用通用工具在混合知识图谱中智能导航，少读文本却答得更准。
Schema-Agnostic Graph Reasoning Agent for Hybrid Knowledge Graphs
- 用7个通用操作探索混合知识图谱，动态发现领域信息。
- 在258个工业问题上准确率达88.4%，比全上下文模型高5.1个百分点。
- 适合需要高效处理结构化数据的智能问答系统开发者。
工具调用的大语言模型代理能通过少量通用指令（如列出、读取、搜索文件）在陌生代码库中导航。知识图谱也具备相同接口：列出邻居、读取节点内容、搜索描述，只是对象不同。基于此，我们提出GRA——一种图推理代理，通过七种通用工具探索混合知识图谱（节点为文本概念或关系表），在运行时动态发现领域特定信息。在UFK-M（统一工厂知识模型）工业基准上，该模型回答258个分析问题的准确率为88.4%，优于全上下文代理（83.3%），且仅需其输入token的三分之一。无图控制实验表明，性能提升主要源于代理的有选择性访问，而非图结构本身，且依赖于能可靠驱动工具的模型。看得越少，答得越好：对结构化底座的有选择性导航优于全量上下文遍历。
原文摘要 · Abstract (English)
Tool-calling LLM agents navigate unfamiliar codebases with a handful of generic primitives for listing, reading and searching files (ls, cat, grep). A knowledge graph admits the same interface: listing neighbours, reading node content and searching descriptions are the same operations on a different substrate. Building on this correspondence, we present GRA, a Graph Reasoning Agent that explores hybrid knowledge graphs, whose nodes are either textual concepts or relational tables, with seven generic tools, discovering everything domain-specific at run time. On UFK-M (Unified Factory Knowledge Model), an industrial benchmark of 258 analytical questions whose gold answers are produced by executing validated SQL programs, GRA beats a full-context agent by 5.1 pp (88.4% vs. 83.3%), while reading under a third of its input tokens. A graph-free control shows the gain comes chiefly from selective agentic access rather than graph topology, and that the effect depends on a model able to drive tools reliably. Seeing less, the agent answers better: selective navigation over a structured substrate beats exhaustive context.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。