Accelerating NeurASP with vectorization and caching
Neurosymbolic AI combines neural networks with symbolic programs to create robust and explainable predictions. One such framework is NeurASP, which trains a neural network to predict concepts and reasons over them using rules written in answer set programming (ASP) to solve downstream tasks. Crucially, labels are only provided for the downstream prediction produced by the symbolic rules, not for the latent concepts themselves. Backpropagation through the non-differentiable ASP component requires expensive probability and gradient calculations, which has hindered scalability to more sophisticated tasks. In this paper, we address the current limitations of NeurASP by improving its computati...
| Authors | Alexander Philipp Rader, Alessandra Russo |
|---|---|
| Primary category | cs.AI |
| Published | 9 June 2026 |
| Last updated at source | 18 June 2026 |
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