arXiv AI
SUDO: From Scientific Publications to Global Scholarly Discourse
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 12
Agents-K1: Towards Agent-native Knowledge Orchestration
arXiv:2606. 13669v1 Announce Type: new Abstract: Current LLM-based research agents have advanced through agent orchestration, yet largely overlook scientific knowledge orchestration.
arXiv Computation and Language
Sep 1
Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship
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...
arXiv AI
Aug 20
Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
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.
arXiv AI
Jun 19
Charting the Future of Scholarly Knowledge with AI: A Community Perspective
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.
Hugging Face Trending Papers
Aug 19
Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models
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 AI
Jul 8
Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH
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.