An Agentic Framework for Neuro-Symbolic Programming

• URL: http://arxiv.org/abs/2601.00743v1
• Date: Fri, 02 Jan 2026 16:59:39 GMT
• Title: An Agentic Framework for Neuro-Symbolic Programming
• Authors: Aliakbar Nafar, Chetan Chigurupati, Danial Kamali, Hamid Karimian, Parisa Kordjamshidi,
• Abstract summary: We show how ADS enables experienced DomiKnowS users and non-users to rapidly construct neuro-symbolic programs.<n>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.
• Score: 22.629690263728914
• License: http://creativecommons.org/licenses/by/4.0/
• 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 AgenticDomiKnowS (ADS) 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 human-in-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.

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