Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations 文章

arXiv:2605.26874v1 Announce Type: cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores. AssetOpsBench (KDD 2026) establishes that GPT-4 agents achieve 65% on 139 industrial maintenance scenarios backed by CouchDB, YAML, and CSV. It compares LLM orchestration paradigms (Agent-As-Tool vs Plan-Execute) on a fixed data layer; we ask a complementary, orthogonal question: how much does the data model behind the tools affect agent performance? Building on the same scenarios, we introduce a knowledge graph layer (781 nodes, 955 edges, 16 relationship types) and evaluate three architectures: (1) deterministic graph handlers (no LLM) at 99% (137/139); (2) LLM-generated Cypher over the graph at 82-83% with the same GPT-4 model the baseline uses; and (3) the original tool-augmented LLM baseline at 65% (91/139, matching the published KDD 2026 leaderboard ceiling).