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Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
Sajratul Y. Rubaiat, Hasan M. Jamil
https://arxiv.org/abs/2506.17580

Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
The exponential growth of scientific literature challenges researchers extracting and synthesizing knowledge. Traditional search engines return many sources without direct, detailed answers, while general-purpose LLMs may offer concise responses that lack depth or omit current information. LLMs with search capabilities are also limited by context window, yielding short, incomplete answers. This paper introduces WISE (Workflow for Intelligent Scientific Knowledge Extraction), a system addressing…

SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
Chun Xie, Yuichi Yoshii, Itaru Kitahara
https://arxiv.org/abs/2507.05148 https://

SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
X-ray imaging is a rapid and cost-effective tool for visualizing internal human anatomy. While multi-view X-ray imaging provides complementary information that enhances diagnosis, intervention, and education, acquiring images from multiple angles increases radiation exposure and complicates clinical workflows. To address these challenges, we propose a novel view-conditioned diffusion model for synthesizing multi-view X-ray images from a single view. Unlike prior methods, which are limited in an…

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Haoxuan Zhang, Ruochi Li, Yang Zhang, Ting Xiao, Jiangping Chen, Junhua Ding, Haihua Chen
https://arxiv.org/abs/2507.11810

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs). As science faces mounting challenges including information overload, disciplinary silos, and diminishing returns on conventional research methods, LLMs are emerging as powerful agents capable not only of enhancing scientific workflows but also of participating in and potentially leading the innovation process. Existing surveys mainly focus on different perspectives, phrases, and…