arXiv AI By Jiawen Chen, Zichen Zhang, Bingxuan Li, Quan Sun, Yiyan Zhang, Edric Tam, Jinjie Lin, Didong Li, Yun Li, Bingxin Zhao

Positioning manuscripts in the scientific landscape with agentic AI

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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
arXiv AI
Oct 16

Do Agents Need a New Kind of Paper? SPRA: Structured Paper Representation for Agents

The paper introduces SPRA (Structured Paper Representation for Agents), a comprehensive representation of AI research papers that organizes key research elements—Problems, Contributions, Components, ExperimentSetups, Measures, and Findings—into typed relations for easier querying, traversal, and reuse. SPRA includes paper-level citation relations that capture dependencies, component usage, and comparisons, and it has been instantiated across more than one million AI papers, yielding millions of research units and internal relations. Empirical evaluations show that SPRA improves source-conditioned scientific QA and directed cross-paper reasoning over passage retrieval while significantly reducing input and output token counts, demonstrating its practicality for scientific workflows.

By Yutong Leng, Haojiong Chen, Fukai Shang, Xiaoyang Wang, Mindong Liu, Runyuan Ma, Jiantao Qiu
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