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arXiv · 2506.15566

Task-Agnostic Experts Composition for Continual Learning

Luigi Quarantiello·Andrea Cossu·Vincenzo Lomonaco
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Abstract

Compositionality is one of the fundamental abilities of the human reasoning process, that allows to decompose a complex problem into simpler elements. Such property is crucial also for neural networks, especially when aiming for a more efficient and sustainable AI framework. We propose a compositional approach by ensembling zero-shot a set of expert models, assessing our methodology using a challenging benchmark, designed to test compositionality capabilities. We show that our Expert Composition method is able to achieve a much higher accuracy than baseline algorithms while requiring less computational resources, hence being more efficient.

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Luigi Quarantiello, Andrea Cossu, Vincenzo Lomonaco. 2025-06-18. Task-Agnostic Experts Composition for Continual Learning. https://arxiv.org/abs/2506.15566

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