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arXiv · 2601.00743

An Agentic Framework for Neuro-Symbolic Programming

Aliakbar Nafar·Chetan Chigurupati·Danial Kamali·Hamid Karimian·Parisa Kordjamshidi
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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 https://hlr-demo.egr.msu.edu/.

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Aliakbar Nafar, Chetan Chigurupati, Danial Kamali, Hamid Karimian, Parisa Kordjamshidi. 2026-09-15. An Agentic Framework for Neuro-Symbolic Programming. https://arxiv.org/abs/2601.00743

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