mlsanityData to 7 Oct 2026

When Does a Language Model Commit? A Finite-Answer Theory of Pre-Verbalization Commitment

arXiv 2605.06723 ↗First posted 7 May 2026cs.AI cs.CL cs.LGcited by 2

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

Language models often generate reasoning before giving a final answer, but the visible answer does not reveal when the model's answer preference became stable. We study this question through a narrow computable object: finite-answer preference stabilization. For a model state and specified answer verbalizers, we project the model's own continuation probabilities onto a finite answer set; in binary tasks this yields an exact log-odds code, δ(xi)=Sθ(yesmidxi)-Sθ(nomidxi). This target defines parser-based answer onset, retrospective stabilization time, and lead without relying on greedy rollouts or learned probes. In controlled delayed-verdict tasks with Qwen3-4B-Instruct, the contextual finite-answer projection stabilizes before the answer is parseable, with 17--31 token mean lead in the main templates and positive, shorter lead in a parser-clean replication. The signal tracks the model's eventual output rather than truth, is linearly recoverable from compact hidden summaries, is partly separable from cursor progress, and transfers as shared information without a single invariant coordinate. Diagnostics separate the measurement from online stopping, verbalizer-free belief, and causal answer control; exact steering shows local sensitivity of δ but not reliable generation control.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 27 of its 29 references that are in the map. First posted on arXiv 7 May 2026.

Builds on

27 of 29 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Language Models (Mostly) Know What They Know11 Jul

Other references · 26

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  4. 2022Locating and Editing Factual Associations in GPTcited by 2,609
  5. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  6. 2016Understanding intermediate layers using linear classifier probescited by 1,279
  7. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,259
  8. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  9. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
  10. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
  11. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,081
  12. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
14 more
  1. 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 831
  2. 2022Discovering Latent Knowledge in Language Models Without Supervisioncited by 733
  3. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 673
  4. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  5. 2023The Internal State of an LLM Knows When It's Lyingcited by 644
  6. 2023Eliciting Latent Predictions from Transformers with the Tuned Lenscited by 579
  7. 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 442
  8. 2021Causal Abstractions of Neural Networkscited by 401
  9. 2023A mathematical perspective on Transformerscited by 160
  10. 2021Conditional probing: measuring usable information beyond a baselinecited by 61
  11. 2026Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thoughtcited by 35
  12. 2025LogitLens4LLMs: Extending Logit Lens Analysis to Modern Large Language Modelscited by 22
  13. 2026Large Reasoning Models Are (Not Yet) Multilingual Latent Reasonerscited by 8
  14. 2025SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?cited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026TrAC: Trace-Conditioned Answer Consistency for Efficient Uncertainty Quantification in LLMscited by 0
  2. 2026Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulnesscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Large Language Models Decide Early and Explain Later
  2. 2025Answer-Centric or Reasoning-Driven? Uncovering the Latent Memory Anchor in LLMs
  3. 2026State commitment learning: training language models to distinguish computation from memory
  4. 2026When Confidence Rises Too Early: Detecting Shortcut Reasoning via Premature Answer Commitment
  5. 2026CALIBER: Calibrating Confidence Before and After Reasoning in Language Models
  6. 2026Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
  7. 2026Language-Conditioned Token and Reasoning Efficiency in Large Language Models: A Paired Cross-Lingual Study Protocol
  8. 2026Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models