Graph Serializations for Reasoning LLMs: A Study Across Knowledge Extraction and Structural Reasoning

arXiv:2610.22734v1 Announce Type: new Abstract: The way a knowledge graph is serialized as text for inclusion into LLM context can substantially affect downstream LLM performance, with prior work reporting tens of percentage points variations across formats. However, existing studies consider only a limited set of representations and predate frontier reasoning models, which now offer stronger reasoning, instruction-following, and long-context capabilities. We evaluate 15 graph representation formats across nine frontier LLM configurations on ontology-based knowledge extraction and structural g

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