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The paper introduces the "ontology tower," a narrow-and-deep ontology designed for a single industrial equipment system. By enriching knowledge through derived quantities and operational lessons, the ontology improves large language model agents’ performance on plant tasks, raising error avoidance by ~20 percentage points and matching the largest models’ scores. In live safety tests, agents guided the system’s controlled variable into target ranges in 12 of 14 runs.
The paper presents a knowledge layer that reorganizes enterprise Retrieval-Augmented Generation (RAG) for multi‑agent systems by placing an ontology at the artifact level rather than on extracted entities. Using a seven‑agent reverse‑engineering pipeline, the authors processed 241 repositories across 18 services to generate thousands of artifacts that capture scope, journeys, rules, and acceptance criteria, each tagged with provenance metadata. The resulting self‑describing catalog, sparse metadata registry, and navigation hypergraph enable queries to resolve to artifact identifiers, thereby structurally disambiguating near‑identical rules and improving retrieval accuracy. The system was built in under a week and helped root‑cause over 100 production defects and attribute 35 release regressions to specific rules and services.
arXiv:2608.23370v1 Announce Type: new Abstract: Large Language Models (LLMs) recognise patterns but do not natively track the path of exclusions that a coherent discourse demands. When an input rests...
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.
The paper investigates how to synchronize operator manuals with tractor-side assistants over ISO 11783 CAN buses, enabling offline, multilingual LLM assistance. It benchmarks data transfer using Markdown, JSON, and XML, finding that Markdown files transfer fastest and that retrieval‑augmented generation works well for English but less so for other languages. The study shows that a new parameter group can be added to existing ISO 11783 mechanisms, suggesting a path toward standardization.
arXiv:2607. 11948v1 Announce Type: new Abstract: Regulated financial institutions operating under data-residency rules need tenant-owned language models that can run inside the institution's perimeter.