arXiv:2602. 22600v2 Announce Type: replace-cross Abstract: Training selects for behavior, not circuitry: many weight configurations can implement the same function.
By Joshua S. Schiffman
The paper introduces TANGO, a Token‑Aggregated Nonlinear Gating Operator that blends cross‑token mixing and token‑wise transformation in transformer architectures. By computing a nonlinear gate per source token and averaging these gates for each destination, TANGO forms a source‑conditioned linear operator that improves predictive performance. Experiments on web text, formal mathematics, and code show that full‑prefix TANGO achieves the lowest test negative log‑likelihood across 16 settings, while a narrower variant offers substantial throughput gains with only a modest increase in loss.
By Joshua Nunley
arXiv:2601. 11618v2 Announce Type: replace-cross Abstract: Geometric Attention (GA) specifies an attention layer by four independent inputs: a finite carrier (what indices are addressable), an evidence-kernel rule (how masked proto-scores and a link induce nonnegative weights), a probe family (which observables are treated as admissible), and an anchor/update rule (which representative kernel is selected and how it is applied).
By Luis Rosario Freytes
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:2606. 00091v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) have reshaped self-supervised representation learning in vision.
By Sangdae Nam
The projection of queries and keys are central to the attention mechanism in Transformer architectures. While they are mathematically symmetric, they play different roles in attention mechanisms.