Spectral Outliers Reveal Dominant Learned Structure in Transformer Attention
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Computer Science > Machine Learning
Title:Spectral Outliers Reveal Dominant Learned Structure in Transformer Attention
Abstract:We apply Marchenko-Pastur (MP) random matrix theory to pre-trained attention weights in order to separate each projection matrix into a random-like bulk and a set of spectral outliers. We validate this decomposition causally: zeroing the MP-identified outliers (signal) in Mistral-7B drives HellaSwag, MMLU, and PIQA close to random-chance performance, whereas zeroing a count-matched subset of bulk singular values causes smaller but non-negligible degradation. Across 11 pre-trained transformers we identify five recurring patterns: spectral outliers encode a dominant component of the learned structure; Q projections carry the most outliers; V projections under grouped-query attention lack a clean signal/noise separation; entry-level outliers form structured row-bands in Q and column-bands in O; and specific residual-stream dimensions persist as band outliers across layers in K and O. We close by outlining how these observations could inform parameter-efficient fine-tuning and structured pruning.
| Comments: | Accepted at the International Conference on Machine Learning and Applications (ICMLA 2026); to appear in IEEE proceedings |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.07921 [cs.LG] |
| (or arXiv:2608.07921v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.07921
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Kasun Dewage Dewage [view email][v1] Sat, 8 Aug 2026 04:56:11 UTC (9,211 KB)
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