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

Parity, Sensitivity, and Transformers

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

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
Oct 3

Universal interpolation for deep residual self-attention networks

The paper proves that deep residual self‑attention networks can universally interpolate between any two collections of sequences using only two fixed single‑head attention blocks with Gaussian‑initialized projections. The interpolation is achieved by varying the order, signs, and durations of these blocks, independent of the specific input and output sequences. The result holds for both continuous and finite depth, and the authors also extend the analysis to causal‑masked settings.

By Sibylle Marcotte, Joan Bruna
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