# An Agentic Framework for Neuro-Symbolic Programming

arXiv: https://arxiv.org/abs/2601.00743 · HTML: https://mlsanity.com/paper/2601.00743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 2 Jan 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 27 of 27)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [ViperGPT: Visual Inference via Python Execution for Reasoning](https://mlsanity.com/paper/2303.08128/) (2023)
- [Visual Programming: Compositional visual reasoning without training](https://mlsanity.com/paper/2211.11559/) (2022)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning](https://mlsanity.com/paper/2305.12295/) (2023)
- [The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision](https://mlsanity.com/paper/1904.12584/) (2019)
- [Transformers as Soft Reasoners over Language](https://mlsanity.com/paper/2002.05867/) (2020)
- [Binding Language Models in Symbolic Languages](https://mlsanity.com/paper/2210.02875/) (2022)
- [The Multilingual Amazon Reviews Corpus](https://mlsanity.com/paper/2010.02573/) (2020)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [WIQA: A dataset for "What if..." reasoning over procedural text](https://mlsanity.com/paper/1909.04739/) (2019)
- [Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehension](https://mlsanity.com/paper/1805.06975/) (2018)
- [BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief](https://mlsanity.com/paper/2109.14723/) (2021)
- [Empowering LLMs with Logical Reasoning: A Comprehensive Survey](https://mlsanity.com/paper/2502.15652/) (2025)
- [On the Effectiveness of Large Language Models in Domain-Specific Code Generation](https://mlsanity.com/paper/2312.01639/) (2023)
- [What's Left? Concept Grounding with Logic-Enhanced Foundation Models](https://mlsanity.com/paper/2310.16035/) (2023)
- [Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection](https://mlsanity.com/paper/2405.02318/) (2024)
- [Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning](https://mlsanity.com/paper/2410.22077/) (2024)
- [NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization](https://mlsanity.com/paper/2412.15588/) (2024)
- [DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning](https://mlsanity.com/paper/2108.12370/) (2021)
- [A Taxonomy of Inefficiencies in LLM-Generated Python Code](https://mlsanity.com/paper/2503.06327/) (2025)
- [ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming](https://mlsanity.com/paper/2510.05774/) (2025)
- [NePTune: A Neuro-Pythonic Framework for Tunable Compositional Reasoning on Vision-Language](https://mlsanity.com/paper/2509.25757/) (2025)
- [Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language](https://mlsanity.com/paper/2407.20513/) (2024)
- [Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysis](https://mlsanity.com/paper/2509.07122/) (2025)

## Built on by (most cited first, 1 of 1)

- [SATURN: Symbolic Spatial Reasoning for Multi-Perspective Grounding](https://mlsanity.com/paper/2606.22694/) (2026)

## Abstract (arXiv)

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/.