Schema-Agnostic Graph Reasoning Agent for Hybrid Knowledge Graphs

Abstract: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.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)

arXiv:2608.15834 [cs.AI]

(or arXiv:2608.15834v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.15834 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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