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cs.LG2026
Length Generalization Needs Proper Regularization
Pavlo Vasylenko, Matthias Lindemann, André F. T. Martins +1
Length generalization is the ability of sequential models to perform well on context lengths unseen during training. In this work, we show that the challenge of achieving length ge…
cs.LG2026
AdaSplash-2: Faster Differentiable Sparse Attention
Nuno Gonçalves, Hugo Pitorro, Vlad Niculae +4
Sparse attention has been proposed as a way to alleviate the quadratic cost of transformers, a central bottleneck in long-context training. A promising line of work is -entmax a…
cs.LG2025
TowerVision: Understanding and Improving Multilinguality in Vision-Language Models
André G. Viveiros, Patrick Fernandes, Saul Santos +7
Despite significant advances in vision-language models (VLMs), most existing work follows an English-centric design process, limiting their effectiveness in multilingual settings.…