arXiv:2609. 04046v1 Announce Type: cross Abstract: What can a single layer of self-attention compute?
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arXiv:2608. 04243v1 Announce Type: new Abstract: Multi-head attention layers produce vector representations that support multiple downstream tasks.
arXiv:2609.23094v1 Announce Type: cross Abstract: We study the number of prototypes needed to represent Boolean functions by nearest-neighbour classification. There are two distinct settings: the pro...
arXiv:2608.31067v1 Announce Type: new Abstract: Learning generalizable algorithmic computations remains a challenge for neural networks, as reflected in persistent failures on compositional and lengt...
arXiv:2606. 17319v1 Announce Type: cross Abstract: Motivated by the optimization of bounded binary black-box functions, we study the problem of learning polynomial surrogates over the Boolean hypercube.
arXiv:2610.27402v1 Announce Type: cross Abstract: Motivated by the notion of features from machine learning, we study the complexity of representing data that allows for evaluation with a given famil...
arXiv:2606. 11673v1 Announce Type: cross Abstract: Standard dot-product self-attention computes, in a single layer, only pairwise (order-2) interactions between tokens; representing a generic order-$k$ interaction is known to require either super-quadratic resources in one layer or composition across depth.