Narrow and Deep: An Ontology Tower as the Knowledge of an LLM Agent for an Industrial Equipment System

Key points

The authors proposed an ontology tower for a single equipment system, which deepens through quantities derived from measured points by physical relations and lessons incorporated from the operating journal.
The studied tower consists of 162 nodes and 217 edges, holding 42 tags, 34 assumptions, 33 bindings, 24 knowledge nodes, 16 derived quantities, and 13 plant elements.
In a preregistered evaluation, the text projected from the tower raised the primary-trap avoidance rate of the four models from 0.605 to 0.802.
With the projected text, the 9-billion-parameter model scored above the largest model without it, at 0.737 against 0.672.
Lessons left in the operating journal were seldom used by the agents, and were used when incorporated into the tower or placed in the prompt as records.
In live runs the agents brought the controlled variable into its target band in 12 of 14 runs, and no setpoint changed except through the safety layer.

External research summaries. These are not HDATF publications or measured product results.