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)
DTScribe automates the creation of digital twin reporting models by ingesting digital twin documents and producing both a conceptual architecture visualization and a reporting page. It extracts key details based on a twenty‑one‑characteristic framework, generates an Ontological Modeling Language description, and refines the artifact through loops involving local large language models and ontological reasoning. The study evaluates how architectural and parameter choices—such as using medium‑sized local models and multiple refinement loops—affect artifact quality, offering practical insights for practitioners.
By K\'erian Fiter, Angelica Portocarrero, Bentley Oakes
Still a long way to go, but the future is promising The post Setting Up Your Own Large Language Model appeared first on Towards Data Science .
By Ivo Bernardo
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .
By angela shi
AgentOnto is an ontology-guided large language model agent designed for enterprise data analytics. It structures heterogeneous enterprise knowledge—such as databases, business documents, and example tasks—into an ontology that directs analysis and incorporates confirmed feedback. The system improves its ontology through iterative preparation and demonstrates superior performance over existing methods on public datasets, with a real-world deployment confirming its practical value.
By Dayan Pan, Bincheng Zuo, Xiaojian Wang, Daogang Zhang, Jingyuan Wang