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.
@article{arxiv.2601.00743,
title = {An Agentic Framework for Neuro-Symbolic Programming},
author = {Aliakbar Nafar and Chetan Chigurupati and Danial Kamali and Hamid Karimian and Parisa Kordjamshidi},
journal= {arXiv preprint arXiv:2601.00743},
year = {2026}
}