[2603.30033] Tucker Attention: A generalization of approximate attention mechanisms

Computer Science > Machine Learning arXiv:2603.30033 (cs) [Submitted on 31 Mar 2026] Title:Tucker Attention: A generalization of approximate attention mechanisms Authors:Timon Klein, Jonas Kusch, Sebastian Sager, Stefan Schnake, Steffen Schotthöfer View a PDF of the paper titled Tucker Attention: A generalization of approximate attention mechanisms, by Timon Klein and 4 other authors View PDF HTML (experimental) Abstract:The pursuit of reducing the memory footprint of the self-attention mechanism in multi-headed self attention (MHA) spawned a rich portfolio of methods, e.g., group-query attention (GQA) and multi-head latent attention (MLA). The methods leverage specialized low-rank factorizations across embedding dimensions or attention heads. From the point of view of classical low-rank approximation, these methods are unconventional and raise questions of which objects they really approximate and how to interpret the low-rank behavior of the resulting representations. To answer these questions, this work proposes a generalized view on the weight objects in the self-attention layer and a factorization strategy, which allows us to construct a parameter efficient scheme, called Tucker Attention. Tucker Attention requires an order of magnitude fewer parameters for comparable validation metrics, compared to GQA and MLA, as evaluated in LLM and ViT test cases. Additionally, Tucker Attention~encompasses GQA, MLA, MHA as special cases and is fully compatible with flash-attention...