mlsanityData to 7 Oct 2026

Neural Turing Machines

arXiv 1410.5401 ↗First posted 20 Oct 2014cs.NEcited by 630

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Cited by 41 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.

Key facts. Cited by 630 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2014.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 1; 2021: 126; 2022: 97; 2023: 107; 2024: 86; 2025: 101; 2026: 111.

630 papers.

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  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,126
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  5. 2021Perceiver: General Perception with Iterative Attentioncited by 1,089
  6. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 871
  7. 2022Solving math word problems with process- and outcome-based feedbackcited by 871
  8. 2023WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instructcited by 618
  9. 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 599
  10. 2023MemoryBank: Enhancing Large Language Models with Long-Term Memorycited by 539
  11. 2022Memory-Based Model Editing at Scalecited by 443
  12. 2024Parallelizing Linear Transformers with the Delta Rule over Sequence Lengthcited by 373
38 more
  1. 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approachcited by 372
  2. 2024Titans: Learning to Memorize at Test Timecited by 303
  3. 2023Transformers learn to implement preconditioned gradient descent for in-context learningcited by 274
  4. 2022Transformer for Partial Differential Equations' Operator Learningcited by 270
  5. 2021Combinatorial optimization and reasoning with graph neural networkscited by 267
  6. 2021Escaping the Big Data Paradigm with Compact Transformerscited by 261
  7. 2022Transformers Learn Shortcuts to Automatacited by 260
  8. 2025MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agentcited by 235
  9. 2022Neural Networks and the Chomsky Hierarchycited by 214
  10. 2024BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystackcited by 211
  11. 2022Recurrent Memory Transformercited by 197
  12. 2024Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attentioncited by 194
  13. 2024InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memorycited by 192
  14. 2022Scalable Adaptive Computation for Iterative Generationcited by 190
  15. 2021PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learningcited by 181
  16. 2025Hierarchical Reasoning Modelcited by 167
  17. 2024Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streamscited by 160
  18. 2022A Review on Language Models as Knowledge Basescited by 156
  19. 2023Birth of a Transformer: A Memory Viewpointcited by 154
  20. 2025Dynamic Cheatsheet: Test-Time Learning with Adaptive Memorycited by 146
  21. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 136
  22. 2023Scalable Transformer for PDE Surrogate Modelingcited by 123
  23. 2024HGRN2: Gated Linear RNNs with State Expansioncited by 122
  24. 2025NoLiMa: Long-Context Evaluation Beyond Literal Matchingcited by 120
  25. 2022MeMOT: Multi-Object Tracking with Memorycited by 113
  26. 2021Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networkscited by 108
  27. 2022Retrieval Augmented Classification for Long-Tail Visual Recognitioncited by 106
  28. 2022The CLRS Algorithmic Reasoning Benchmarkcited by 103
  29. 2023Scaling Transformer to 1M tokens and beyond with RMTcited by 97
  30. 2021Structured Scene Memory for Vision-Language Navigationcited by 95
  31. 2022Constrained Few-shot Class-incremental Learningcited by 93
  32. 2021Neural Algorithmic Reasoningcited by 92
  33. 2024Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrappingcited by 89
  34. 2022Learning from Few Examples: A Summary of Approaches to Few-Shot Learningcited by 86
  35. 2022Remember Intentions: Retrospective-Memory-based Trajectory Predictioncited by 86
  36. 2021Going Beyond Linear Transformers with Recurrent Fast Weight Programmerscited by 84
  37. 2021Coordination Among Neural Modules Through a Shared Global Workspacecited by 83
  38. 2022Knowledge Tracing: A Surveycited by 76

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