mlsanityData to 7 Oct 2026

Deep Learning for Symbolic Mathematics

arXiv 1912.01412 ↗First posted 2 Dec 2019cs.SC cs.LGcited by 266

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Cited by 26 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving differential equations. We propose a syntax for representing mathematical problems, and methods for generating large datasets that can be used to train sequence-to-sequence models. We achieve results that outperform commercial Computer Algebra Systems such as Matlab or Mathematica.

Key facts. Cited by 266 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 49; 2022: 51; 2023: 48; 2024: 47; 2025: 46; 2026: 25.

266 papers.

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  1. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  2. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  3. 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 611
  4. 2022Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofscited by 278
  5. 2023Mathematical Capabilities of ChatGPTcited by 263
  6. 2022Transformers Learn Shortcuts to Automatacited by 260
  7. 2022End-to-end symbolic regression with transformerscited by 195
  8. 2022HyperTree Proof Search for Neural Theorem Provingcited by 192
  9. 2021Neural Symbolic Regression that Scalescited by 185
  10. 2023MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Trainingcited by 175
  11. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  12. 2021Investigating the Limitations of Transformers with Simple Arithmetic Taskscited by 136
38 more
  1. 2021Proof Artifact Co-training for Theorem Proving with Language Modelscited by 130
  2. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  3. 2023LEGO-Prover: Neural Theorem Proving with Growing Librariescited by 108
  4. 2021SymbolicGPT: A Generative Transformer Model for Symbolic Regressioncited by 101
  5. 2022On the Paradox of Learning to Reason from Datacited by 93
  6. 2022Vision Transformers provably learn spatial structurecited by 92
  7. 2022Three things everyone should know about Vision Transformerscited by 90
  8. 2022Tutorial on amortized optimizationcited by 83
  9. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  10. 2022Towards Learning Universal Hyperparameter Optimizers with Transformerscited by 81
  11. 2021NaturalProofs: Mathematical Theorem Proving in Natural Languagecited by 80
  12. 2022NaturalProver: Grounded Mathematical Proof Generation with Language Modelscited by 72
  13. 2023Transformer-based Planning for Symbolic Regressioncited by 72
  14. 2023Length Generalization in Arithmetic Transformerscited by 64
  15. 2023Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasonerscited by 63
  16. 2021Linear algebra with transformerscited by 62
  17. 2022Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree searchcited by 61
  18. 2022Theory of Graph Neural Networks: Representation and Learningcited by 60
  19. 2023ODEFormer: Symbolic Regression of Dynamical Systems with Transformerscited by 58
  20. 2022SymFormer: End-to-end symbolic regression using transformer-based architecturecited by 56
  21. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  22. 2022Deep Symbolic Regression for Recurrent Sequencescited by 53
  23. 2021Complex Knowledge Base Question Answering: A Surveycited by 52
  24. 2021LIME: Learning Inductive Bias for Primitives of Mathematical Reasoningcited by 46
  25. 2023SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingcited by 41
  26. 2023Deep Generative Symbolic Regression with Monte-Carlo-Tree-Searchcited by 40
  27. 2021Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)cited by 38
  28. 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 37
  29. 2021Latent Execution for Neural Program Synthesiscited by 35
  30. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  31. 2022A Review of Emerging Research Directions in Abstract Visual Reasoningcited by 33
  32. 2022Formal Specifications from Natural Languagecited by 33
  33. 2023Learning the greatest common divisor: explaining transformer predictionscited by 33
  34. 2024PatternBoost: Constructions in Mathematics with a Little Help from AIcited by 32
  35. 2021Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spacescited by 30
  36. 2023Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentationcited by 29
  37. 2024In-Context Symbolic Regression: Leveraging Large Language Models for Function Discoverycited by 29
  38. 2024Global Lyapunov functions: a long-standing open problem in mathematics, with symbolic transformerscited by 29

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