Knowledge Graphs Boost LLM Accuracy for Industrial Asset Operations from 65% to 99%

A new study introduces a knowledge graph layer that dramatically improves the accuracy of LLM-based agents for industrial asset operations. The research, published on arXiv, builds on the AssetOpsBench benchmark from KDD 2026, which previously established that GPT-4 agents achieve only 65% accuracy on 139 industrial maintenance scenarios using flat document stores (CouchDB, YAML, CSV). The authors propose a complementary approach: instead of changing the LLM orchestration paradigm, they modify the underlying data model. They construct a knowledge graph with 781 nodes, 955 edges, and 16 relationship types representing the same scenarios. Three architectures are evaluated: deterministic graph handlers (no LLM) achieve 99% accuracy (137/139); LLM-generated Cypher queries over the graph yield 82-83% with the same GPT-4 model; and the original tool-augmented LLM baseline remains at 65% (91/139), matching the published KDD 2026 leaderboard ceiling. The key finding is inverted LLM usage: rather than relying on the LLM for reasoning over raw data, the graph structure enables precise retrieval, minimizing LLM involvement. This suggests that the data model behind tools is a critical, often overlooked factor in agent performance.