AI RESEARCH
Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix
arXiv CS.AI
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ArXi:2605.16112v1 Announce Type: cross Transformer-based architectures have become the dominant paradigm for Continuous-Time Dynamic Graph (CTDG) learning, yet their performance remains limited on temporally shifted datasets. In this work, we identify attention dispersion as a shared failure mode of dynamic graph Transformers under temporal distribution shift.