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Graph Evidence Is Not Enough: Diagnosing Native Decoder Use in Graph-Augmented LLMs

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Graph-augmented large language models often assume that graph evidence produced by external computation and placed in the input can be used by the native decoder. We test this assumption with HopQA, a deliberately bounded diagnostic that asks for the shortest-hop distance between two query nodes. Because the answer is a small integer and the target is purely topological, failure cannot be dismissed as open-ended generation or ambiguous evaluation. Yet existing graph-augmented baselines still fail on this setting, showing that providing graph evidence is not the same as making it usable. We int

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