How to Report for 100+ Clients With One Semantic Model

Open-sourcing AstaBrief, the fast report-generation model in Asta

Modelpedia: A Catalog of Model Findings for the Meta-Science of AI

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.

DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models

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.

Setting Up Your Own Large Language Model

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 .

A Production RAG Pipeline in Action: Every Answer Typed and Cited

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 .

AgentOnto: Ontology-Guided LLM Agents for Enterprise Data Analytics

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.

AgentOnto: Ontology-Guided LLM Agents for Enterprise Data Analytics

Enterprise data analysis requires agents to connect analytical requests with knowledge distributed across databases, business documents, and existing example tasks. Without an explicit representation...

How Many Stories Can Your Data Tell?

The article explores how different data representations can alter our interpretation of the same dataset, highlighting that the way data is visualized or structured can lead to varying narratives. It examines the implications of these storytelling choices for data analysis and communication. The piece emphasizes the importance of thoughtful data presentation in shaping conclusions.

PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models

arXiv:2606. 08926v1 Announce Type: new Abstract: Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring different evaluation perspectives.

Retrieval-Augmented Generation of Ontologies from Relational Databases

arXiv:2506. 01232v2 Announce Type: replace-cross Abstract: Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning.

UniDataAgent: An Ontology-Grounded Agent for Enterprise Question-to-Report Automation

UniDataAgent (UniDataAgent) is an ontology‑grounded system designed to automate enterprise question‑to‑report tasks while preserving organization‑specific semantics. It separates semantic acquisition from online execution, with an Ontology Acquisition and Validation (OAV) stage that builds versioned ontologies from metadata, business knowledge, and expert input, and a Question‑to‑Report Execution (QRE) stage that retrieves semantic contracts, coordinates skills and data tools, validates results, and produces evidence‑linked reports. In a deployment across 27 enterprise tables and thousands of metric types, ontology construction took a few hours versus a week manually, and report generation took minutes versus several working days, achieving 95.0% strict accuracy on real business questions compared to 72.5% for document RAG.

POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation

arXiv:2606. 23533v2 Announce Type: replace Abstract: Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict formatting and structural rules.