Arroyo, Alvaro

9 publications

TMLR 2026 A Survey on Over-Smoothing and Over-Squashing: Unified Propagation Perspectives on Graph Neural Networks and Transformers Alvaro Arroyo, Federico Barbero, Hugh Blayney, Michael M. Bronstein, Xiaowen Dong, Pietro Lio, Razvan Pascanu, Pierre Vandergheynst

ICLR 2026 Attention Sinks and Compression Valleys in LLMs Are Two Sides of the Same Coin Enrique Queipo-de-Llano, Alvaro Arroyo, Federico Barbero, Xiaowen Dong, Michael M. Bronstein, Yann LeCun, Ravid Shwartz-Ziv

ICLR 2026 gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity Hugh Blayney, Alvaro Arroyo, Xiaowen Dong, Michael M. Bronstein

NeurIPS 2025 On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning Alvaro Arroyo, Alessio Gravina, Benjamin Gutteridge, Federico Barbero, Claudio Gallicchio, Xiaowen Dong, Michael M. Bronstein, Pierre Vandergheynst

NeurIPS 2025 Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks Ali Hariri, Alvaro Arroyo, Alessio Gravina, Moshe Eliasof, Carola-Bibiane Schönlieb, Davide Bacciu, Xiaowen Dong, Kamyar Azizzadenesheli, Pierre Vandergheynst

NeurIPS 2024 Rough Transformers: Lightweight and Continuous Time Series Modelling Through Signature Patching Fernando Moreno-Pino, Álvaro Arroyo, Harrison Waldon, Xiaowen Dong, Álvaro Cartea

ICMLW 2023 Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong

NeurIPS 2023 Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong

ICLRW 2023 Projections of Model Spaces for Latent Graph Inference Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ingmar Posner