arXiv:2606. 28363v1 Announce Type: cross Abstract: Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates the complete systematic review and meta-analysis (SR/MA) workflow -- from literature search through statistical analysis, manuscript generation, and quality assurance -- with mandatory human oversight at critical decision points.
By Hsieh-Ting Lin, Jiunn-Tyng Yeh
arXiv:2607. 15247v1 Announce Type: new Abstract: Evidence synthesis is crucial for turning primary research into reliable knowledge for science, medicine, education, and policy.
By Moein Taherinezhad, Sebastian Maier, Gerardo Vitagliano, Francesco Pierri, Stefan Feuerriegel
arXiv:2607. 21859v2 Announce Type: replace Abstract: Constructing causal directed acyclic graphs (DAGs) is a core step in biomedical causal analysis, yet it remains a largely manual process.
By Yi-han Sheu, Michael R. Steigman, Yu Zhou, Bo Wang, Fan-Yu Yen, Jordan W. Smoller
arXiv:2608.28974v1 Announce Type: new
Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
By Daniel Kang, Michelle Hu, Soorya Ram Shimgekar, Shayan Vassef, Yufan Wang, Anit Kumar Sahu, Munmun De Choudhury, Vedant Das Swain, Christian Poellabauer, Li Yan Khor, Koustuv Saha, Robert Wojciechowski, Elliot Kidd, Piyum Zonooz, Navin Kumar
arXiv:2603. 22327v2 Announce Type: replace-cross Abstract: Systematic literature reviews (SLRs) are a demanding and high-stakes form of scientific knowledge synthesis that remains underspecified as an evaluation setting for large language models (LLMs).
By Shreyansh Padarha, Ryan Othniel Kearns, Tristan Naidoo, Lingyi Yang, {\L}ukasz Borchmann, Piotr B{\L}aszczyk, Christian Morgenstern, Ruth McCabe, Sangeeta Bhatia, Philip H. Torr, Jakob Foerster, Scott A. Hale, Thomas Rawson, Anne Cori, Elizaveta Semenova, Adam Mahdi
The paper evaluates browser-based large language models (LLMs) for extracting detailed, contextualized data from scientific papers. It presents four workflows: (1) expert-curated prompts yield good extraction but struggle with nuance; (2) LLMs can generate effective prompts from simple instructions; (3) autonomous literature discovery is challenging, with missing or hallucinated references; (4) LLMs can build new datasets from guidelines that align closely with human experts, yet still need human oversight. The study outlines a practical, auditable workflow where experts set standards, models cross-check extractions, and researchers resolve disputes, enabling scalable scientific data curation.