mlsanityData to 7 Oct 2026

Emergent Response Planning in LLMs

arXiv 2502.06258 ↗First posted 10 Feb 2025cs.CL cs.LGcited by 17

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: their hidden representations encode future outputs beyond the next token. Through simple probing, we demonstrate that LLM prompt representations encode global attributes of their entire responses, including textitstructure attributes (e.g., response length, reasoning steps), textitcontent attributes (e.g., character choices in storywriting, multiple-choice answers at the end of response), and textitbehavior attributes (e.g., answer confidence, factual consistency). In addition to identifying response planning, we explore how it scales with model size across tasks and how it evolves during generation. The findings that LLMs plan ahead for the future in their hidden representations suggest potential applications for improving transparency and generation control.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 32 of its 48 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

32 of 48 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  5. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  6. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  7. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  8. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  9. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,239
  10. 2023Enhancing Chat Language Models by Scaling High-quality Instructional Conversationscited by 825
  11. 2023Steering Language Models With Activation Engineeringcited by 824
  12. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
20 more
  1. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
  2. 2023Dissecting Recall of Factual Associations in Auto-Regressive Language Modelscited by 512
  3. 2022Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Taskcited by 417
  4. 2016A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Storiescited by 413
  5. 2022Self-critiquing models for assisting human evaluatorscited by 372
  6. 2019Analysing Mathematical Reasoning Abilities of Neural Modelscited by 331
  7. 2024Token-Budget-Aware LLM Reasoningcited by 242
  8. 2024The pitfalls of next-token predictioncited by 156
  9. 2023Inspecting and Editing Knowledge Representations in Language Modelscited by 140
  10. 2023Future Lens: Anticipating Subsequent Tokens from a Single Hidden Statecited by 116
  11. 2021CREAK: A Dataset for Commonsense Reasoning over Entity Knowledgecited by 88
  12. 2024LLM Internal States Reveal Hallucination Risk Faced With a Querycited by 80
  13. 2024Learning How Hard to Think: Input-Adaptive Allocation of LM Computationcited by 67
  14. 2024Do language models plan ahead for future tokens?cited by 47
  15. 2024Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Modelscited by 32
  16. 2024Do LLMs "know" internally when they follow instructions?cited by 25
  17. 2024HSF: Defending against Jailbreak Attacks with Hidden State Filteringcited by 21
  18. 2023Weakly Supervised Detection of Hallucinations in LLM Activationscited by 20
  19. 2024Learning to Plan for Language Modeling from Unlabeled Datacited by 12
  20. 2024Extracting Paragraphs from LLM Token Activationscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 13.

17 papers.

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  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  2. 2025Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimizationcited by 23
  3. 2025SpecExit: Accelerating Large Reasoning Model via Speculative Exitcited by 9
  4. 2025Language Models Can Predict Their Own Behaviorcited by 8
  5. 2026WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoningcited by 7
  6. 2026Latent Planning Emerges with Scalecited by 5
  7. 2026The Hidden Signal of Verifier Strictness: Controlling and Improving Step-Wise Verification via Selective Latent Steeringcited by 5
  8. 2026From Simulation to Enaction: Post-trained language models recognize and react to their own generationscited by 3
  9. 2026How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMscited by 3
  10. 2026On the Overscaling Curse of Parallel Thinking: System Efficacy Contradicts Sample Efficiencycited by 1
  11. 2026How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoningcited by 1
  12. 2026How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understandingcited by 1
5 more
  1. 2026Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generationcited by 1
  2. 2026Large Language Models Can Take False First Steps at Inference-time Planningcited by 0
  3. 2026A Behavioural and Representational Evaluation of Goal-Directedness in Language Model Agentscited by 0
  4. 2026From Tokens to States: LLMs as a Special Case of World Models and the Continuous Path Beyondcited by 0
  5. 2026Signatures of semantic search in the activations of large language modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Understanding Large Language Models
  2. 2026A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners
  3. 2025Internal Planning in Language Models: Characterizing Horizon and Branch Awareness
  4. 2025Emergent Abilities in Large Language Models: A Survey
  5. 2026LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations
  6. 2025A Survey on Large Language Models with some Insights on their Capabilities and Limitations
  7. 2023On the Planning Abilities of Large Language Models : A Critical Investigation
  8. 2024Beyond the Black Box: A Statistical Model for LLM Reasoning and Inference