mlsanityData to 14 Oct 2026

Are language models aware of the road not taken? Token-level uncertainty and hidden state dynamics

arXiv 2511.04527 ↗First posted 6 Nov 2025cs.CL cs.AIcited by 11

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

Abstract (arXiv)

When a language model generates text, the selection of individual tokens might lead it down very different reasoning paths, making uncertainty difficult to quantify. In this work, we consider whether reasoning language models represent the alternate paths that they could take during generation. To test this hypothesis, we use hidden activations to control and predict a language model's uncertainty during chain-of-thought reasoning. In our experiments, we find a clear correlation between how uncertain a model is at different tokens, and how easily the model can be steered by controlling its activations. This suggests that activation interventions are most effective when there are alternate paths available to the model -- in other words, when it has not yet committed to a particular final answer. We also find that hidden activations can predict a model's future outcome distribution, demonstrating that models implicitly represent the space of possible paths.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 20 of its 24 references that are in the map. First posted on arXiv 6 Nov 2025.

Builds on

20 of 24 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Measuring Faithfulness in Chain-of-Thought Reasoning17 Jul
  2. 2022Large Language Models are Zero-Shot Reasoners24 May

Other references · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
  4. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
  5. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
  6. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  7. 2023Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMscited by 974
  8. 2022In-context Learning and Induction Headscited by 964
  9. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 938
  10. 2023Steering Language Models With Activation Engineeringcited by 922
  11. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 712
  12. 2022What learning algorithm is in-context learning? Investigations with linear modelscited by 684
6 more
  1. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 650
  2. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 256
  3. 2024A Primer on the Inner Workings of Transformer-based Language Modelscited by 85
  4. 2024ICLR: In-Context Learning of Representationscited by 50
  5. 2024Forking Paths in Neural Text Generationcited by 34
  6. 2023In-Context Learning Dynamics with Random Binary Sequencescited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 11.

11 papers.

  1. 2026Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thoughtcited by 40
  2. 2026Uncertainty Propagation in LLM-Based Systemscited by 3
  3. 2026The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safetycited by 2
  4. 2026Understanding the Dynamics of Demonstration Conflict in In-Context Learningcited by 2
  5. 2026Not All Denoising Steps Are Equal: Model Scheduling for Faster Masked Diffusion Language Modelscited by 1
  6. 2026Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chainscited by 1
  7. 2026How Language Models Fail: Token-Level Signatures of Committed and Persistent Reasoning Failurescited by 1
  8. 2026Shared Semantics, Divergent Mechanisms: Unsupervised Feature Discovery by Aligning Semantics and Mechanismscited by 0
  9. 2026Think-Probe-Respond: Improving Large Language Models as Judges of Research Idea Noveltycited by 0
  10. 2026Hesitation Has a Geometry: Entropy-Trained Hyperbolic Probes for Sparse Activation Steeringcited by 0
  11. 2026From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Reasoning Models Know What's Important, and Encode It in Their Activations
  2. 2026Dissecting Failure Dynamics in Large Language Model Reasoning
  3. 2026Reasoning as State Transition: A Representational Analysis of Reasoning Evolution in Large Language Models
  4. 2026Reasoning Models Don't Just Think Longer, They Move Differently
  5. 2026How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning
  6. 2025On the Effect of Uncertainty on Layer-wise Inference Dynamics
  7. 2024Understanding Hidden Computations in Chain-of-Thought Reasoning
  8. 2026Hidden Error Awareness in Chain-of-Thought Reasoning: The Signal Is Diagnostic, Not Causal