arXiv:2603. 22473v2 Announce Type: replace-cross Abstract: Hybrid language models combine softmax attention with linear-time sequence mechanisms such as state-space or linear-attention layers, but the functional contribution of each component type remains insufficiently characterized.
By Hector Borobia, Elies Segu\'i-Mas, Guillermina Tormo-Carb\'o
arXiv:2606. 02288v1 Announce Type: new Abstract: Massive activation spikes in Large Language Models (LLMs) severely degrade quantization by stretching dynamic ranges.
By Yung-Chin Chen, Chung Peng Lee, Ze-Wei Liou, Naveen Verma
arXiv:2606. 02378v1 Announce Type: cross Abstract: We track the developmental trajectory of attention-head circuit formation across three 1B-class language models spanning two architecture families (dense transformer, mixture-of-experts) and two pretraining corpora (The Pile, DCLM): Pythia 1B, OLMo 1B-0724-hf, and OLMoE 1B-7B-0924.
By Yongzhong Xu
arXiv:2606. 14259v1 Announce Type: new Abstract: Prior work has identified several factors that can contribute to the performance gap between Adam and SGD, spanning data aspects, architecture design, and optimization properties.
By Chenxiang Zhang, Rustem Islamov, Enea Monzio Compagnoni, Jun Pang, Aurelien Lucchi, Antonio Orvieto
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:2606. 00926v1 Announce Type: new Abstract: Mechanistic studies of sequence models often treat layerwise state encodings as architectural traits: recurrent models concentrate readable state, attention-based models distribute it.
By Yuhang Jiang