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:2602.05896v3 Announce Type: replace-cross
Abstract: Understanding what neural architectures can and cannot compute is a central challenge in the theory of AI. One of the fundamental problems in...
By Alexander Kozachinskiy, Tomasz Steifer, Przemys{\l}aw Wa{\l}\c{e}ga
arXiv:2605. 22223v2 Announce Type: replace Abstract: We study how we can leverage only a handful of characteristics of a transformer's architecture to closely predict the number of different sequences it can output, both qualitatively and quantitatively.
By Maxime Meyer, Mario Michelessa, Caroline Chaux, Vincent Y. F. Tan
arXiv:2601. 22002v5 Announce Type: replace Abstract: Transformers achieve superior performance on many tasks, but impose heavy compute and memory requirements during inference.
By Anderson de Andrade, Alon Harell, Ivan V. Baji\'c
arXiv:2609.08851v1 Announce Type: new
Abstract: Recent advancements in transformer length generalization theory enable us to reliably predict when a transformer can learn to solve a task. In particul...
By Georg Zetzsche, Hongjian Jiang, Andy Yang, Pascal Bergstr\"a{\ss}er, Marco S\"alzer, David Chiang, Anthony W. Lin
arXiv:2511. 17852v3 Announce Type: replace Abstract: Transformers can acquire Chain-of-Thought (CoT) capabilities to solve reasoning tasks via fine-tuning.
By Bochen Lyu, Yiyang Jia, Xiaohao Cai, Zhanxing Zhu