic AI System for Bioinformatics

arXiv:2607. 05456v1 Announce Type: new Abstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) experimental results are frequently fabricated rather than executed, and (iii) there exists no standardized, multi-dimensional framework to assess whether AI-generated manuscripts meet the quality and rigor required for real-world publication.

By Ramsha Kamran, Maheera Amjad, Zartasha Mustansar, Arsalan Shaukat, Salma Sherbaz, Muhammad U. S. Khan
llmsragagentssafety
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arXiv AI
Aug 19

Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review

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.

By Zhicheng Lin
llmsnlprobotics
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