研究图Transformer在远距离与局部信息传递中的失配问题，提出自适应控制机制提升性能。
Distance-Misaligned Training in Graph Transformers and Adaptive Graph-Aware Control
- 通过合成数据测试不同任务下最优通信距离偏好
- 自适应控制器在混合与局部任务上显著优于固定策略
- 仅靠自适应不够，控制目标设计至关重要
图Transformer虽具全局信息融合能力，但其灵活性也带来失效模式：某些任务需长程通信，而另一些更依赖局部交互。本文在上下文随机块模型图上构建合成节点分类基准，标签由可控的局部与远距离信号混合生成。定义距离错配训练为标签相关信号位置与模型通信距离分配之间的不匹配。实验发现：第一，任务局部性变化时，模型偏好通信距离系统性改变；第二，给定任务侧距离目标的最优自适应控制器，在各类场景中几乎达到最佳固定偏置表现，且在混合与局部任务上显著优于中性基线；第三，无任务感知的零间隙控制器表现较弱，说明仅靠自适应不足以提升性能，控制目标的设计同样关键。结果表明，距离解析诊断有助于理解图Transformer失败机制，并指导图感知控制设计。
原文摘要 · Abstract (English)
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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