arXiv AI By Alexander Kozachinskiy, Tomasz Steifer, Przemys{\l}aw Wa{\l}\c{e}ga

Parity, Sensitivity, and Transformers

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arXiv Machine Learning
Jul 7

Legible-by-Construction: Attention and End-to-End Transformers

arXiv:2607. 04319v1 Announce Type: cross Abstract: A companion paper showed that a transformer's feed-forward layer can be rebuilt from explicit fuzzy set operations - intersection, set-difference, and a self-forgetting sequence quantifier - so its hidden units read as named logical operators at no cost to language-model quality.

By Mark Oskin
arXiv Machine Learning
Jul 27

Indexing: the Beginning and the End

arXiv:2607. 22361v1 Announce Type: new Abstract: We study information bottlenecks in modern deep-learning architectures -- RNNs, softmax transformers, linear-attention transformers and state-space models -- through the lens of the indexing primitive.

By Alexander Kozachinskiy, Vicente Opazo, Felipe Urrutia
arXiv Machine Learning
Sep 2

Performance-Efficiency Tradeoffs in Transformers: An Approximation Theory Perspective

The paper examines how to allocate attention heads and head dimensions across Transformer layers to balance expressivity and efficiency. It provides a mathematical analysis of early layers’ role in information extraction and characterizes the trade‑off between head count and dimension under a fixed parameter budget. The authors prove a saturation effect of softmax activations, showing that increasing head dimensions yields diminishing returns, especially for long sequences, and propose strategies for efficient parameter allocation across layers.

By Ruoxi Yu, Haotian Jiang, Jingpu Cheng, Penghao Yu, Qianxiao Li, Zhong Li