A systematic literature review and an original taxonomy-driven analysis of open-source development frameworks for building neuro-symbolic software systems identify open challenges related to balancing expressiveness and scalability, improving engineering maturity, and performing comparisons over standard benchmarks.
Neuro-Symbolic (NeSy) predictors incorporate prior knowledge into the prediction process of neural networks, ensuring that outputs satisfy specified constraints, making them particularly suitable for high-stakes applications where compliance with domain knowledge is essential. A key bottleneck in this paradigm is the a...
Samuele Bortolotti, Wei-Xin Chen, Han Zhao et al.· 0 citations
A neuro-symbolic framework for automatic CA that significantly reduces user involvement by introducing neural Oracle Transformer models which learn to emulate user responses and to generalize conceptual knowledge is proposed.
Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar et al.· 0 citations
PolyFlow is a neural-symbolic framework for statically reasoning about information flow across language boundaries, combining large language models (LLMs) and static analysis synergistically, and is cost-effective and superior to various kinds of state-of-the-art baselines.
Haoran Yang, Zhi-Xuan Zhong, Jiawei Guo et al.· 0 citations
A novel method called DKA is presented, which introduces a limited set of concept-supervised data to enhance the knowledge base, effectively solving the reasoning shortcut problem and improving the applicability of the NeSy system.
Yu-Feng Li, Xiao-Wen Yang, Wenda Wei et al.· Proceedings of the 32nd ACM...· 0 citations
This work develops a mathematical formalization that is grounded in Category Theory, in order to enable proof-based guarantees of properties of neuro-symbolic systems, and advances the theoretical foundations of neuro-symbolic AI by supporting principled design of HI systems.
Johannes E. Bendler· Proceedings of the Thirty-Fi...· 0 citations
