> For the complete documentation index, see [llms.txt](https://neurosymbolicai.gitbook.io/neuro-symbolic-ai-in-practice/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://neurosymbolicai.gitbook.io/neuro-symbolic-ai-in-practice/back-matter/references.md).

# References

All references are listed in MLA format. Where applicable, links to preprints, published versions, code repositories, datasets, and model hubs are provided.

***

## Foundational AI Planning

**Fikes, Richard E., and Nils J. Nilsson.** "STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving." *Artificial Intelligence* 2.3–4 (1971): 189–208.\
<https://doi.org/10.1016/0004-3702(71)90010-5>

**Ghallab, Malik, et al.** "PDDL — The Planning Domain Definition Language." *IPC Technical Report CVC TR-98-003*, Yale Center for Computational Vision and Control, 1998.\
<https://planning.wiki/ref/pddl>

**Ghallab, Malik, Dana Nau, and Paolo Traverso.** *Automated Planning: Theory and Practice*. Morgan Kaufmann, 2004.\
<https://www.sciencedirect.com/book/9781558608566/automated-planning>

**Fox, Maria, and Derek Long.** "PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains." *Journal of Artificial Intelligence Research* 20 (2003): 61–124.\
<https://doi.org/10.1613/jair.1129>

**Bylander, Tom.** "The Computational Complexity of Propositional STRIPS Planning." *Artificial Intelligence* 69.1–2 (1994): 165–204.\
<https://doi.org/10.1016/0004-3702(94)90081-7>

**Erol, Kutluhan, Dana Nau, and V. S. Subrahmanian.** "Complexity, Decidability and Undecidability Results for Domain-Independent Planning." *Artificial Intelligence* 76.1–2 (1995): 75–88.\
<https://doi.org/10.1016/0004-3702(94)00080-K>

**Reiter, Raymond.** "On Closed World Data Bases." *Logic and Data Bases*, edited by Hervé Gallaire and Jack Minker, Plenum Press, 1978, pp. 55–76.

***

## HTN Planning

**Erol, Kutluhan, James Hendler, and Dana S. Nau.** "HTN Planning: Complexity and Expressivity." *Proceedings of AAAI*, 1994, pp. 1123–1128.\
<https://www.aaai.org/Library/AAAI/aaai94contents.php>

**Nau, Dana, Tsz-Chiu Au, Okhtay Ilghami, Ugur Kuter, J. William Murdock, Dan Wu, and Fusun Yaman.** "SHOP2: An HTN Planning System." *Journal of Artificial Intelligence Research* 20 (2003): 379–404. \[Foundational reference for SHOP/SHOP2; SHOP3 is the current maintained successor.]\
<https://doi.org/10.1613/jair.1141> | Code (SHOP3): <https://github.com/shop-planner/shop3>

**Höller, Daniel, Gregor Behnke, Pascal Bercher, Susanne Biundo, Humbert Fiorino, Damien Pellier, and Ron Alford.** "HDDL: A Language to Describe Hierarchical Planning Problems." *Proceedings of AAAI*, 2020.\
<https://doi.org/10.1609/aaai.v34i06.6542>

***

## Planning Variations

**Smith, David E.** "Choosing Objectives in Over-Subscription Planning." *Proceedings of ICAPS 2004*, 2004, pp. 393–401.

**Van den Briel, Menkes, et al.** "Effective Approaches for Partial Satisfaction (Over-Subscription) Planning." *Proceedings of AAAI 2004*, 2004, pp. 562–569.

**Mirkis, Vitaly, and Carmel Domshlak.** "Landmarks in Oversubscription Planning." *Proceedings of ECAI 2014*, 2014.\
<https://doi.org/10.3233/978-1-61499-419-0-621>

**Goldman, Robert P., and Mark S. Boddy.** "Expressive Planning and Explicit Knowledge." *Proceedings of AIPS*, 1996, pp. 110–117.

**Peot, Mark A., and David E. Smith.** "Conditional Nonlinear Planning." *Proceedings of AIPS*, 1992, pp. 189–197.

**Puterman, Martin L.** *Markov Decision Processes: Discrete Stochastic Dynamic Programming*. Wiley, 1994.\
<https://doi.org/10.1002/9780470316887>

**Kaelbling, Leslie Pack, Michael L. Littman, and Anthony R. Cassandra.** "Planning and Acting in Partially Observable Stochastic Domains." *Artificial Intelligence* 101.1–2 (1998): 99–134.\
<https://doi.org/10.1016/S0004-3702(98)00023-X>

***

## Real-World AI Planning Deployments

**Muscettola, Nicola, P. Pandurang Nayak, Barney Pell, and Brian C. Williams.** "Remote Agent: To Boldly Go Where No AI System Has Gone Before." *Artificial Intelligence* 103.1–2 (1998): 5–47.\
<https://doi.org/10.1016/S0004-3702(98)00068-X>

**Muscettola, Nicola.** "HSTS: Integrating Planning and Scheduling." *IJCAI Workshop on Knowledge Engineering for Planning*, 1993.\
<https://ti.arc.nasa.gov/publications/2/download/>

**Johnston, Mark D., and Glenn Miller.** "Spike: Intelligent Scheduling of Hubble Space Telescope Observations." *Innovative Applications of Artificial Intelligence (IAAI)*, 1994, pp. 1–8.\
<https://dl.acm.org/doi/10.5555/893664>

**Ai-Chang, Michael, et al.** "MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission." *IEEE Intelligent Systems* 19.1 (2004): 8–12.\
<https://doi.org/10.1109/MIS.2004.1265878>

**Bresina, John L., et al.** "Activity Planning for the Mars Exploration Rovers." *Proceedings of ICAPS 2005*.\
<https://ojs.aaai.org/index.php/ICAPS/article/view/13597>

**Chien, Steve, et al.** "Using Iterative Repair to Increase the Responsiveness of Planning and Scheduling for Observation Scheduling." *Journal of Autonomous Agents and Multi-Agent Systems* 4.1–2 (2000): 129–145.\
<https://doi.org/10.1023/A:1010081516793>

***

## Neurosymbolic AI — Foundations

**Sheth, Amit, Kaushik Roy, and Manas Gaur.** "Neurosymbolic Artificial Intelligence (Why, What, and How)." *IEEE Intelligent Systems* 38.3 (2023): 56–62.\
<https://doi.org/10.1109/MIS.2023.3234994>

**Garcez, Artur d'Avila, and Luís C. Lamb.** "Neurosymbolic AI: The 3rd Wave." *Artificial Intelligence Review* 56 (2023): 12387–12406.\
<https://doi.org/10.1007/s10462-023-10448-w>

**Garcez, Artur d'Avila, et al.** "Neurosymbolic AI: The 3rd Wave." *arXiv preprint* arXiv:2012.05876 (2020).\
<https://arxiv.org/abs/2012.05876>

**Hitzler, Pascal, and Md Kamruzzaman Sarker, eds.** *Neuro-Symbolic Artificial Intelligence: The State of the Art*. IOS Press, 2022.\
<https://doi.org/10.3233/FAIA342>

**Marcus, Gary, and Ernest Davis.** *Rebooting AI: Building Artificial Intelligence We Can Trust*. Pantheon Books, 2019.\
<https://rebooting.ai>

**Kahneman, Daniel.** *Thinking, Fast and Slow*. Farrar, Straus and Giroux, 2011. ISBN: 978-0374533557.

**Booch, Grady, et al.** "Thinking Fast and Slow in AI." *Proceedings of AAAI 2021*.\
<https://arxiv.org/abs/2010.06002>

***

## LLMs and Planning

**Kambhampati, Subbarao, et al.** "Position: LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks." *Forty-First International Conference on Machine Learning (ICML)*, 2024.\
<https://arxiv.org/abs/2402.01817>

**Valmeekam, Karthik, et al.** "Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change)." *NeurIPS 2022 Workshop on Neuro-Symbolic AI*, 2022.\
<https://arxiv.org/abs/2206.10498>

**Valmeekam, Karthik, et al.** "PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change." *Advances in Neural Information Processing Systems (NeurIPS)* 36 (2023).\
<https://arxiv.org/abs/2206.10498> | Benchmark: <https://github.com/karthikv792/LLMs-Planning>

**Stechly, Kaya, Matthew Marquez, and Subbarao Kambhampati.** "GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems." *NeurIPS 2023 Workshop on Neuro-Symbolic AI*, 2023.\
<https://arxiv.org/abs/2310.12397>

**Kaplan, Jared, et al.** "Scaling Laws for Neural Language Models." *arXiv preprint* arXiv:2001.08361 (2020).\
<https://arxiv.org/abs/2001.08361>

**OpenAI.** "OpenAI o1 System Card." *OpenAI Technical Report*, 2024.\
<https://openai.com/index/openai-o1-system-card/>

***

## LLM Agent Patterns (ReAct, Toolformer, Voyager)

**Yao, Shunyu, et al.** "ReAct: Synergizing Reasoning and Acting in Language Models." *International Conference on Learning Representations (ICLR)*, 2023.\
<https://arxiv.org/abs/2210.03629> | Code: <https://github.com/ysymyth/ReAct>

**Schick, Timo, et al.** "Toolformer: Language Models Can Teach Themselves to Use Tools." *Advances in Neural Information Processing Systems (NeurIPS)* 36 (2023).\
<https://arxiv.org/abs/2302.04761>

**Silver, David, et al.** "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm." *arXiv preprint* arXiv:1712.01815 (2017). Published as "A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-Play." *Science* 362.6419 (2018): 1140–1144.\
<https://doi.org/10.1126/science.aar6404>

**Wang, Guanzhi, et al.** "Voyager: An Open-Ended Embodied Agent with Large Language Models." *Transactions on Machine Learning Research (TMLR)*, 2024.\
<https://arxiv.org/abs/2305.16291> | Code: <https://github.com/MineDreamer/Voyager>

***

## Neuro-Symbolic Planning Systems

**Liu, Bo, et al.** "LLM+P: Empowering Large Language Models with Optimal Planning Proficiency." *arXiv preprint* arXiv:2304.11477 (2023).\
<https://arxiv.org/abs/2304.11477> | Code: <https://github.com/Cranial-XIX/llm-pddl>

**Ahn, Michael, et al.** "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances." *Conference on Robot Learning (CoRL)*, 2022.\
<https://arxiv.org/abs/2204.01691> | Website: <https://say-can.github.io>

**Liang, Jacky, et al.** "Code as Policies: Language Model Programs for Embodied Control." *IEEE International Conference on Robotics and Automation (ICRA)*, 2023.\
<https://arxiv.org/abs/2209.07753> | Website: <https://code-as-policies.github.io>

**Yao, Shunyu, et al.** "Tree of Thoughts: Deliberate Problem Solving with Large Language Models." *Advances in Neural Information Processing Systems (NeurIPS)* 36 (2023).\
<https://arxiv.org/abs/2305.10601> | Code: <https://github.com/princeton-nlp/tree-of-thought-llm>

**Hao, Shibo, et al.** "Reasoning with Language Model is Planning with World Model." *Proceedings of the International Conference on Machine Learning (ICML)*, 2023.\
<https://arxiv.org/abs/2305.14992> | Code: <https://github.com/Ber666/llm-reasoners>

**Guan, Lin, et al.** "Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning." *Advances in Neural Information Processing Systems (NeurIPS)* 36 (2023).\
<https://arxiv.org/abs/2305.14909>

***

## AlphaCode and AlphaProof

**Li, Yujia, et al.** "Competition-Level Code Generation with AlphaCode." *Science* 378.6624 (2022): 1092–1097.\
<https://doi.org/10.1126/science.abq1158> | Dataset: <https://github.com/google-deepmind/code_contests>

**AlphaProof Team and AlphaGeometry Team, DeepMind.** "AI Achieves Silver-Medal Standard Solving International Mathematical Olympiad Problems." *DeepMind Technical Report*, 2024.\
<https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/>

**Silver, David, et al.** "Mastering the Game of Go with Deep Neural Networks and Tree Search." *Nature* 529.7587 (2016): 484–489.\
<https://doi.org/10.1038/nature16961>

***

## Constrained / Guided Learning and Alignment

**Rafailov, Rafael, et al.** "Direct Preference Optimization: Your Language Model Is Secretly a Reward Model." *Advances in Neural Information Processing Systems (NeurIPS)* 36 (2023).\
<https://arxiv.org/abs/2305.18290> | Code: <https://github.com/eric-mitchell/direct-preference-optimization>

**Xu, Jingyi, et al.** "A Semantic Loss Function for Deep Learning with Symbolic Knowledge." *Proceedings of ICML*, 2018.\
<https://arxiv.org/abs/1711.11157> | Code: <https://github.com/UCLA-StarAI/Semantic-Loss>

**Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis.** "Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear PDEs." *Journal of Computational Physics* 378 (2019): 686–707.\
<https://doi.org/10.1016/j.jcp.2018.10.045> | Code: <https://github.com/maziarraissi/PINNs>

**Hohenecker, Patrick, and Thomas Lukasiewicz.** "Ontology Reasoning for Text Classification." *IEEE Transactions on Neural Networks and Learning Systems* 31.7 (2020): 2559–2577.\
<https://doi.org/10.1109/TNNLS.2019.2942570>

**Giunchiglia, Eleonora, and Thomas Lukasiewicz.** "Coherent Hierarchical Multi-Label Classification Networks." *NeurIPS 2020*.\
<https://arxiv.org/abs/2010.11061>

***

## Neuro-Symbolic Concept Learning

**Mao, Jiayuan, et al.** "The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences from Natural Supervision." *ICLR 2019*.\
<https://arxiv.org/abs/1904.12584> | Code: <https://github.com/vacancy/NSCL-PyTorch-Release>

**Yi, Kexin, et al.** "Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding." *Advances in Neural Information Processing Systems* 31 (NeurIPS 2018). arXiv:1810.02338.\
<https://arxiv.org/abs/1810.02338> | Code: <https://github.com/kexinyi/ns-vqa>

**Yi, Kexin, et al.** "CLEVRER: CoLlision Events for Video REpresentation and Reasoning." *ICLR 2020*.\
<https://arxiv.org/abs/1910.01442> | Dataset: <http://clevrer.csail.mit.edu>

**Locatello, Francesco, et al.** "Object-Centric Learning with Slot Attention." *Advances in Neural Information Processing Systems* 33 (NeurIPS 2020). arXiv:2006.15055.\
<https://arxiv.org/abs/2006.15055> | Code: <https://github.com/google-deepmind/slot_attention>

***

## Differentiable Reasoning

**Serafini, Luciano, and Artur d'Avila Garcez.** "Logic Tensor Networks." *Artificial Intelligence* 303 (2022): 103649.\
<https://doi.org/10.1016/j.artint.2021.103649> | Code: <https://github.com/logictensornetworks/logictensornetworks>

**Manhaeve, Robin, et al.** "DeepProbLog: Neural Probabilistic Logic Programming." *NeurIPS 2018 Workshop on Modeling and Decision-Making in the Spatiotemporal Domain* (2018); extended version in *Machine Learning* 110.7 (2021): 1637–1679.\
<https://arxiv.org/abs/1805.10872> | Code: <https://github.com/ML-KULeuven/deepproblog>

**Evans, Richard, and Edward Grefenstette.** "Learning Explanatory Rules from Noisy Data." *Journal of Artificial Intelligence Research* 61 (2018): 1–64.\
<https://doi.org/10.1613/jair.5714>

**Rocktäschel, Tim, and Sebastian Riedel.** "End-to-End Differentiable Proving." *Advances in Neural Information Processing Systems (NeurIPS)*, 2017.\
<https://arxiv.org/abs/1705.11040>

**Cornelio, Cristina, et al.** "A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference." *Advances in Neural Information Processing Systems* 36 (NeurIPS 2023). arXiv:2212.12393.\
<https://arxiv.org/abs/2212.12393> | Code: <https://github.com/HEmile/a-nesi>

**Cropper, Andrew, and Sebastijan Dumancic.** "Learning Programs by Learning from Failures." *Machine Learning* 111.3 (2022): 801–856. arXiv:2005.02259.\
<https://arxiv.org/abs/2005.02259>

**Stahl, Isabelle, et al.** "Neural Predicate Invention for Visual Concept Learning." *Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24)*. 2024.\
<https://ojs.aaai.org/index.php/AAAI/article/view/28278>

***

## Neural Subroutines in Planning

**Shen, William, Felipe Trevizan, and Sylvie Thiébaux.** "Learning Domain-Independent Planning Heuristics with Hypergraph Networks." *Proceedings of ICAPS*, 2020.\
<https://arxiv.org/abs/2007.06743> | Code: <https://github.com/ischubert/hgn>

**Selsam, Daniel, et al.** "Learning a SAT Solver from Single-Bit Supervision." *ICLR 2019*.\
<https://arxiv.org/abs/1802.03685> | Code: <https://github.com/dselsam/neurosat>

**Chitnis, Rohan, et al.** "PLOI: Planning with Learned Object Importance." *Proceedings of AAAI*, 2021.\
<https://arxiv.org/abs/2108.08823> | Code: <https://github.com/tomsilver/ploi>

**Veličković, Petar, et al.** "Neural Execution of Graph Algorithms." *International Conference on Learning Representations (ICLR)*, 2020. arXiv:1910.10593.\
<https://arxiv.org/abs/1910.10593>

**Veličković, Petar, et al.** "The CLRS Algorithmic Reasoning Benchmark." *Proceedings of ICML*, 2022. arXiv:2205.15659.\
<https://arxiv.org/abs/2205.15659> | Code: <https://github.com/google-deepmind/clrs>

**Ibarz, Borja, et al.** "A Generalist Neural Algorithmic Learner." *Learning on Graphs Conference (LoG)*, 2022. arXiv:2209.11142.\
<https://arxiv.org/abs/2209.11142>

**Asai, Masataro, and Alex Fukunaga.** "Classical Planning in Deep Latent Space." *Journal of Artificial Intelligence Research* 71 (2021): 1009–1050. arXiv:2107.00110.\
<https://arxiv.org/abs/2107.00110> | Code: <https://github.com/guicho271828/latplan>

***

## Knowledge Graph Integration

**Pan, Shirui, et al.** "Unifying Large Language Models and Knowledge Graphs: A Roadmap." *IEEE Transactions on Knowledge and Data Engineering*, 2024.\
<https://arxiv.org/abs/2501.05435>

**Bordes, Antoine, et al.** "Translating Embeddings for Modeling Multi-Relational Data." *NeurIPS 2013*.\
<https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html>

**Sun, Zhiqing, et al.** "RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space." *ICLR 2019*.\
<https://arxiv.org/abs/1902.10197> | Code: <https://github.com/DeepGraphLearning/KnowledgeGraphEmbedding>

**Zhang, Xikun, et al.** "GreaseLM: Graph REASoning Enhanced Language Models." *ICLR 2022*.\
<https://arxiv.org/abs/2201.08860> | Code: <https://github.com/snap-stanford/GreaseLM>

**Logan, Robert, et al.** "Barack's Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling." *ACL 2019*.\
<https://arxiv.org/abs/1906.07241>

**Edge, Darren, et al.** "From Local to Global: A Graph RAG Approach to Query-Focused Summarization." *arXiv preprint* arXiv:2404.16130 (2024).\
<https://arxiv.org/abs/2404.16130> | Code: <https://github.com/microsoft/graphrag>

***

## World Models

**Ha, David, and Jürgen Schmidhuber.** "World Models." *NeurIPS 2018*.\
<https://arxiv.org/abs/1803.10122> | Website: <https://worldmodels.github.io>

**Hafner, Danijar, et al.** "Mastering Diverse Domains through World Models (DreamerV3)." *International Conference on Learning Representations (ICLR)*, 2024. arXiv:2301.04104.\
<https://arxiv.org/abs/2301.04104> | Code: <https://github.com/danijar/dreamerv3>

**Maes, Lucas, et al.** "stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation." *arXiv preprint* arXiv:2605.21800 (2026).\
<https://arxiv.org/abs/2605.21800>

***

## Neural Network Verification

**Katz, Guy, et al.** "Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks." *Computer Aided Verification (CAV)*, 2017. arXiv:1702.01135.\
<https://arxiv.org/abs/1702.01135>

**Katz, Guy, et al.** "The Marabou Framework for Verification and Analysis of Deep Neural Networks." *Computer Aided Verification (CAV)*, 2019.\
<https://doi.org/10.1007/978-3-030-25540-4_26> | Code: <https://github.com/NeuralNetworkVerification/Marabou>

**Wang, Shiqi, et al.** "Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification." *Advances in Neural Information Processing Systems* 34 (NeurIPS 2021). arXiv:2103.06624.\
<https://arxiv.org/abs/2103.06624> | Code: <https://github.com/huanzhang12/alpha-beta-CROWN>

**Gehr, Timon, et al.** "AI²: Safety and Robustness Certification of Neural Networks with Abstract Interpretation." *IEEE Symposium on Security and Privacy*, 2018.

**Gomber, Shaurya, and Gagandeep Singh.** "Neural Abstract Interpretation." *ICLR 2025 Workshop: VerifAI: AI Verification in the Wild*, 2025.\
<https://openreview.net/forum?id=WTyyhWhp4m>

***

## JEPA World Models and Self-Supervised Representation Learning

**LeCun, Yann.** "A Path Towards Autonomous Machine Intelligence." *OpenReview*, Version 0.9.2, June 2022.\
<https://openreview.net/forum?id=BZ5a1r-kVsf>

**Assran, Mido, et al.** "V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning." *arXiv preprint* arXiv:2506.09985 (2025). \[V-JEPA 2 results cited in §4.4.2 and §5.1]\
<https://arxiv.org/abs/2506.09985>

**Mur-Labadia, Lorenzo, et al.** "V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning." *arXiv preprint* arXiv:2603.14482 (2026). \[V-JEPA 2.1 results — +20 percentage points in real-robot grasping — cited in §4.4.2 and §5.1]\
<https://arxiv.org/abs/2603.14482>

**Terver, Basile, Tsung-Yen Yang, Jean Ponce, Adrien Bardes, and Yann LeCun.** "What Drives Success in Physical Planning with Joint-Embedding Predictive World Models." *Transactions on Machine Learning Research (TMLR)*, 2026. arXiv:2512.24497. \[Engineering design guide for JEPA-WMs cited in §4.4.2, §5.1, and §5.3]\
<https://arxiv.org/abs/2512.24497> | Code: <https://github.com/facebookresearch/jepa-wms>

**Davis, Liam, Leopold Haller, Alberto Alfarano, and Mark Santolucito.** "Lattice Deduction Transformers." *arXiv preprint* arXiv:2605.08605 (2026). \[DAI §4.4.1 results cited throughout]\
<https://arxiv.org/abs/2605.08605>

***

## Planning Domain Model Learning

**Aineto, Diego, Sergio Jiménez, and Eva Onaindia.** "Learning STRIPS Action Models with Classical Planning." *Proceedings of ICAPS*, 2019.\
<https://arxiv.org/abs/1903.09824>

***

## Causal Reasoning and Multi-Agent Planning

**Zheng, Xun, Bryon Aragam, Pradeep Ravikumar, and Eric P. Xing.** "DAGs with NO TEARS: Continuous Optimization for Structure Learning." *Advances in Neural Information Processing Systems* 31 (NeurIPS 2018). arXiv:1803.01422.\
<https://arxiv.org/abs/1803.01422> | Code: <https://github.com/xunzheng/notears>

**Pearl, Judea, and Dana Mackenzie.** *The Book of Why: The New Science of Cause and Effect*. Basic Books, 2018. ISBN: 978-0465097609.

**Schölkopf, Bernhard, et al.** "Towards Causal Representation Learning." *Proceedings of the IEEE* 109.5 (2021): 612–634.\
<https://arxiv.org/abs/2102.11107>

**Sharon, Guni, et al.** "Conflict-Based Search for Optimal Multi-Agent Pathfinding." *Artificial Intelligence* 219 (2015): 40–66.\
<https://doi.org/10.1016/j.artint.2014.11.006>

**Geffner, Tomas, et al.** "Deep End-to-end Causal Inference." *Advances in Neural Information Processing Systems* 35 (NeurIPS 2022). arXiv:2202.02195.\
<https://arxiv.org/abs/2202.02195> | Code: <https://github.com/microsoft/causica>

**Lippe, Phillip, et al.** "CITRIS: Causal Identifiability from Temporal Interventions in Latent Spaces." *Proceedings of ICML*, 2022. arXiv:2202.03169.\
<https://arxiv.org/abs/2202.03169> | Code: <https://github.com/phlippe/CITRIS>

***

## Neuro-Symbolic Verification (Open Problems)

**Miya, Shinobu.** "Eidoku: A Neuro-Symbolic Verification Gate for LLM Reasoning via Structural Constraint Satisfaction." *arXiv preprint* arXiv:2512.20664 (2025).\
<https://arxiv.org/abs/2512.20664>

**Sigloch, Paul, and Christoph Benzmüller.** "Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains." *Proceedings of KI 2026 (German Conference on Artificial Intelligence)*, 2026.\
<https://arxiv.org/abs/2605.26942>

***

## Constrained Generation & Verification

**Scholak, Torsten, Nathan Schucher, and Dzmitry Bahdanau.** "PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models." *Proceedings of EMNLP*, 2021. arXiv:2109.05093.\
<https://arxiv.org/abs/2109.05093> | Code: <https://github.com/ServiceNow/picard>

**Liu, Alisa, et al.** "DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts." *Proceedings of ACL*, 2021. arXiv:2105.03023.\
<https://arxiv.org/abs/2105.03023>

**Rebedea, Traian, et al.** "NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails." *Proceedings of EMNLP* (System Demonstrations), 2023. arXiv:2310.10501.\
<https://arxiv.org/abs/2310.10501> | Code: <https://github.com/NVIDIA/NeMo-Guardrails>

**Zelikman, Eric, et al.** "STaR: Bootstrapping Reasoning With Reasoning." *Advances in Neural Information Processing Systems* 35 (NeurIPS 2022). arXiv:2203.14465.\
<https://arxiv.org/abs/2203.14465> | Code: <https://github.com/ezelikman/STaR>

**Lightman, Hunter, et al.** "Let's Verify Step by Step." *Advances in Neural Information Processing Systems* 37 (NeurIPS 2024). arXiv:2305.20050.\
<https://arxiv.org/abs/2305.20050> | Data: <https://github.com/openai/prm800k>

***

## LLM-to-Logic-Solver Integration

**Pan, Liangming, et al.** "Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning." *Findings of EMNLP*, 2023. arXiv:2305.12295.\
<https://arxiv.org/abs/2305.12295> | Code: <https://github.com/teacherpeterpan/Logic-LLM>

**Ye, Xi, et al.** "SatLM: Satisfiability-Aided Language Models Using Declarative Prompting." *Advances in Neural Information Processing Systems* 36 (NeurIPS 2023). arXiv:2305.09656.\
<https://arxiv.org/abs/2305.09656>

**Olausson, Theo X., et al.** "LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers." *Proceedings of EMNLP* (Outstanding Paper Award), 2023. arXiv:2310.15164.\
<https://arxiv.org/abs/2310.15164> | Code: <https://github.com/benlipkin/linc>

***

## State-of-the-Art Planners

**Helmert, Malte.** "The Fast Downward Planning System." *Journal of Artificial Intelligence Research* 26 (2006): 191–246.\
<https://doi.org/10.1613/jair.1879> | Code: <https://github.com/aibasel/downward>

**Richter, Silvia, and Matthias Westphal.** "The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks." *Journal of Artificial Intelligence Research* 39 (2010): 127–177.\
<https://doi.org/10.1613/jair.2972>

**Gerevini, Alfonso, Alessandro Saetti, and Ivan Serina.** "An Approach to Efficient Planning with Numerical Fluents and Multi-Criteria Plan Quality." *Artificial Intelligence* 172.8–9 (2008): 899–944.\
<https://doi.org/10.1016/j.artint.2007.11.020>

**Rintanen, Jussi.** "Madagascar: Scalable Planning with SAT." *Proceedings of IPC 8 Planner Abstracts*, 2014.\
<https://research.it.uts.edu.au/magic/Jussi/satplan.html>

**Corrêa, Augusto B., et al.** "Lifted Successor Generation Using Query Optimization Techniques." *Proceedings of ICAPS*, 2020.\
<https://arxiv.org/abs/2001.08705> | Code: <https://github.com/abcorrea/powerlifted>

**Alkhazraji, Yusra, et al.** "Pyperplan." GitHub repository, 2020.\
<https://github.com/aibasel/pyperplan>

**International Planning Competition (IPC) results and domain archive:**\
<https://planning.wiki> | <https://ipc.icaps-conference.org>


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