mlsanityData to 7 Oct 2026

An Agentic Framework for Neuro-Symbolic Programming

arXiv 2601.00743 ↗First posted 2 Jan 2026cs.AIcited by 1

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

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/.

Key facts. Cited by 1 paper in the map (data through 7 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.

Builds on

27 of 54 references.

References · 27

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
  4. 2023ViperGPT: Visual Inference via Python Execution for Reasoningcited by 591
  5. 2022Visual Programming: Compositional visual reasoning without trainingcited by 507
  6. 2019Neural Probabilistic Logic Programming in DeepProbLogcited by 389
  7. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 379
  8. 2019The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervisioncited by 369
  9. 2020Transformers as Soft Reasoners over Languagecited by 265
  10. 2022Binding Language Models in Symbolic Languagescited by 224
  11. 2020The Multilingual Amazon Reviews Corpuscited by 142
  12. 2017HDLTex: Hierarchical Deep Learning for Text Classificationcited by 105
15 more
  1. 2019WIQA: A dataset for "What if..." reasoning over procedural textcited by 71
  2. 2018Tracking State Changes in Procedural Text: A Challenge Dataset and Models for Process Paragraph Comprehensioncited by 62
  3. 2021BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Beliefcited by 56
  4. 2025Empowering LLMs with Logical Reasoning: A Comprehensive Surveycited by 51
  5. 2023On the Effectiveness of Large Language Models in Domain-Specific Code Generationcited by 44
  6. 2023What's Left? Concept Grounding with Logic-Enhanced Foundation Modelscited by 33
  7. 2024Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detectioncited by 18
  8. 2024NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalizationcited by 13
  9. 2021DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learningcited by 12
  10. 2024Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoningcited by 11
  11. 2025ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programmingcited by 10
  12. 2025A Taxonomy of Inefficiencies in LLM-Generated Python Codecited by 8
  13. 2025NePTune: A Neuro-Pythonic Framework for Tunable Compositional Reasoning on Vision-Languagecited by 8
  14. 2024Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Languagecited by 3
  15. 2025Neuro-Symbolic Frameworks: Conceptual Characterization and Empirical Comparative Analysiscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SATURN: Symbolic Spatial Reasoning for Multi-Perspective Groundingcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming
  2. 2026Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks
  3. 2026Neuro-Symbolic Computer Use: Learning Reusable Policies for Reliable and Efficient Execution
  4. 2023Scallop: A Language for Neurosymbolic Programming
  5. 2026AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents
  6. 2025Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototypical Neurosymbolic AI
  7. 2026The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
  8. 2024Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning