AI RESEARCH
The Semantic Training Gap: Ontology-Grounded Tool Architectures for Industrial AI Agent Systems
arXiv CS.AI
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ArXi:2605.11234v1 Announce Type: new Large language model (LLM)-based AI agents are increasingly deployed in manufacturing environments for analytics, quality management, and decision. These agents nstrate statistical fluency with domain terminology but lack grounded understanding of operational semantics -- the relational structure that connects equipment identifiers, process parameters, failure codes, and regulatory constraints within a specific production context. This paper identifies and formalizes the semantic.