An Agentic Framework for Neuro-Symbolic Programming

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 alike to construct complete neuro-symbolic programs in 10-15 minutes, whereas manually coding even a component of DomiKnowS takes an hour. Access the UI at this https URL.

23 pages. Updated to the NeSy 2026 camera-ready version

Artificial Intelligence (cs.AI)

arXiv:2601.00743 [cs.AI]

(or arXiv:2601.00743v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2601.00743 Focus to learn more arXiv-issued DOI via DataCite

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