Energy-Gated Attention: Spectral Salience as an Inductive Bias for Transformer Attention
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Computer Science > Machine Learning
arXiv:2605.21842 (cs)
[Submitted on 21 May 2026]
Title:Energy-Gated Attention: Spectral Salience as an Inductive Bias for Transformer Attention
Authors:Athanasios Zeris
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Abstract:Standard transformer attention computes pairwise similarity between queries and keys, treating all tokens as equally salient regardless of their intrinsic informational content. In turbulent fluid dynamics, coherent structures -- the energetically dominant, spatially organized patterns that persist amid background chaos -- carry a disproportionate fraction of total energy and govern all transport. We propose that tokens play an analogous role in transformer attention: informationally dense positions (morphological boundaries, syntactic heads, discourse markers) concentrate spectral energy and should attract proportionally more attention than background tokens (function words, repeated patterns, low-information filler). We propose Energy-Gated Attention (EGA): a simple modification that gates value aggregation by the spectral energy of key token embeddings, computed by a single learned linear projection that discovers the dominant spectral mode of the embedding field. On TinyShakespeare, EGA achieves +0.103 validation loss improvement with only 12,480 additional parameters (<0.26% overhead) and no measurable computational cost. The result is consistent on Penn Treebank (+0.101), demonstrating dataset independence. A systematic ablation across three wavelet families (fixed Morlet, Daubechies db2/db4, and a parametric Morlet) establishes that fixed structured bases are suboptimal -- the optimal energy direction is data-adaptive and non-sinusoidal -- while identifying learned wavelet packets as a promising open direction. The learned energy threshold converges to tau ~= 0.35 independently of initialization, corresponding to the fraction (~36%) of tokens carrying above-average spectral energy in English text, a stable linguistic property consistent with the fraction of content words in running English text.
| Comments: | 12 pages, 4 figures |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL); Signal Processing (eess.SP) |
| Cite as: | arXiv:2605.21842 [cs.LG] |
| (or arXiv:2605.21842v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21842
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Athanasios Zeris [view email][v1] Thu, 21 May 2026 00:21:14 UTC (1,275 KB)
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