The authors identify the "semantic training gap": LLMs learn manufacturing vocabulary statistically but lack grounded understanding of operational semantics—the relational structure linking equipment IDs, failure codes, and process parameters. They propose embedding manufacturing ontology directly into the AI tool layer as a typed relational configuration, enforcing semantic constraints at runtime instead of relying on model training. In a controlled experiment with Qwen3-32B, unconstrained tool parameters hallucinated domain identifiers 43% of the time; ontology-grounded parameters reduced this to 0%.
Main takeaways:
- LLMs can use domain vocabulary fluently but make operationally incorrect inferences because they lack grounded relational semantics.
- Multi-agent systems compound this into "semantic drift"—errors propagate across agents when each lacks operational grounding.
- Proposes a three-operation interface (resolve, contextualize, annotate) with invariants enforced by an orchestration layer.
- Controlled experiment: 43% hallucination rate for unconstrained tool parameters, 0% with ontology-grounded constraints.
- The 0% rate is an architectural guarantee from runtime enforcement, not model-dependent learning.