← 研踪 PaperTrailarXiv:2611.09356 · v1
Challenges, Opportunities, and Misconceptions in Neurosymbolic AI
首次提交 2026-11-06 19:52
版本更新 2026-11-06 19:52
雷达观测 2026-11-09 11:19
摘要
We examine opportunities for neurosymbolic AI alongside increasingly powerful neural models, particularly with regards to efficiency, safety and interpretability. We identify two major challenges: ensuring semantic alignment between neural and symbolic components, and managing the computational cost of symbolic inference and learning. We discuss representative methodological approaches to these challenges and address several common misconceptions concerning differentiability, independence assumptions, and scalability. This paper synthesizes perspectives developed through discussions in the Neurosymbolic Programming and Reasoning (NPR) working group at the 2026 CNRS workshop Neurosymbolic Methods and Their Applications in Data Science, rather than providing an exhaustive survey.
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v1 · 2026-11-06 19:52 · 最新观测版本
Challenges, Opportunities, and Misconceptions in Neurosymbolic AI
We examine opportunities for neurosymbolic AI alongside increasingly powerful neural models, particularly with regards to efficiency, safety and interpretability. We identify two major challenges: ensuring semantic alignment between neural and symbolic components, and managing the computational cost of symbolic inference and learning. We discuss representative methodological approaches to these challenges and address several common misconceptions concerning differentiability, independence assumptions, and scalability. This paper synthesizes perspectives developed through discussions in the Neurosymbolic Programming and Reasoning (NPR) working group at the 2026 CNRS workshop Neurosymbolic Methods and Their Applications in Data Science, rather than providing an exhaustive survey.
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