arXiv AI

Knowledge Graph and Accurate Portrait Construction of Scientific and Technological Academic Conferences

arXiv:2204. 04888v2 Announce Type: replace-cross Abstract: In recent years, with the continuous progress of science and technology, the number of scientific research achievements has increased rapidly.

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.

By Zongsheng Cao, Bihao Zhan, Jinxin Shi, Jiong Wang, Fangchen Yu, Zhijie Zhong, Zijie Guo, Tianshuo Peng, Zhuo Liu, Yi Xie, Xiang Zhuang, Yue Fan, Runmin Ma, Shiyang Feng, Xiangchao Yan, Anran Liu, Peng Ye, Wenlong Zhang, Shufei Zhang, Chunfeng Song, Fenghua Ling, Jie Zhou, Liang He, Bo Zhang, Lei Bai
arXiv AI
Aug 10

SCALE: Scientific Concept Aggregation via LLMs and Embeddings for Fine-Grained Taxonomy Extension

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.

By Daniele Raimondi, Feichi Lu, Oliver Grun, Mariia Eremina, Andrea Perlato
arXiv Machine Learning
Jun 9

GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

arXiv:2603. 24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources.

By Ruizhong Miao, Yuying Wang, Rongguang Wang, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth
arXiv AI
Jul 24

From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics

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