Computer Science / Artificial Intelligence

An Agentic Framework for Neuro-Symbolic Programming

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📝 Original Info

  • Title: An Agentic Framework for Neuro-Symbolic Programming
  • ArXiv ID: 2601.00743
  • Date: 2026-01-02
  • Authors: Aliakbar Nafar, Chetan Chigurupati, Danial Kamali, Hamid Karimian, Parisa Kordjamshidi

📝 Abstract

Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient. Still, it remains a time-consuming and challenging task. Existing frameworks like DomiKnowS help this integration by providing a high-level declarative programming interface, but they still assume the user is proficient with the library's specific syntax. We propose Agen-ticDomiKnowS (ADS) 1 to eliminate this dependency. ADS translates free-form task descriptions into a complete DomiKnowS program using an agentic workflow that creates and tests each DomiKnowS component separately. The workflow supports optional humanin-the-loop intervention, enabling users familiar with DomiKnowS to refine intermediate outputs. We show how ADS enables experienced DomiKnowS users and non-users to rapidly construct neuro-symbolic programs, reducing development time from hours to 10-15 minutes.

📄 Full Content

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