arXiv Machine Learning

Kuramoto Attention: Synchronizing Self-Attention on the Torus

arXiv:2606. 11585v1 Announce Type: new Abstract: We introduce Kuramoto attention, a self-attention layer in which each hidden coordinate is an angle.

arXiv Machine Learning
Jun 18

Attention as Frustrated Synchronization

arXiv:2606. 18694v1 Announce Type: new Abstract: A network of oscillators that synchronizes perfectly computes nothing further, so an attention architecture built from synchronization must locate its computation in structured departures from agreement.

By Joshua Nunley
arXiv AI
Jul 23

Geometric Attention: A Regime-Explicit Operator Semantics for Transformer Attention

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 Machine Learning
Jul 14

From Direction to Magnitude: How Multimodal Instruction-Tuning Reorganizes the Geometric Encoding of Identity-Specifying Prompts in Transformer Hidden States

arXiv:2607. 09842v1 Announce Type: new Abstract: We investigate whether identity-specifying system prompts produce statistically distinguishable geometric fingerprints in the hidden-state trajectories of four open-weight transformer language models spanning four post-training regimes: no training (Gemma-4-E4B base), multimodal RLHF (Gemma-4-E4B-it), RL distillation (DeepSeek-R1-Distill-Qwen-7B), and SFT (Qwen2.

By Jorge A. Castillo, Marco Torres Y\'evenes, Juan Carlos Lanas
arXiv Machine Learning
Jun 11

Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics

arXiv:2605. 04893v2 Announce Type: replace Abstract: When a language model processes a hallucinated response, its attention routing tends to fail in one of two shapes: over-concentrating on a narrow set of positions, or spreading so diffusely that relevance is diluted, and the shape of the failure carries diagnostic signal.

By Dominik Dahlem, Diego Maniloff, Mac Misiura
arXiv Machine Learning
Jun 25

Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency

arXiv:2604. 07904v2 Announce Type: replace Abstract: Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized to support flexible coordination such as feature binding.

By Mingqing Xiao, Yansen Wang, Dongqi Han, Caihua Shan, Dongsheng Li
arXiv AI
Jul 3

The Wiola Architecture for Efficient Small Language Models

arXiv:2607. 01394v1 Announce Type: new Abstract: We present Wiola, a fully original Small Language Model (SLM) architecture built from first principles, sharing no structural lineage with any existing model family including GPT, LLaMA, Mistral, or Falcon.

By Aryuemaan Kumar Chowdhury, Afreen Shaik, Yaparla Bhargavi, Brahma Kumar
arXiv Machine Learning
Aug 4

TextNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention

arXiv:2608. 02050v1 Announce Type: cross Abstract: Can a strictly local, iterated, weight-shared computation primitive support language modelling, and which of those three properties actually drives the model's behaviour?

By Avni Mittal, Avinash Anand, Ashutosh Kumar, Dikshant Kukreja, Kritarth Prasad, Sushane Dulloo, Erik Cambria, Timothy Liu, Zhengkui Wang, Rajiv Ratn Shah