https://github.com/LAMDASZ-ML/Awesome-Neuro-Symbolic-Learning-with-LLM

✨✨Latest Advances on Neuro-Symbolic Learning in the era of Large Language Models
https://github.com/LAMDASZ-ML/Awesome-Neuro-Symbolic-Learning-with-LLM
artificial-intelligence awesome embodied-ai first-order-logic gpt-4o knowledge-graph large-language-models llm-agent logic-programming machine-learning neuro-network neuro-symbolic papers planning reasoning reinforcement-learning visual-reasoning
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✨✨Latest Advances on Neuro-Symbolic Learning in the era of Large Language Models
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Topics: artificial-intelligence, awesome, embodied-ai, first-order-logic, gpt-4o, knowledge-graph, large-language-models, llm-agent, logic-programming, machine-learning, neuro-network, neuro-symbolic, papers, planning, reasoning, reinforcement-learning, visual-reasoning
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README

        



    



 Awesome-Neuro-Symbolic-Learning-with-LLM  



    ✨✨ Curated collection of papers and resources on latest Advances on Neuro-Symbolic Learning in the Era of Large Language Model



🗂️ Table of Contents



Awesome Tutorials & Workshops & Talks

Awesome Survey

Awesome Papers

  

    Basic Neuro-Symbolic Frameworks

    LLM for Neuro-Symbolic Learning

    Neuro-Symbolic Learning for LLM Reasoning

    Neuro-Symbolic Learning for Planning

    Neuro-Symbolic Learning for the Explainable & Trustworthy

    Neuro-Symbolic Learning for Visual Reasoning

    Neuro-Symbolic Learning for Math Reasoning

    Neuro-Symbolic Learning for Reinforcement Learning

    Neuro-Symbolic Learning for Embodied AI

    Neuro-Symbolic Learning for Agent

    Neuro-Symbolic Learning for AIGC

    Misc

  

Awesome Datasets & Benchmarks



## Awesome Tutorials & Workshops & Talks

- [Summer School on Neurosymbolic Programming](https://www.neurosymbolic.org/index.html)

- [Advances in Neuro Symbolic Reasoning and Learning](https://neurosymbolic.asu.edu/2023-aaai-tutorial-advances-in-neuro-symbolic-reasoning/) Tutorials in AAAI 2023

- [Neuro-Symbolic Methods for Language and Vision](https://sites.google.com/allenai.org/nsmlv-tutorial-aaai-22) Tutorials in AAAI 2022

- [Neuro-Symbolic Approaches: Large Language Models + Tool Use](https://wenting-zhao.github.io/complex-reasoning-tutorial/slides/6.pdf) Tutorials in ACL 2023

- [Neuro-Symbolic Visual Reasoning and Program Synthesis](http://nscv.csail.mit.edu/) Tutorials in CVPR 2020

- [Neuro-Symbolic Generative Models](https://nesygems.github.io/) Workshop in ICLR 2023

- [Neuro-Symbolic Learning and Reasoning in the Era of Large Language Models](https://nuclear-workshop.github.io/aaai2024/) Workshop in AAAI 2024

- [MIT 6.S191: Neuro-Symbolic AI](http://introtodeeplearning.com/2020/slides/6S191_MIT_DeepLearning_L7.pdf) Talk given by David Cox [[Video]](https://www.youtube.com/watch?v=4PuuziOgSU4)

- [Neuro-Symbolic Concepts for Robotic Manipulation](https://jiayuanm.com/data/2023-07-09-rss-neuro-symbolic-concepts.pdf) Talk given by Jiayuan Mao  [[Video]](https://www.youtube.com/watch?v=S8KsCtbJqz0)

- [Building General-Purpose Robots with Compositional Action Abstractions](https://jiayuanm.com/data/2024-04-19-brown-compositional-action-abstractions.pdf) Talk given by Jiayuan Mao  

## Awesome Survey & Books

- [A Survey on Neural-symbolic Learning Systems](https://arxiv.org/abs/2111.08164)

- [Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI](https://arxiv.org/abs/2401.01040)

- [Neuro-Symbolic AI: The 3rd Wave](https://arxiv.org/pdf/2012.05876)

- [Neuro-Symbolic AI and its Taxonomy: A Survey](https://arxiv.org/pdf/2305.08876)

- [The third AI summer: AAAI Robert S. Engelmore Memorial Lecture](https://henrykautz.com/papers/AI%20Magazine%20-%202022%20-%20Kautz%20-%20The%20third%20AI%20summer%20%20AAAI%20Robert%20S%20%20Engelmore%20Memorial%20Lecture.pdf)

- [Neurosymbolic AI - Why, What, and How](https://arxiv.org/pdf/2305.00813)

- [From Statistical Relational to Neuro-Symbolic Artificial Intelligence: a Survey](https://arxiv.org/pdf/2108.11451)

- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://people.cs.ksu.edu/~hitzler/pub2/2021_AIC_NeSy.pdf)

- [A Review on Neuro-symbolic AI Improvements to Natural Language Processing](https://www.zemris.fer.hr/~ajovic/articles/Keber_et_al_MIPRO_2024_IEEE_copyright.pdf)

- [Survey on Applications of Neurosymbolic Artificial Intelligence](https://arxiv.org/abs/2209.12618)

- [Neurosymbolic Programming](https://www.cs.utexas.edu/~swarat/pubs/PGL-049-Plain.pdf) [[Slides]](https://nips.cc/media/neurips-2022/Slides/55804.pdf)

- [Overview of Neuro-Symbolic Integration Frameworks](https://link.springer.com/chapter/10.1007/978-981-97-8171-3_10)

- [Survey on applications of neurosymbolic artificial intelligence](https://arxiv.org/abs/2209.12618)

## Awesome Papers

### Basic Neuro-Symbolic Frameworks

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Semantic-based regularization for learning and inference**](https://arxiv.org/abs/1904.11694) 
 | Artificial Intelligence | 2017 | - |

|[**DeepProbLog: Neural Probabilistic Logic Programming**](https://arxiv.org/abs/1805.10872) 
 | NeurIPS | 2018 | [Github](https://github.com/ML-KULeuven/deepproblog) |

|[**Learning Explanatory Rules from Noisy Data**](https://arxiv.org/abs/1711.04574) 
 | Journal of Artificial Intelligence Research | 2018 | [Github](https://github.com/ai-systems/DILP-Core) |

|[**Augmenting Neural Networks with First-order Logic**](https://arxiv.org/pdf/1906.06298) 
 | ACL | 2019 | - |

|[**Neural Logic Machines**](https://arxiv.org/abs/1904.11694) 
 | ICLR | 2019 | [Github](https://github.com/google/neural-logic-machines) |

|[**Bridging Machine Learning and Logical Reasoning by Abductive Learning**](https://proceedings.neurips.cc/paper_files/paper/2019/file/9c19a2aa1d84e04b0bd4bc888792bd1e-Paper.pdf) 
 | NeurIPS | 2019 | [Github](https://github.com/IBM/LNN) |

|[**SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver**](https://arxiv.org/pdf/1905.12149) 
 | ICML | 2019 | [Github](https://github.com/locuslab/satnet) |

|[**DL2: Training and Querying Neural Networks with Logic**](http://proceedings.mlr.press/v97/fischer19a/fischer19a.pdf) 
 | ICML | 2019 | [Github](https://github.com/eth-sri/dl2) |

|[**The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision**](http://nscl.csail.mit.edu/data/papers/2019ICLR-NSCL.pdf) 
 | ICLR | 2019 | [Github](https://github.com/vacancy/NSCL-PyTorch-Release) |

|[**NeurASP: Embracing Neural Networks into Answer Set Programming**](https://arxiv.org/abs/2307.07700) 
 | IJCAI | 2020 | [Github](https://github.com/azreasoners/NeurASP) |

|[**Learning programs by learning from failures**](https://arxiv.org/abs/2005.02259) 
 | Machine Learning | 2020 | - |

|[**Logical Neural Networks**](https://arxiv.org/pdf/2006.13155) 
 | Arxiv | 2020 | [Github](https://github.com/IBM/LNN) |

|[**Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning**](https://arxiv.org/pdf/2006.06649) 
 | ICML | 2020 | [Github](https://liqing-ustc.github.io/NGS) |

|[**Ontology Reasoning with Deep Neural Networks**](https://arxiv.org/abs/1808.07980) 
 | Artificial Intelligence | 2020 | - |

|[**MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks**](https://arxiv.org/pdf/2111.01564) 
 | AAAI | 2022 | - |

|[**Neuro-Symbolic Hierarchical Rule Induction**](https://proceedings.mlr.press/v162/glanois22a.html) 
 | ICML | 2022 | - |

|[**Logic Tensor Networks**](https://arxiv.org/abs/2012.13635) 
 | Artificial Intelligence | 2022 | [Github](https://github.com/logictensornetworks/logictensornetworks) |

|[**Neuro-symbolic Learning Yielding Logical Constraints**](https://arxiv.org/abs/2410.20957) 
 | NeurIPS | 2023 | [Github](https://github.com/Lizn-zn/Nesy-Programming) |

|[**Neural-Symbolic Recursive Machine for Systematic Generalization**](https://arxiv.org/abs/2210.01603) 
 | ICLR | 2024 | [Github](https://liqing.io/NSR/) |

### LLM for Neuro-Symbolic Learning

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Evaluating Large Language Models Trained on Code**](https://arxiv.org/abs/2107.03374) 
 | Arxiv | 2021 | [Github](https://github.com/openai/human-eval) |

|[**Autoformalization with Large Language Models**](https://arxiv.org/abs/2205.12615) 
 | NeurIPS | 2022 | - |

|[**MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning**](https://arxiv.org/pdf/2205.00445) 
 | Arxiv | 2022 | - |

|[**Symbol-LLM: Leverage Language Models for Symbolic System in Visual Human Activity Reasoning**](https://proceedings.neurips.cc/paper_files/paper/2023/hash/5edb57c05c81d04beb716ef1d542fe9e-Abstract-Conference.html) 
 | NeurIPS | 2023 |[Github](https://mvig-rhos.com/symbol_llm)|

|[**Large Language Models Are Neurosymbolic Reasoners**](https://arxiv.org/abs/2401.09334) 
 | AAAI | 2024 | [Github](https://github.com/hyintell/LLMSymbolic) |

|[**AutoSAT: Automatically Optimize SAT Solvers via Large Language Models**](https://arxiv.org/pdf/2402.10705) 
 | Arxiv | 2024 | - |

|[**Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models**](https://openreview.net/pdf?id=RzlCqnncQv) 
 | NeurIPS | 2024 | [Github](https://github.com/BorealisAI/llm-pddl-planning) |

|[**A Foundation Model for Zero-shot Logical Query Reasoning**](https://openreview.net/pdf?id=JRSyMBBJi6) 
 | NeurIPS | 2024 | - |

### Neuro-Symbolic Learning for LLM Reasoning

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**PTR: Prompt Tuning with Rules for Text Classification**](https://arxiv.org/pdf/2105.11259) 
 | Arxiv | 2021 |[Github](https://github.com/thunlp/PTR)|

|[**A Generative-Symbolic Model for Logical Reasoning in NLU**](https://openreview.net/pdf?id=LucARkxeWoE) 
 | IJCAI Workshop | 2021 | - |

|[**NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints**](https://arxiv.org/abs/2010.12884) 
 | ACL | 2021 | - |

|[**NeuroLogic A-star Decoding: Constrained Text Generation with Lookahead Heuristics**](https://arxiv.org/abs/2112.08726) 
 | ACL | 2022 | - |

|[**Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text**](https://aclanthology.org/2022.findings-acl.127/) 
 | ACL| 2022 | [Github](https://github.com/SiyuanWangw/LReasoner) |

|[**PAL: Program-aided Language Models**](https://arxiv.org/abs/2211.10435) 
 | ICML | 2023 | [Github](https://reasonwithpal.com/) |

|[**LLM Sandwich: NeuroSymbolic Approach to Solving Complex Reasoning Problem**](https://aclanthology.org/2023.emnlp-main.313.pdf) 
 | ACL | 2023 | [Github](https://github.com/benlipkin/linc) |

|[**Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning**](https://arxiv.org/abs/2305.12295) 
 | EMNLP | 2023 |[Github](https://github.com/teacherpeterpan/Logic-LLM)|

|[**LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers**](https://aclanthology.org/2023.emnlp-main.313.pdf) 
 | EMNLP | 2023 |[Github](https://github.com/benlipkin/linc)|

|[**Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks**](https://arxiv.org/abs/2211.12588) 
 | TMLR | 2023 | [Github](https://github.com/TIGER-AI-Lab/Program-of-Thoughts) |

|[**Leveraging Large Language Models to Generate Answer Set Programs**](https://arxiv.org/pdf/2307.07699) 
 | KR | 2023 |-|

|[**Reasoning with Language Model is Planning with World Model**](https://aclanthology.org/2023.emnlp-main.507/) 
 | ACL | 2023 |-|

|[**Faithful Logical Reasoning via Symbolic Chain-of-Thought**](https://arxiv.org/abs/2405.18357) 
 | ACL | 2023 |[Github](https://github.com/Aiden0526/SymbCoT)|

|[**Binding Language Models in Symbolic Languages**](https://openreview.net/forum?id=lH1PV42cbF) 
 | ICLR | 2023 |[Github](https://lm-code-binder.github.io/)|

|[**SATLM: Satisfiability-Aided Language Models Using Declarative Prompting**](https://arxiv.org/pdf/2305.09656) 
 | NeurIPS | 2023 |[Github](https://github.com/xiye17/SAT-LM)|

|[**StackSight: Unveiling WebAssembly through Large Language Models and Neurosymbolic Chain-of-Thought Decompilation**](https://openreview.net/forum?id=gn5AsHIIwb) 
 | ICML | 2024 |-|

|[**Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approach**](https://arxiv.org/abs/2410.21779) 
 | Arxiv | 2024 | - |

|[**Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models**](https://arxiv.org/abs/2310.00836) 
 | Arxiv | 2024 | - |

|[**Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs**](https://arxiv.org/abs/2311.09802) 
 | Arxiv | 2024 | [Github](https://github.com/DAMO-NLP-SG/CaRing) |

|[**SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs**](https://aclanthology.org/2024.acl-long.238.pdf) 
 | ACL | 2024 | - |

|[**Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning**](https://arxiv.org/abs/2309.10814) 
 | ACL | 2024 | [Github](https://github.com/luohongyin/LangCode) |

|[**Prototype-then-Refine: A Neuro-Symbolic Approach for Improved Logical Reasoning with LLMs**](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1244/final-projects/BassemAkoushHashemElezabi.pdf) 
 | Arxiv | 2024 |-|

|[**Premise Order Matters in Reasoning with Large Language Models**](https://arxiv.org/abs/2402.08939) 
 | ICML | 2024 | - |

|[**LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models**](https://arxiv.org/abs/2401.00757) 
 | EMNLP | 2024 | [Github](https://github.com/yxwan123/LogicAsker) |

|[**Language Models can be Logical Solvers**](https://arxiv.org/pdf/2311.06158) 
 | ACL| 2024 | - |

|[**Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning**](https://arxiv.org/abs/2305.12599) 
 | ACL| 2024 | [Github](https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning) |

|[**Learning to Reason via Program Generation, Emulation, and Search**](https://arxiv.org/abs/2405.16337) 
 | NeurIPS | 2024 | [Github](https://github.com/nweir127/CoGEX) |

|[**Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios**](https://openreview.net/pdf?id=uoJQ9qadjY) 
 | NeurIPS | 2024 | [Github](https://github.com/shantanuj/IPRM_Iterative_and_Parallel_Reasoning_Mechanism) |

|[**Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus**](https://arxiv.org/abs/2411.12498) 
 | NeurIPS | 2024 | - |

|[**Adaptable Logical Control for Large Language Models**](https://arxiv.org/abs/2406.13892) 
 | NeurIPS | 2024 | - |

|[**Rule Based Rewards for Language Model Safety**](https://openreview.net/pdf?id=QVtwpT5Dmg) 
 | NeurIPS | 2024 | - |

|[**Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts**](https://arxiv.org/abs/2409.13728) 
 | NeurIPS | 2024 | - |

|[**KnowGPT: Knowledge Graph based PrompTing for Large Language Models**](https://openreview.net/pdf?id=PacBluO5m7) 
 | NeurIPS | 2024 | - |

|[**Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning**](https://arxiv.org/abs/2310.01061) 
 | ICLR | 2024 | - |

### Neuro-Symbolic Learning for Planning

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**PDDL Planning with Pretrained Large Language Models**](https://openreview.net/forum?id=1QMMUB4zfl) 
 | NeurIPS Workshop | 2022 |[Github](https://github.com/Learning-and-Intelligent-Systems/llm4pddl)|

|[**Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning**](https://proceedings.neurips.cc/paper_files/paper/2023/file/f9f54762cbb4fe4dbffdd4f792c31221-Paper-Conference.pdf) 
 | NeurIPS | 2023 |[Github](https://guansuns.github.io/pages/llm-dm/)|

|[**LLM+P: Empowering Large Language Models with Optimal Planning Proficiency**](https://arxiv.org/abs/2304.11477) 
 | Arxiv | 2023 |[Github](https://github.com/Cranial-XIX/llm-pddl)|

|[**Learning Rational Subgoals from Demonstrations and Instructions**](https://arxiv.org/pdf/2303.05487) 
 | AAAI | 2023 |[Github](https://github.com/C-SUNSHINE/RSG-PyTorch-Release)|

|[**Planning with Large Language Models for Code Generation**](https://arxiv.org/abs/2303.05510) 
 | ICLR | 2023 | [Github](https://codeaimcts.github.io/) |

|[**Neuro-Symbolic Procedural Planning with Commonsense Prompting**](https://arxiv.org/pdf/2206.02928) 
 | ICLR | 2023 |[Github](https://github.com/YujieLu10/CLAP)|

|[**Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos**](https://openaccess.thecvf.com/content/CVPR2024/papers/Nagasinghe_Why_Not_Use_Your_Textbook_Knowledge-Enhanced_Procedure_Planning_of_Instructional_CVPR_2024_paper.pdf) 
 | CVPR | 2024 |[Github](https://github.com/Ravindu-Yasas-Nagasinghe/KEPP)|

|[**Learning Planning Abstractions from Language**](https://arxiv.org/pdf/2405.03864) 
 | ICLR | 2024 |-|

|[**Generalized Planning in PDDL Domains with Pretrained Large Language Models**](https://arxiv.org/abs/2305.11014) 
 | AAAI | 2024 |-|

### Neuro-Symbolic Learning for the Explainable & Trustworthy

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**An Interpretable Neuro-Symbolic Reasoning Framework for Task-Oriented Dialogue Generation**](https://arxiv.org/abs/2203.05843) 
 | ACL| 2022 | [Github](https://github.com/shiquanyang/NS-Dial) |

|[**Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations**](https://openreview.net/forum?id=o-1v9hdSult)
 | NeurIPS | 2022 |-|

|[**Interpretable Neural-Symbolic Concept Reasoning**](https://proceedings.mlr.press/v202/barbiero23a/barbiero23a.pdf) 
 | ICML | 2023 |-|

### Neuro-Symbolic Learning for Visual Reasoning 

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding**](https://arxiv.org/abs/1810.02338) 
 | NeurIPS | 2018 | [Github](https://github.com/kexinyi/ns-vqa) |

|[**Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering**](https://proceedings.mlr.press/v97/vedantam19a/vedantam19a.pdf) 
 | ICML | 2019 |-|

|[**Visual Concept-MetaConcept Learning**](https://arxiv.org/pdf/2002.01464) 
 | NeurIPS | 2019 |[Github](https://github.com/Glaciohound/VCML)|

|[**The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision**](http://nscl.csail.mit.edu/data/papers/2019ICLR-NSCL.pdf) 
 | ICLR | 2019 | [Github](https://github.com/vacancy/NSCL-PyTorch-Release) |

|[**Learning to Describe Scenes with Programs**](https://jiajunwu.com/papers/scene2prog_iclr.pdf) 
 | ICLR | 2019 |-|

|[**Learning to Infer and Execute 3D Shape Programs**](https://arxiv.org/abs/1901.02875) 
 | ICLR | 2019 | [Github](https://github.com/HobbitLong/shape2prog) |

|[**Program-Guided Image Manipulators**](https://jiajunwu.com/papers/pgim_iccv.pdf) 
 | ICCV | 2019 |-|

|[**Neuro-Symbolic Visual Reasoning: Disentangling “Visual” from “Reasoning”**](https://arxiv.org/abs/2006.11524) 
 | ICML | 2020 | [Github](https://github.com/microsoft/DFOL-VQA) |

|[**FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations**](https://arxiv.org/pdf/2203.16639)
 | ICLR | 2022 | - |

|[**A Neuro-Symbolic ASP Pipeline for Visual Question Answering**](https://arxiv.org/abs/2205.07548)
 | ICLP | 2022 | - |

|[**Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models**](https://arxiv.org/pdf/2304.09842)
 | NeurIPS | 2023 |[Github](https://github.com/lupantech/chameleon-llm)|

|[**What's Left? Concept Grounding with Logic-Enhanced Foundation Models**](https://jiajunwu.com/papers/left_nips.pdf)
 | NeurIPS | 2023 | [Github](https://github.com/joyhsu0504/LEFT) |

|[**Visual Programming: Compositional visual reasoning without training**](https://arxiv.org/abs/2211.11559)
 | CVPR (Best Paper) | 2023 | [Github](https://github.com/allenai/visprog) |

|[**NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations**](https://arxiv.org/abs/2303.13483)
 | CVPR | 2023 | [Github](https://github.com/joyhsu0504/NS3D) |

|[**ViperGPT: Visual Inference via Python Execution for Reasoning**](https://arxiv.org/abs/2303.08128)
 | CVPR | 2023 | [Github](https://github.com/cvlab-columbia/viper) |

|[**Rapid Image Labeling via Neuro-Symbolic Learning**](https://dl.acm.org/doi/pdf/10.1145/3580305.3599485) 
 | KDD | 2023 |[Github](https://github.com/Neural-Symbolic-Image-Labeling/Rapid/)|

|[**GENOME: Generative Neuro-Symbolic Visual Reasoning by Growing and Reusing Modules**](https://openreview.net/forum?id=MNShbDSxKH) 
 | ICLR | 2024 |-|

|[**Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification**](https://arxiv.org/abs/2410.04492) 
 | NeurIPS | 2024 |-|

|[**Integrating Neural-Symbolic Reasoning With Variational Causal Inference Network for Explanatory Visual Question Answering**](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522958) 
 | TPAMI | 2024 |-|

|[**TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning**](https://arxiv.org/abs/2402.19467) 
 | EMNLP | 2024 |-|

### Neuro-Symbolic Learning for Math Reasoning

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Solving Geometry Problems:Combining Text and Diagram Interpretation**](https://aclanthology.org/D15-1171.pdf)
 | ACL | 2015 | - |

|[**SMT-Solving Induction Proofs of Inequalities**](https://arxiv.org/abs/2307.16761)
 | Arxiv | 2023 | - |

|[**A Survey on Deep Learning for Theorem Proving**](https://arxiv.org/pdf/2404.09939)
 | COLM | 2024 | - |

|[**A Symbolic Framework for Evaluating Mathematical Reasoning and Generalization with Transformers**](https://arxiv.org/abs/2305.12563) 
 | ACL | 2024 | [Github](https://github.com/jmeadows17/transformers-for-calculus) |

|[**AlphaIntegrator: Transformer Action Search for Symbolic Integration Proofs**](https://arxiv.org/abs/2410.02666)
 | Arxiv| 2024 | - |

|[**Proving Olympiad Algebraic Inequalities without Human Demonstrations**](https://arxiv.org/abs/2406.14219)
 | Arxiv| 2024 | - |

|[**Frugal LMs Trained to Invoke Symbolic Solvers Achieve Parameter-Efficient Arithmetic Reasoning**](https://arxiv.org/abs/2312.05571) 
 | AAAI | 2024 |-|

|[**Neuro-Symbolic Data Generation for Math Reasoning**](https://openreview.net/pdf?id=CIcMZGLyZW) 
 | NeurIPS | 2024 |-|

|[**Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic Consistency**](https://arxiv.org/abs/2410.20936) 
 | NeurIPS | 2024 |[Github](https://github.com/Miracle-Messi/Isa-AutoFormal)|

|[**ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving**](https://arxiv.org/abs/2309.17452) 
 | ICLR | 2024 |[Github](https://github.com/microsoft/ToRA)|

|[**Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization**](https://arxiv.org/abs/2403.18120) 
 | ICLR | 2024 |[Github](https://github.com/jinpz/dtv)|

|[**MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning**](https://arxiv.org/abs/2310.03731) 
 | ICLR | 2024 |[Github](https://github.com/mathllm/MathCoder)|

|[**SubgoalXL: Subgoal-based Expert Learning for Theorem Proving**](https://arxiv.org/abs/2408.11172) 
 | Arxiv | 2024 |-|

|[**Autoformalizing Euclidean Geometry**](https://arxiv.org/pdf/2405.17216)
 | ICML | 2024 | [Github](https://github.com/loganrjmurphy/LeanEuclid) |

|[**A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges**](https://arxiv.org/abs/2412.11936) 
 | Arxiv | 2024 |-|

### Neuro-Symbolic Learning for Reinforcement Learning

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Modular multitask reinforcement learning with policy sketches**](http://proceedings.mlr.press/v70/andreas17a.html)|ICML|2017|-|

|[**Programmatically interpretable reinforcement learning**](http://proceedings.mlr.press/v80/verma18a.html)|ICML|2018|-|

|[**SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning**](https://arxiv.org/abs/1811.00090)|AAAI|2019|-|

|[**LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning**](https://www.ijcai.org/proceedings/2019/0840.pdf)|IJCAI|2019|-|

|[**Distilling deep reinforcement learning policies in soft decision trees**](https://cris.vub.be/ws/portalfiles/portal/46718934/IJCAI_2019_XAI_WS_paper.pdf)|IJCAI Workshop|2019|-|

|[**SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning**](https://arxiv.org/abs/1811.00090)|AAAI|2019|-|

|[**Imitationprojected programmatic reinforcement learning**](https://arxiv.org/abs/1907.05431)|NeurIPS|2019|-|

|[**Language as an Abstraction for Hierarchical Deep Reinforcement Learning**](https://arxiv.org/abs/1906.07343)|NeurIPS|2019|-|

|[**Regression Planning Networks**](https://faculty.cc.gatech.edu/~danfei/rpn_neurips19_final.pdf)|NeurIPS|2019|-|

|[**PLANS: Neuro-Symbolic Program Learning from Videos**](https://proceedings.neurips.cc/paper/2020/file/fe131d7f5a6b38b23cc967316c13dae2-Paper.pdf)|NeurIPS|2020|

|[**Synthesizing Programmatic Policies that Inductively Generalize**](https://openreview.net/forum?id=S1l8oANFDH)|ICLR|2020|

|[**Neurosymbolic Reinforcement Learning with Formally Verified Exploration**](https://proceedings.neurips.cc/paper_files/paper/2020/file/448d5eda79895153938a8431919f4c9f-Paper.pdf)
 | NeurIPS| 2020 | - |

|[**Deepsynth: Program synthesis for automatic task segmentation in deep reinforcement learning**](https://arxiv.org/abs/1911.10244)|AAAI|2021|-|

|[**Neuro-Symbolic Reinforcement Learning with First-Order Logic**](https://aclanthology.org/2021.emnlp-main.283/) 
 | EMNLP | 2021 |-|

|[**Discovering symbolic policies with deep reinforcement learning**](https://proceedings.mlr.press/v139/landajuela21a.html)|ICML|2021|-|

|[**Compositional Reinforcement Learning from Logical Specifications**](https://arxiv.org/abs/2106.13906)|NeurIPS|2021| - |

|[**Program Synthesis Guided Reinforcement Learning for Partially Observed Environments**](https://arxiv.org/pdf/2102.11137)|NeurIPS|2021|-|

|[**Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search**](https://arxiv.org/abs/2212.14849)|Arxiv|2022|-|

|[**Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework**](https://link.springer.com/article/10.1007/s10994-022-06142-7) 
 | Machine Learning | 2022 |-|

|[**Neurosymbolic Reinforcement Learning and Planning: A Survey**](https://arxiv.org/abs/2309.01038) 
 | IEEE TAI | 2023 |-|

|[**Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs**](https://arxiv.org/abs/2301.12950)|ICML| 2023| - |

|[**Robust Subtask Learning for Compositional Generalization**](https://arxiv.org/abs/2302.02984)|ICML|2023|-|

|[**Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction**](https://proceedings.neurips.cc/paper_files/paper/2023/file/9f42f06a54ce3b709ad78d34c73e4363-Paper-Conference.pdf)|NeurIPS|2023|[Github](https://github.com/k4ntz/NUDGE)|

|[**Cosmos: Neurosymbolic Grounding for Compositional World Models**](https://arxiv.org/abs/2310.12690)|ICLR|2024|[Github](https://github.com/trishullab/cosmos)|

|[**Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning**](https://arxiv.org/abs/2310.03325)|ICLR|2024|[Github](https://fqyqc.github.io/ConTranPlan/)|

|[**Skill Machines: Temporal Logic Skill Composition in Reinforcement Learning**](https://arxiv.org/abs/2205.12532)|ICLR|2024|[Github](https://fqyqc.github.io/ConTranPlan/)|

|[**Enhancing Human-AI Collaboration Through Logic-Guided Reasoning**](https://openreview.net/forum?id=TWC4gLoAxY)|ICLR|2024|-|

|[**Neurosymbolic Grounding for Compositional World Models**](https://arxiv.org/abs/2310.12690)|ICLR|2024|[Github](https://trishullab.github.io/cosmos-web/)|

|[**End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations**](https://arxiv.org/abs/2403.12451) 
 | ICML | 2024 |[Github](https://ins-rl.github.io/)|

|[**Interpretable end-to-end Neurosymbolic Reinforcement Learning agents**](https://arxiv.org/abs/2410.14371)|Arxiv|2024|-|

|[**Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning**](https://arxiv.org/abs/2402.17217)|Arxiv|2024|-|

### Neuro-Symbolic Learning for Embodied AI

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Neural Task Programming: Learning to Generalize Across Hierarchical Tasks**](https://ai.stanford.edu/~yukez/papers/icra2018.pdf)|ICRA|2018||

|[**Learning symbolic operators for task and motion planning**](https://arxiv.org/abs/2103.00589)|IROS|2021||

|[**PDSketch: Integrated Domain Programming, Learning, and Planning**](https://pdsketch.csail.mit.edu/data/papers/2022NeurIPS-PDSketch.pdf) 
 | NeurIPS| 2022 | [Github](https://github.com/vacancy/PDSketch-Alpha-Release) |

|[**JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents**](https://arxiv.org/pdf/2208.13266)
 | Arxiv| 2022 | - |

|[**Mind's Eye: Grounded Language Model Reasoning through Simulation**](https://arxiv.org/abs/2210.05359)|Arxiv|2022|[Github](https://arxiv.org/pdf/2210.05359)|

|[**Do As I Can, Not As I Say: Grounding Language in Robotic Affordances**](https://arxiv.org/abs/2204.01691)|Arxiv|2023|[Github](https://say-can.github.io/)|

|[**Learning Neuro-Symbolic Skills for Bilevel Planning**](https://arxiv.org/abs/2206.10680)
 | COIL | 2022 | [Github](https://github.com/Learning-and-Intelligent-Systems/predicators/releases/tag/skill-learning-june-2022) |

|[**ProgPrompt: Generating Situated Robot Task Plans using Large Language Models**](https://arxiv.org/abs/2209.11302) 
 | ICRA | 2023 |[Github](https://github.com/NVlabs/progprompt-vh)|

|[**Learning Neuro-symbolic Programs for Language Guided Robot Manipulation**](https://arxiv.org/abs/2211.06652)|ICRA|2023||

|[**What's Left? Concept Grounding with Logic-Enhanced Foundation Models**](https://jiajunwu.com/papers/left_nips.pdf)
 | NeurIPS | 2023 | [Github](https://github.com/joyhsu0504/LEFT) |

|[**Programmatically Grounded, Compositionally Generalizable Robotic Manipulation**](https://arxiv.org/pdf/2304.13826) 
 | ICLR | 2023 |[Github](https://progport.github.io/)|

|[**Knowledge-based Embodied Question Answering**](https://arxiv.org/pdf/2109.07872) 
 | TPAMI | 2023 |-|

|[**Learning Adaptive Planning Representations with Natural Language Guidance**](https://arxiv.org/pdf/2312.08566) 
 | ICLR | 2024 |-|

|[**Learning Reusable Manipulation Strategies**](https://arxiv.org/pdf/2311.03293) 
 | CORL | 2023 |-|

|[**Code as Policies: Language Model Programs for Embodied Control**](https://arxiv.org/abs/2209.07753) 
 | Arxiv | 2023 |[Github](https://github.com/google-research/google-research/tree/master/code_as_policies)|

|[**Compositional Diffusion-Based Continuous Constraint Solvers**](https://arxiv.org/abs/2309.00966) 
 | CORL | 2023 |[Github](https://github.com/zt-yang/diffusion-ccsp)|

|[**Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation**](https://arxiv.org/pdf/2309.00987.pdf)  | CORL | 2023 ||

|[**RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools**](https://arxiv.org/abs/2306.14447)|CORL|2023||

|[**Hierarchical Planning and Learning for Robots in Stochastic Settings Using Zero-Shot Option Invention**](https://aair-lab.github.io/Publications/ss_aaai24.pdf) 
 | AAAI | 2024 |-|

|[**Learning Neuro-Symbolic Abstractions for Robot Planning and Learning**](https://ojs.aaai.org/index.php/AAAI/article/view/30409) 
 | AAAI | 2024 |-|

|[**Grounding Language Plans in Demonstrations through Counter-factual Perturbations**](https://arxiv.org/pdf/2403.17124) 
 | ICLR | 2024 |[Github](https://github.com/yanweiw/glide)|

|[**VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning**](https://arxiv.org/abs/2410.23156) 
 | Arxiv | 2024 |-|

|[**A Framework for Neurosymbolic Robot Action Planning using Large Language Models**](https://arxiv.org/abs/2303.00438) 
 | Frontiers in Neurorobotics | 2024 |-|

|[**Fast and Accurate Task Planning using Neuro-Symbolic Language Models and Multi-level Goal Decomposition**](https://arxiv.org/abs/2409.19250) 
 | Arxiv | 2024 |-|

|[**ClevrSkills: Compositional Language and Visual Reasoning in Robotics**](https://arxiv.org/pdf/2411.09052v1) 
 | NeurIPS Benchmark | 2024 |[Github](https://github.com/Qualcomm-AI-research/ClevrSkills)|

### Neuro-Symbolic Learning for Agent

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**WebGPT: Browser-assisted question-answering with human feedback**](https://arxiv.org/abs/2112.09332)
 | Arxiv| 2022 |-|

|[**HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face**](https://arxiv.org/abs/2303.17580)
 | Arxiv| 2023 |-|

|[**ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings**](https://arxiv.org/abs/2305.11554)
 | NeurIPS | 2023 |[Github](https://github.com/Ber666/ToolkenGPT)|

|[**TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs**](https://arxiv.org/abs/2303.16434)
 | Arxiv| 2023 |-|

|[**Symbolic Learning Enables Self-Evolving Agents**](https://arxiv.org/pdf/2406.18532v1)
 | Arxiv| 2024 | [Github](https://github.com/aiwaves-cn/agents) |

### Neuro-Symbolic Learning for AIGC 

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Neuro-Symbolic Generative Art: A Preliminary Study**](https://arxiv.org/pdf/2007.02171) 
 | Arxiv | 2020 |-|

|[**Generative Neurosymbolic Machines**](https://proceedings.neurips.cc/paper_files/paper/2020/file/94c28dcfc97557df0df6d1f7222fc384-Paper.pdf) 
 | NeurIPS | 2020 |-|

|[**SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models**](https://arxiv.org/abs/2305.11281) 
 | NeurIPS | 2023 |[Github](https://github.com/Wuziyi616/SlotDiffusion)|

|[**Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0**](https://arxiv.org/abs/2307.06975) 
 | Arxiv | 2024 |-|

### Misc

|  Title  |   Venue  |   Date   |   Code   |

|:--------|:--------:|:--------:|:--------:|

|[**Harnessing Deep Neural Networks with Logic Rules**](https://arxiv.org/abs/1603.06318) 
 | ACL | 2016 |-|

|[**Neural-Symbolic Reasoning Under Open-World and Closed-World Assumptions**](https://ceur-ws.org/Vol-3121/paper3.pdf) 
 | AAAI-MAKE | 2022 |-|

|[**Neuro-Symbolic Entropy Regularization**](https://proceedings.mlr.press/v180/ahmed22a/ahmed22a.pdf) 
 | UAI | 2022 |[Github](https://github.com/UCLA-StarAI/NeSyEntropy)|

|[**RuleMatch: Matching Abstract Rules for Semi-supervised Learning of Human Standard Intelligence Tests**](https://www.ijcai.org/proceedings/2023/0179.pdf) 
 | IJCAI | 2023 |[Github](https://github.com/ZjjConan/AVR-RuleMatch)|

|[**Learning with Logical Constraints but without Shortcut Satisfaction**](https://arxiv.org/abs/2403.00329) 
 | ICLR | 2023 |[Github](https://github.com/SoftWiser-group/NeSy-without-Shortcuts)|

|[**The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns**](https://arxiv.org/abs/2402.17431) 
 | Arxiv | 2024 |-|

|[**On the Hardness of Probabilistic Neurosymbolic Learning**](https://proceedings.mlr.press/v235/maene24a.html) 
 | ICML | 2024 |-|

|[**On the Independence Assumption in Neurosymbolic Learning**](https://openreview.net/pdf?id=S1gSrruVd4) 
 | ICML | 2024 |-|

|[**Analysis for Abductive Learning and Neural-Symbolic Reasoning Shortcuts**](https://openreview.net/pdf?id=AQYabSOfci) 
 | ICML | 2024 |-|

|[**Convex and Bilevel Optimization for Neural-Symbolic Inference and Learning**](https://openreview.net/pdf?id=6NQ77Vj3DT) 
 | ICML | 2024 |-|

|[**Bridging Neural and Symbolic Representations with Transitional Dictionary Learning**](https://openreview.net/forum?id=uqxBTcWRnj) 
 | ICLR | 2024 |-|

|[**LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints**](https://openreview.net/forum?id=BLGQ3oqldb) 
 | ICLR | 2024 |-|

|[**Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts**](https://proceedings.neurips.cc/paper_files/paper/2023/hash/e560202b6e779a82478edb46c6f8f4dd-Abstract-Conference.html) 
 | NeurIPS | 2023 |-|

|[**Localized Symbolic Knowledge Distillation for Visual Commonsense Models**](https://proceedings.neurips.cc/paper_files/paper/2023/hash/257be12f31dfa7cc158dda99822c6fd1-Abstract-Conference.html) 
 | NeurIPS | 2023 |-|

|[**A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference**](https://proceedings.neurips.cc/paper_files/paper/2023/hash/4d9944ab3330fe6af8efb9260aa9f307-Abstract-Conference.html) 
 | NeurIPS | 2023 |-|

|[**Neuro-Symbolic Continual Learning:Knowledge, Reasoning Shortcuts and Concept Rehearsal**](https://proceedings.mlr.press/v202/marconato23a/marconato23a.pdf) 
 | ICML | 2023 | [Github](https://github.com/ema-marconato/NeSy-CL) |

|[**Out-of-Distribution Generalization by Neural-Symbolic Joint Training**](https://ojs.aaai.org/index.php/AAAI/article/view/26444) 
 | AAAI | 2023 |[Github](https://github.com/sbx126/NToC)|

## Awesome Datasets & Benchmarks

- [Visual Sudoku](https://github.com/linqs/visual-sudoku-puzzle-classification): An visual sudoku puzzle

- [CLEVR Dataset](https://cs.stanford.edu/people/jcjohns/clevr/) for VQA

- [GQA Dataset](https://cs.stanford.edu/people/dorarad/gqa/) for VQA

- [LogiCity](https://jaraxxus-me.github.io/LogiCity/) for Abstract Urban Simulation

- [LogicGame](https://www.arxiv.org/pdf/2408.15778) Benchmarking Rule-Based Reasoning Abilities of Large Language Models

- [Minigrid](https://github.com/Farama-Foundation/Minigrid?tab=readme-ov-file)

- [BabyAI](https://github.com/mila-iqia/babyai/tree/iclr19)

- [Minecraft](https://github.com/tomsilver/pddlgym)

- [Mini-Behavior](https://github.com/StanfordVL/mini_behavior) for Embodied Tasks

- [CLIPort Dataset](https://cliport.github.io/) for Embodied Tasks

- [ALFworld](https://github.com/alfworld/alfworld) for Embodied Tasks

- [VirtualHome](http://virtual-home.org/) for Embodied Tasks

- [Planbench](https://arxiv.org/pdf/2206.10498) for evaluating LLM Planning

- [RSbench](https://unitn-sml.github.io/rsbench/) A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts



    

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