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arXiv · 2604.22413

Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control

Qinhan Hou·Jing Tang
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Abstract

Graph Transformers can mix information globally, but this flexibility also creates failure modes: some tasks require long-range communication while others are better served by local interaction. We study this through a synthetic node-classification benchmark on contextual stochastic block model graphs, where labels are generated by a controllable mixture of local and far-shell signals. We define distance-misaligned training as a mismatch between where label-relevant information lies and where the model allocates communication over graph distance. On this benchmark, we find three points. First, the preferred graph-distance bias changes systematically with task locality. Second, an oracle adaptive controller, given offline access to the task-side distance target, nearly matches the best fixed bias across regimes and strongly improves over a neutral baseline on mixed and local tasks. Third, a task-agnostic zero-gap controller is weaker, indicating that adaptation alone is not enough and that the control target matters. These results suggest that distance-resolved diagnosis is useful for understanding Graph Transformer failures and for designing graph-aware control.

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Qinhan Hou, Jing Tang. 2026-04-24. Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control. https://arxiv.org/abs/2604.22413

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