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

arXiv: https://arxiv.org/abs/2604.22413 · HTML: https://mlsanity.com/paper/2604.22413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Demystifying Oversmoothing in Attention-Based Graph Neural Networks](https://mlsanity.com/paper/2305.16102/) (2023)
- [Setting the Record Straight on Transformer Oversmoothing](https://mlsanity.com/paper/2401.04301/) (2024)
- [What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding](https://mlsanity.com/paper/2406.01977/) (2024)
- [Relieving the Over-Aggregating Effect in Graph Transformers](https://mlsanity.com/paper/2510.21267/) (2025)

## Abstract (arXiv)

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.