Task-Agnostic Experts Composition for Continual Learning

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.

@article{quarantiello2025_2506.15566,
  title={ Task-Agnostic Experts Composition for Continual Learning },
  author={ Luigi Quarantiello and Andrea Cossu and Vincenzo Lomonaco },
  journal={arXiv preprint arXiv:2506.15566},
  year={ 2025 }
}