Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 2861-2872· 0 citations· 12 references
Abstract
Recent advancements in neuro-symbolic learning (NeSy) have shown significant promise in integrating deep learning with symbolic reasoning, offering both interpretability and generalization. However, the prevalence of reasoning shortcuts, where the NeSy system predicts incorrect intermediate concepts while maintaining high final accuracy, poses a substantial challenge. This is especially problematic in domains requiring reliable and transparent decision-making. Inspired by recent theories, we find that existing methods fail to address the reasoning shortcut issue when the knowledge base lacks sufficient complexity, highlighting their vulnerability in real-world applications. In this work, we present a novel method called DKA to address this issue. It 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. Theoretical analysis reveals that DKA can reduce shortcut risks with improved data efficiency. Empirical studies across multiple tasks within various neuro-symbolic frameworks also verify the effectiveness of the DKA method.
This overview addresses this issue by providing a gentle introduction to RSs, discussing their causes and consequences in intuitive terms, and details methods for dealing with RSs, including mitigation and awareness strategies, and maps their benefits and limitations.
E. Marconato, Samuele Bortolotti, Emile van Krieken et al.· Journal of Artificial Intell...· 1 citation
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.
The framework is operator-agnostic - it decouples logical structure from the choice of underlying continuous semantics, revealing, through the first controlled comparison of its kind, that this choice alone can swing performance by over 30 points on the same task.
Zi-Han Shao, Chang Lu, Renate A. Schmidt et al.· Proceedings of the 32nd ACM...· 0 citations
A novel and interpretable approach to perform symbolic integration using deep learning through integral rule prediction to speed up the search and introduces the first-of-its-kind symbolic integration rules dataset comprising two million distinct functions and integration rule pairs.
A fine-tuning-based Stratified Consistency Distillation approach that shows significant and consistent improvements in both Pass@K and the novel Equivalent Logical Similarity metrics, demonstrating the potential of advancing logical translation through consistency distillation.
Zhi-Chao Hou, Ferhat Erata, Joseph Lilien et al.· 2 citations
Lite-CoNER is proposed, a lightweight NER framework that achieves an effective balance between recognition accuracy and inference efficiency and provides a transparent view of the decision-making process, proving that lightweight models can effectively inherit complex logic through structured distillation.
Yang Wang, Lushuang Gao· International Conference on...· 0 citations
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