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.
arXiv:2607. 05420v1 Announce Type: cross Abstract: This study examines how alternative systems of scholarly representation identify and characterize broad public administration (PA) and artificial intelligence related public administration (AI-in-PA) scholarship.
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. This paper presents an on-going use case developed within the European project LLMs4EU and the ALT-EDIC infrastructure, aimed at adapting foundation models to SSH research practices and supporting tasks such as question answering, comparative document analysis and literature review.
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.
arXiv:2606. 08936v1 Announce Type: cross Abstract: This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\&AS), which examined how GenAI is reshaping academic search systems and research practices.
The zbMATH Open Knowledge Graph is a large-scale RDF knowledge graph that spans more than 250 years of mathematical scholarship. It goes beyond traditional bibliographic metadata by incorporating expert-curated semantic content such as reviews, keywords, subject classifications, software references, and disambiguated authorship. With 34 million entities and 168 million RDF triples, the graph enables fine-grained, historically grounded exploration of mathematical concepts, research fields, and scholarly relationships over time.
arXiv:2608. 10740v1 Announce Type: new Abstract: Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature.
arXiv:2607. 20328v1 Announce Type: cross Abstract: This study empirically analyzed generative AI as an emerging discovery pathway to academic library resources.
arXiv:2608. 07254v1 Announce Type: cross Abstract: The increasing specialization of scientific research challenges existing classification systems, which provide effective representations of broad disciplines and research topics but often fail to capture the fine-grained conceptual structure of contemporary science.
arXiv:2609.07713v1 Announce Type: new Abstract: Generative and agentic AI are reshaping both the production and evaluation of scientific research. These developments are often studied separately, as...
arXiv:2608.24559v1 Announce Type: cross Abstract: Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from moder...
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...
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.