arXiv:2604.17324v2 Announce Type: replace Abstract: Global self-attention drives modern graph transformers, yet the softmax at its core imposes a structural constraint rarely examined directly: every attention row is non-negative and sums to one, so each per-head output is a mass-conserving convex combination of value vectors. A node can never "attend to nothing." We argue this conservation constraint is a single root cause behind three pathologies usually studied in isolation: the collapse of node representations with depth (over-smoothing), a low-rank bottleneck on per-head outputs, and brit
This paper addresses a fundamental limitation in graph transformer architectures, indicating ongoing advancements in core AI research that are critical for complex data processing.
Improved graph transformer capabilities can unlock new frontiers in AI applications requiring robust relational reasoning, impacting fields from drug discovery to social network analysis.
The proposed 'Capacity-Controlled Global Attention' method offers a potential solution to existing pathologies in graph transformer behavior, suggesting more stable and powerful models in the future.
- · AI researchers
- · Machine learning developers
- · Industries relying on graph data
- · Deep learning frameworks
- · Inefficient graph transformer architectures
- · Current methods limited by over-smoothing
Graph transformers could become significantly more effective and robust for a wider range of tasks.
Enhanced graph AI might accelerate progress in areas like scientific discovery and complex system optimization.
The development of more powerful and adaptable AI agents could leverage these improved graph reasoning capabilities.
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Read at arXiv cs.LG