An Agentic Framework for Neuro-Symbolic Programming
cs.AI
Submitted: 2026-01-02
Updated: 2026-09-15
Comments: 23 pages. Updated to the NeSy 2026 camera-ready version
Code: https://github.com/HLR/AgenticDomiKnowS
License: http://creativecommons.org/licenses/by/4.0/
The gist: Integrating symbolic constraints into deep learning models could make them more robust, interpretable, and data-efficient.
Terminology
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/.
Sources
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