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

arXiv: https://arxiv.org/abs/2610.11768 · HTML: https://mlsanity.com/paper/2610.11768/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Placed in

- LLM agents and tool use

## Builds on (most cited first, 9 of 9)

- [Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation](https://mlsanity.com/paper/2307.11019/) (2023)
- [AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance](https://mlsanity.com/paper/2506.03828/) (2025)
- [Less LLM, More Documents: Searching for Improved RAG](https://mlsanity.com/paper/2510.02657/) (2025)
- [Can Small Language Models Use What They Retrieve? An Empirical Study of Retrieval Utilization Across Model Scale](https://mlsanity.com/paper/2603.11513/) (2026)
- [Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents](https://mlsanity.com/paper/2604.00555/) (2026)
- [Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations](https://mlsanity.com/paper/2605.26874/) (2026)
- [LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction](https://mlsanity.com/paper/2507.04748/) (2025)
- [ThermoLLM: Thermodynamics-Aware HVAC Control with Spatial-Semantic Knowledge Graph](https://mlsanity.com/paper/2606.22911/) (2026)
- [Automated generation of experimentally validated digital twins for desiccant-based low-dew-point air-conditioning systems from declarative topology specifications](https://mlsanity.com/paper/2608.08967/) (2026)

## Abstract (arXiv)

Large language model (LLM) agents are beginning to operate industrial energy equipment, and what they get right depends on what they are told about the plant. Established building ontologies name many kinds of points across many sites, whereas an industrial equipment system needs few entities with much knowledge about each. This study proposes the ontology tower, a narrow-and-deep ontology of a single equipment system whose knowledge deepens in two ways: through quantities derived from the measured points by physical relations, and through lessons from the operating journal incorporated as knowledge nodes. On a real low-humidity air-handling test plant operated daily through a programmable logic controller, agents received a text projected from its tower in a preregistered evaluation of nine tasks replayed from the plant's records, using four open-weight models from 9 to about 750 billion parameters. This knowledge raised the rate at which the agents avoided the most plausible misjudgment of each task by about 20 percentage points, and the overall task score of the 9-billion-parameter model as much as that of the largest. Operating lessons were used when incorporated into the tower or placed in the prompt as records, but seldom when left in the journal behind a search tool. In live runs through an invariant safety layer, the agents brought the controlled variable into its target band in 12 of 14 runs. An ontology narrow in entities but deep in what is known about them can thus supply the knowledge that an agent for an industrial equipment system needs.