AI RESEARCH
Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
arXiv CS.LG
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ArXi:2605.18202v1 Announce Type: new Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enhanced reliability in high-stakes applications. They work by first extracting high-level concepts from the input and then inferring a task label from these compatibly with given logical constraints. Yet, their label and concept predictions can be overconfident, making it difficult for stakeholders to gauge when the model's decisions can be trusted.