

























Abstract:Transformers commonly exhibit an attention sink: disproportionately high attention to the first position. We study this behavior in GPT-2-style models with learned query biases and absolute positional embeddings. Combining structural analysis with causal interventions, validated across natural-language, mathematical, and code inputs, we find that the sink arises from the interaction among (i) a learned query bias, (ii) the first-layer MLP transformation of the positional encoding, and (iii) structure in the key projection. Crucially, each component we identify is individually dispensable: architectures omitting each of them robustly exhibit sinks. This indicates that attention sinks may arise through distinct circuits across architectures. These findings inform mitigation of sinks, and motivate broader investigation into why sinks emerge.
| Comments: | 9 pages, 8 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.14722 [cs.LG] |
| (or arXiv:2604.14722v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.14722 arXiv-issued DOI via DataCite |
From: Yuval Ran-Milo [view email]
[v1]
Thu, 16 Apr 2026 07:32:37 UTC (529 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。