Relational inductive biases on attention mechanisms

Víctor Mijangos; Ximena Gutierrez-Vasques; Verónica E. Arriola; Ulises Rodríguez-Domínguez; Alexis Cervantes; José Luis Almanzara

arXiv · 2507.04117

Relational inductive biases on attention mechanisms

Víctor Mijangos·Ximena Gutierrez-Vasques·Verónica E. Arriola·Ulises Rodríguez-Domínguez·Alexis Cervantes·José Luis Almanzara

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Abstract

Inductive learning aims to construct general models from specific examples, guided by biases that influence hypothesis selection and determine generalization capacity. In this work, we focus on characterizing the relational inductive biases present in attention mechanisms, understood as assumptions about the underlying relationships between data elements. From the perspective of geometric deep learning, we analyze the most common attention mechanisms in terms of their equivariance properties with respect to permutation subgroups, which allows us to propose a classification based on their relational biases. Under this perspective, we show that different attention layers are characterized by the underlying relationships they assume on the input data.

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Víctor Mijangos, Ximena Gutierrez-Vasques, Verónica E. Arriola, Ulises Rodríguez-Domínguez, Alexis Cervantes, José Luis Almanzara. 2025-07-05. Relational inductive biases on attention mechanisms. https://arxiv.org/abs/2507.04117

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