The paper introduces OAttention, a token‑level attention mechanism that assigns each token a presence coefficient based on its hidden representation. This coefficient both gates the token’s output and weights its contribution to other tokens, making zero‑vector tokens behave as true zeros and enabling exact null‑receiver, null‑source, and empty‑support properties. The authors extend this idea to local O‑components and an O‑Transformer, and demonstrate small performance changes when retrofitting a pretrained TabPFN model.
By Heyang Gong
The paper introduces a coupled query‑key transformation that jointly evolves queries and keys via an invertible coupling before the standard dot‑product scoring in attention mechanisms. Implemented as a lightweight alternating affine map, the coupling is added on top of existing attention methods and preserves the original softmax and architecture. Experiments on WikiText‑103 show that coupling improves performance when combined with Differential Attention, query‑key normalization, and Multi‑Token Attention, especially at larger model scales, while its standalone benefit diminishes with size.
By Barak Gahtan, Alex M. Bronstein
arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.
By Weinuo Ou
arXiv:2606. 08105v1 Announce Type: new Abstract: When attention concentrates on a single token, a sink, what is the model actually computing?
By Lukas Fesser, Mozes Jacobs, Thomas Fel, Andy Keller, Sham Kakade
arXiv:2607. 23050v1 Announce Type: new Abstract: Neural scaling laws describe how loss decreases as models, data, and compute grow, but they do not answer a prior question: for a fixed task, what is the minimum model capacity required to solve it?
By Byeong Hoon Yoon
arXiv:2605. 08475v3 Announce Type: replace-cross Abstract: In this paper, we study in-context kernel ridge regression (KRR) with Gaussian kernels and show, both theoretically and empirically, that a standard softmax-attention transformer can approximate the KRR predictor during its forward pass.
By Mingsong Yan, Dongyang Li, Charles Kulick, Sui Tang