mlsanityData to 7 Oct 2026

Do language models plan ahead for future tokens?

arXiv 2404.00859 ↗First posted 1 Apr 2024cs.LG cs.CLcited by 47

Not on the map. Why?

Abstract (arXiv)

Do transformers "think ahead" during inference at a given position? It is known transformers prepare information in the hidden states of the forward pass at time step t that is then used in future forward passes t+τ. We posit two explanations for this phenomenon: pre-caching, in which off-diagonal gradient terms present during training result in the model computing features at t irrelevant to the present inference task but useful for the future, and breadcrumbs, in which features most relevant to time step t are already the same as those that would most benefit inference at time t+τ. We test these hypotheses by training language models without propagating gradients to past timesteps, a scheme we formalize as myopic training. In a constructed synthetic data setting, we find clear evidence for pre-caching. In the autoregressive language modeling setting, our experiments are more suggestive of the breadcrumbs hypothesis, though pre-caching increases with model scale.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). Builds on 22 of its 36 references that are in the map. First posted on arXiv 1 Apr 2024.

Builds on

22 of 36 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,818
  3. 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
  4. 2023Pythia: A Suite for Analyzing Large Language Models Across Training and Scalingcited by 1,734
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  7. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  8. 2023Progress measures for grokking via mechanistic interpretabilitycited by 822
  9. 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Headscited by 821
  10. 2017Crowdsourcing Multiple Choice Science Questionscited by 742
  11. 2019Compressive Transformers for Long-Range Sequence Modellingcited by 699
  12. 2019Designing and Interpreting Probes with Control Taskscited by 608
10 more
  1. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
  2. 2018Blockwise Parallel Decoding for Deep Autoregressive Modelscited by 318
  3. 2018Analysis Methods in Neural Language Processing: A Surveycited by 276
  4. 2020Understanding the Role of Individual Units in a Deep Neural Networkcited by 254
  5. 2020Information-Theoretic Probing for Linguistic Structurecited by 204
  6. 2023Linearity of Relation Decoding in Transformer Language Modelscited by 190
  7. 2020A Theory of Usable Information Under Computational Constraintscited by 153
  8. 2023Future Lens: Anticipating Subsequent Tokens from a Single Hidden Statecited by 116
  9. 2021Optimal Approximation Rate of ReLU Networks in terms of Width and Depthcited by 95
  10. 2018KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddingscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 15; 2025: 17; 2026: 15.

47 papers.

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  1. 2024Scaling Diffusion Language Models via Adaptation from Autoregressive Modelscited by 268
  2. 2024Let's Think Dot by Dot: Hidden Computation in Transformer Language Modelscited by 186
  3. 2024Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalizationcited by 78
  4. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 38
  5. 2024Activation Scaling for Steering and Interpreting Language Modelscited by 33
  6. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  7. 2025LLM Probability Concentration: How Alignment Shrinks the Generative Horizoncited by 26
  8. 2024Non-myopic Generation of Language Models for Reasoning and Planningcited by 25
  9. 2024AdvPrefix: An Objective for Nuanced LLM Jailbreakscited by 25
  10. 2025The Dual-Route Model of Inductioncited by 22
  11. 2024Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Modelscited by 21
  12. 2024AdaEAGLE: Optimizing Speculative Decoding via Explicit Modeling of Adaptive Draft Structurescited by 17
35 more
  1. 2025Emergent Response Planning in LLMscited by 17
  2. 2024Large Language Models Are Cross-Lingual Knowledge-Free Reasonerscited by 16
  3. 2025How Do LLMs Use Their Depth?cited by 15
  4. 2025L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Modelscited by 14
  5. 2025Is the Reversal Curse a Binding Problem? Uncovering Limitations of Transformers from a Basic Generalization Failurecited by 13
  6. 2025On Reasoning Strength Planning in Large Reasoning Modelscited by 13
  7. 2024Extracting Paragraphs from LLM Token Activationscited by 9
  8. 2026Large Reasoning Models Are (Not Yet) Multilingual Latent Reasonerscited by 8
  9. 2024Transforming Agency. On the mode of existence of Large Language Modelscited by 7
  10. 2026What's the plan? Metrics for implicit planning in LLMs and their application to rhyme generation and question answeringcited by 6
  11. 2026Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probingcited by 6
  12. 2025Looking beyond the next tokencited by 5
  13. 2026Latent Planning Emerges with Scalecited by 5
  14. 2024Quantum Hamiltonian Embedding of Images for Data Reuploading Classifierscited by 4
  15. 2024Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modelingcited by 4
  16. 2025Are Language Models Efficient Reasoners? A Perspective from Logic Programmingcited by 4
  17. 2025Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More Morecited by 3
  18. 2025Natural Language Tools: A Natural Language Approach to Tool Calling In Large Language Agentscited by 3
  19. 2025Modes of Sequence Models and Learning Coefficientscited by 2
  20. 2025ParaScopes: What do Language Models Activations Encode About Future Text?cited by 2
  21. 2026The State-Prediction Separation Hypothesiscited by 2
  22. 2024States Hidden in Hidden States: Implicit Discrete State Representations Emerge in LLMs' Hidden Statescited by 1
  23. 2025Detecting and Characterizing Planning in Language Modelscited by 1
  24. 2026Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generationcited by 1
  25. 2026Predicting Future Behaviors in Reasoning Models Enables Better Steeringcited by 1
  26. 2026Language Models Can Control Their Own Attentioncited by 1
  27. 2025Understanding the learned look-ahead behavior of chess neural networkscited by 0
  28. 2025Internal Planning in Language Models: Characterizing Horizon and Branch Awarenesscited by 0
  29. 2026Large Language Models Can Take False First Steps at Inference-time Planningcited by 0
  30. 2026Understanding the Emergence of Seemingly Useless Features in Next-Token Predictorscited by 0
  31. 2026Efficient Training-Free Multi-Token Prediction via Embedding-Space Probingcited by 0
  32. 2026A transformer architecture alteration to incentivise externalised reasoningcited by 0
  33. 2026The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corporacited by 0
  34. 2026When Tokenization is Secretly Output Supervisioncited by 0
  35. 2026Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspectivecited by 0