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, and compares LLM orchestration paradigms (Agent-As-Tool vs. Plan-Execute) on a fixed data layer. We ask the orthogonal question: how much does the data model behind the tools matter? We treat a typed knowledge graph as a grounding substrate and route each question by how it is best answered: (i) LLM-generated Cypher for structured retrieval, which lifts the same GPT-4 model from 65% to 82-83%; (ii) native graph and optimization primitives, with no LLM, reaching 99% on graph-answerable scenarios; and (iii) generation-augmented knowledge (GAK) for answers absent from the data -- the engine's agent materializes the missing facts as provenance-tagged graph nodes, then answers. A recurring theme is inverted LLM usage: we constrain the LLM to query generation or one-shot enrichment from a typed schema and let the graph execute deterministically. On the 88 real AssetOpsBench failure-mode scenarios the benchmark itself flags non-deterministic -- ten equipment types absent from the graph -- GAK lifts answerability from zero to 100% of equipment types and answers 81.8% of scenarios, every materialized fact tagged source:LLM-derived for auditability. We also contribute 40 graph-native scenarios. For structured operational domains the data layer -- not the LLM orchestration -- is the primary lever, and a typed knowledge graph serves as a grounding substrate between raw industrial data and LLM reasoning.

翻译：基于大语言模型的工业资产运营智能体在处理平面文档库时推理精度有限。AssetOpsBench（KDD 2026）表明，GPT-4智能体在139个工业维护场景中准确率达65%，并在固定数据层上比较了LLM编排范式（工具型智能体与计划执行型）。我们提出正交性问题：工具背后的数据模型究竟产生多大影响？将类型化知识图谱作为接地基底，根据最佳回答方式路由每个问题：（i）LLM生成的Cypher语句实现结构化检索，使同一GPT-4模型准确率从65%提升至82-83%；（ii）完全脱离LLM的原生图与优化基元，在可图回答场景中准确率达99%；（iii）针对数据中缺失答案的生成增强知识——引擎智能体将缺失事实物化为带溯源标签的图节点后作答。核心思路体现反向LLM用法：约束LLM从类型化模式生成查询或单次补全，图谱执行确定性运算。在基准测试标记为非确定性的88个真实AssetOpsBench故障模式场景（十种设备类型从图谱中缺失）中，GAK将回答覆盖率从零提升至100%设备类型，81.8%场景获得解答，每个物化事实均标注可审计的"来源:LLM衍生"。此外贡献40个原生图场景。对于结构化操作领域，数据层（而非LLM编排）是主要杠杆，类型化知识图谱可作为原始工业数据与LLM推理之间的接地基底。