arXiv:2607. 05956v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity, multilingual access to sources and the evaluation of results.
By Adam Faci, Alessio Miaschi, Anne Combe, Pascal Cuxac, Francesca Frontini, Nicolas Larrousse, St\'ephane Pouyllau
arXiv:2605.30947v4 Announce Type: replace
Abstract: LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and qu...
By Yating Pan, Jiajun Zhang, Jun Wang, Qi Su
arXiv:2509. 02581v2 Announce Type: replace-cross Abstract: Despite the growing availability of tools designed to support scholarly knowledge extraction and organization, many researchers still rely on manual methods, sometimes due to unfamiliarity with existing technologies or limited access to domain-adapted solutions.
By Azanzi Jiomekong, Hande K\"u\c{c}\"uk McGinty, Keith G. Mills, Allard Oelen, Enayat Rajabi, Harry McElroy, Antrea Christou, Anmol Saini, Janice Anta Zebaze, Hannah Kim, Anna M. Jacyszyn, Gollam Rabby, Dirk Betz, Claudia Biniossek, Sanju Tiwari, S\"oren Auer
arXiv:2607. 21327v1 Announce Type: cross Abstract: Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI) analytics, yet suffer from temporal lag, semantic shallowness, and an inability to capture the non-linear dynamics of contemporary knowledge ecosystems.
By Muhsen Hammoud
The paper investigates how large language models can extract contextualized data from scientific literature. It presents four workflows: expert‑written prompts, self‑generated prompts, autonomous literature discovery, and dataset creation from guidelines. While models perform well with prompts, they struggle with context, hallucinate references, and still need human oversight for final validation.
By Valentin Romanov, Monique Bax, Steven Niederer
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.
arXiv:2607. 26670v1 Announce Type: cross Abstract: The rapid growth of scholarly literature has made identifying relevant publications increasingly difficult, and conventional search systems still depend heavily on manually formulated queries and effortful manual inspection.
By Eleni Adamidi, Serafeim Chatzopoulos, Thanasis Vergoulis
Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language access to scientific kno...
The study evaluates literature reviews produced by large language models (LLMs) using short and long context windows, assessing their quality across 15 dimensions. Results show that while larger context windows allow LLMs to incorporate more information and maintain coherence, they also increase repetition, omission of key works, and a tendency toward descriptive rather than synthetic content. Human oversight remains essential for meeting academic publishing standards, and the authors suggest future work should blend human expertise with AI to mitigate these limitations.
By Muhammad Ali Chaudhry, Xinyuan Hao, Haifa Alwahaby
arXiv:2609.16519v1 Announce Type: new
Abstract: Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that...
By Bernie Boscoe, Srinath Saikrishnan, Vikram Seenivasan, Jack Stark, Andrew Lizarraga, Morgan Himes, Jonathan Soriano, PJ Allen, Tuan Do
arXiv:2609.23735v2 Announce Type: new
Abstract: Scientific agents support a range of literature-based research tasks, such as retrieval, question answering, evidence-grounded generation, and claim as...
By ScholarSeed AI Team, Caoqinwei Gong, Xue Jiang, Wei Luo, Xiaoyu Qiu, Jiayi Sheng, Yi Wang, Zheng Yu, Ao Zhang, Haifan Zhang, Hanwei Zhang, Jihai Zhang, Yuan Cao, Wei Chen, Liyun Dai, Wenkai Fang, Guanglei Wang, Kai Ying, Tingyu Zhu, Wotao Yin
Scholar assessment plays a fundamental role in faculty recruitment, funding allocation, academic promotion, and talent discovery. Existing scholar assessment methods predominantly rely on bibliometric indicators and reputation proxies, while recent large language model (LLM)-based approaches mainly focus on evaluating individual research papers rather than comprehensively assessing scholars.