TPR-Attention for Combinatorial Generalization

Melisa Civelekoğlu; Isabeau Prémont-Schwarz

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arXiv · 2608.30124

TPR-Attention for Combinatorial Generalization

Melisa Civelekoğlu·Isabeau Prémont-Schwarz

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Abstract

Systematic generalization remains a significant challenge in deep learning. In particular, combinatorial generalization - generalizing to new configurations of known factors of variation - is effortless for humans but difficult for standard neural architectures that rely on statistical correlations rather than explicit structural representations. We introduce a new architectural component that embeds structured inductive bias into deep learning: an attention mechanism operating over tensor-product representations (TPRs). Through controlled experiments on compositional tasks, we show that this TPR-attention mechanism outperforms existing architectural components in combinatorial generalization. These results highlight the value of integrating explicit compositional structure into neural attention and point toward a promising path for models capable of systematic generalization.

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Melisa Civelekoğlu, Isabeau Prémont-Schwarz. 2026-08-31. TPR-Attention for Combinatorial Generalization. https://arxiv.org/abs/2608.30124

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