arXiv (All)
open access
Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models
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 compa
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach#1000227
- Autonomous Assessment of Generalizability of AI Agent Capabilities#1110150
- S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation#622536
- LifeMem: Enabling Lifelong Experience Reuse for LLM Agents#1070141
- Principles of Concept Representation in Sentence Encoders#266260
Record · ID 973178
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive