arXiv:2608. 02071v1 Announce Type: new Abstract: Attention-only dynamical theories model Transformer residual directions as particles aggregating on a sphere.
By Timur Mudarisov, Mikhail Burtsev, Radu State
arXiv:2607. 17696v1 Announce Type: cross Abstract: We develop an adjoint-sensitivity framework for positional influence in causal residual Transformers and separate unconditional analytic results from conditional boundary-shape conclusions.
By Cheng Huan, Hongwei Yuan
arXiv:2608.30720v1 Announce Type: new
Abstract: Representational similarity is foundational to analyses of deep networks, yet distances between point-valued representations are not intrinsically tied...
By Kieran Murphy
arXiv:2609.15975v1 Announce Type: cross
Abstract: Transformer representations evolve through learned additive transformations that either preserve their current direction or redirect it. We study thi...
By Shwai He, Haichao Zhang, Shen Yan
arXiv:2608.22034v1 Announce Type: new
Abstract: Mechanistic interpretability has identified transformer circuits, but lacks a shared vocabulary for describing how their functions compose across tasks...
By Nura Aljaafari, Andre Freitas
arXiv:2609.37717v1 Announce Type: new
Abstract: Decoder-only transformers are trained only through a terminal next-token prediction loss, yet this loss constrains every intermediate hidden state thro...
By Timur Mudarisov, Mikhail Burtsev, Tatiana Petrova, Radu State