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:2607. 01233v1 Announce Type: cross Abstract: LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference.
By Ziyu Chen, Yilun Zhao, Arman Cohan
Modelpedia is an automated, LLM-assisted framework that extracts and organizes findings about AI models from published papers into a searchable public catalog. It links each finding to the relevant model, dataset, method, and concept, and has already extracted over a thousand findings from ICLR 2024 and 2025 papers. The authors invite the community to explore, contribute to, and build on this open catalog, positioning model findings as a shared foundation for the meta‑science of AI.
By Franciszek Bernat (Centre for Credible AI, Warsaw University of Technology), Dawid P{\l}udowski (Centre for Credible AI, Warsaw University of Technology), Micha{\l} Jan W{\l}odarczyk (Centre for Credible AI, Warsaw University of Technology), Luca Longo (University College Cork), Jianlong Zhou (University of Technology Sydney), Andreas Holzinger (Human-Centered AI Lab), Riccardo Guidotti (University of Pisa, ISTI-CNR), Wojciech Samek (Technical University of Berlin, Berlin Institute for the Foundations of Learning and Data), Przemys{\l}aw Biecek (Centre for Credible AI, University of Warsaw)
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.
By Enrico Daga, Valentina Tamma, Terry Payne
arXiv:2608.22974v1 Announce Type: new
Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...
By Xiaohui Zhang, Zequn Sun, Chengyuan Yang, Yuanning Cui, Lingbing Guo, Wei Hu
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