arXiv:2606. 17399v1 Announce Type: cross Abstract: When small transformers grok modular multiplication, prior work reports that the learned embedding has a "dense" Fourier spectrum requiring all frequencies.
By Huu Danh Nguyen (Stanford University)
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...
By Takuya Ito, Ruchir Puri, Murray Campbell, Parikshit Ram
arXiv:2607. 17843v1 Announce Type: new Abstract: Transformer-based language models organize computation along an ordered depth axis, where shallow and deep blocks often develop distinct representational roles.
By Tongtian Zhu
arXiv:2609.37921v1 Announce Type: new
Abstract: What are the inductive biases of a Transformer architecture? Existing theory on how the forward pass shapes representations either considers whether Tr...
By Erkan Turan, Gaspard Abel, Maks Ovsjanikov
arXiv:2607. 11875v1 Announce Type: cross Abstract: We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models.
By Tiberiu Musat, Tiago Pimentel, Nicholas Zucchet, Thomas Hofmann
We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous works on Transformer learning dynamics have so far been mostly tied to specific tasks, we study a generalized class of inductive tasks that unifies several synthetic tasks known in the literature, including in-context n-grams and multi-hop reasoning.