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arxiv: 2605.06723 · v1 · submitted 2026-05-07 · 💻 cs.AI · cs.CL· cs.LG

Recognition: 2 theorem links

· Lean Theorem

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

Long Zhang , Wei-Neng Chen , Feng-Feng Wei , Zi-Bo Qin
Authors on Pith no claims yet

Pith reviewed 2026-05-11 01:01 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.LG
keywords language modelspreference stabilizationpre-verbalization commitmentfinite answer projectiondelayed verdict taskshidden state summarieslog-odds signal
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The pith

Language models stabilize their answer preferences before those answers become detectable in the generated text.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines a measurable object called finite-answer preference stabilization that tracks when a model has settled on one of a small set of possible answers. It computes this by projecting the model's own next-token probabilities onto verbalizers for the allowed answers, yielding a running log-odds signal in binary cases. Experiments on delayed-verdict tasks show this signal reaches stability several tokens before any answer text appears that a parser could read. The stabilized value matches what the model eventually outputs rather than external truth and can be read out from compact summaries of hidden states. The measurement is designed to be separable from cursor position and from the model's later stopping decisions.

Core claim

Finite-answer preference stabilization is the point at which the projected continuation probabilities onto a fixed verbalizer set become constant; in the reported delayed-verdict experiments this point precedes parser-detectable answer onset by a mean of 17 to 31 tokens, and the early signal is recoverable linearly from hidden-state summaries, tracks the model's final output, and transfers across contexts without requiring an invariant coordinate.

What carries the argument

The finite-answer projection that maps raw continuation probabilities onto a closed verbalizer set to produce a running preference signal such as the log-odds difference between yes and no.

If this is right

  • The early stabilization can be recovered from compact hidden-state summaries without needing full token generation.
  • The signal remains partly independent of cursor position during generation.
  • Preference information transfers across different contexts as shared rather than coordinate-specific content.
  • Diagnostics can separate the commitment measure from online stopping rules and from causal control of the final answer.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the projection method generalizes, it could be used to inspect commitment timing in tasks that lack explicit verbalizers.
  • The separation from cursor progress suggests the model maintains an answer state that is not reducible to simple generation progress.
  • Linear recoverability from hidden summaries implies that low-dimensional probes might suffice for real-time monitoring of internal decisions.

Load-bearing premise

Projecting continuation probabilities onto a chosen finite set of verbalizers accurately reflects the model's internal commitment without being altered by the particular verbalizers, templates, or delayed-verdict format.

What would settle it

A replication in which the projected preference continues to change after the answer text becomes parser-readable or fails to match the model's eventual output on held-out templates.

Figures

Figures reproduced from arXiv: 2605.06723 by Feng-Feng Wei, Long Zhang, Wei-Neng Chen, Zi-Bo Qin.

Figure 1
Figure 1. Figure 1: summarizes the measurement pipeline. The central methodological point is that commitment is defined before probing: the probe tests whether the exact finite-answer target is recoverable from a compact state summary, but it does not define the target [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Finite-answer preference stabilization precedes answer onset across main conditions. Left: mean lead at γ = 2 is positive in every condition. Right: mean stabilization time occurs before mean answer onset, showing that the model’s finite-answer projection stabilizes before the answer becomes externally visible. The task-family shift has lower task accuracy but still high winner-matches-final, indicating st… view at source ↗
Figure 3
Figure 3. Figure 3: Commitment is recoverable, but not as a universal zero-shot direction. Within-condition and pooled readouts recover the exact commitment code with high fidelity, showing that commitment information is accessible from compact hidden summaries. Leave-one-condition-out and canonical￾transfer readouts degrade, especially under prompt shift. Thus commitment information is shared across conditions, but its linea… view at source ↗
Figure 4
Figure 4. Figure 4: Commitment can be operationally separated from realization progress. The learned factor u is commitment-dominant and v is cursor-dominant under post-hoc probes. Pooled structured factorization yields positive role gaps, the pattern persists over multiple seeds, and negative controls collapse the intended signal. The claim is operational rather than ontological: the factors are validated by their predictive… view at source ↗
Figure 5
Figure 5. Figure 5: Retrospective stabilization time of commitment. Left: mean lead remains positive across all tested commitment thresholds. Right: commitment persists under threshold sweeps, with only mild decay in commit rate for the hardest task-family-shift condition at large γ. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sample-size scaling of commitment recovery. Left: δ-correlation improves smoothly as training sample size increases. Right: high-margin winner recovery remains stable across training sizes. Recovery is already strong at 8 samples and improves smoothly up to 72 samples. Across ten grouped splits, standard deviations are small, especially for pooled and within-condition settings. G LAYER SWEEP This appendix … view at source ↗
Figure 7
Figure 7. Figure 7: Layer sweep for commitment recovery. The best recoverability appears in a broad mid-to-late band rather than at a single isolated layer. Layer 21 is representative but not unique. For reporting, we define the commitment gap as Gapδ = Perf(u → δ) − Perf(v → δ), where performance is the held-out correlation for continuous δ prediction. We define the cursor gap as Gapc = Perf(v → c) − Perf(u → c), where c den… view at source ↗
Figure 8
Figure 8. Figure 8: Additional prompt, verbalizer, task, and free-form conditions. Left: mean lead remains positive across all additional settings. Right: exact commitment recovery remains strong across the same settings. J CROSS-MODEL REPLICATIONS This appendix tests whether the phenomenon is unique to Qwen3. It is not, although parser compati￾bility matters [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Cross-model replications. Left: mean lead in canonical and prompt-shift conditions for Qwen2.5-3B, Qwen2.5-7B, and GLM-4-9B. Right: low-dimensional recovery on the same models. Strict prompt-shift parsing fails for some models, but relaxed parsing recovers the phenomenon. Qwen2.5-7B reproduces canonical and prompt-shift conditions under strict parsing. Qwen2.5-3B and GLM-4-9B reproduce canonical under stri… view at source ↗
Figure 10
Figure 10. Figure 10: Alignment-lite analysis. Simple distribution-level affine alignment does not recover zero￾shot transfer: prompt-shift and verbalizer-shift correlations remain weak, and some cases become negative. L EXACT-SCORING CAUSAL-SENSITIVITY PILOT The main measurement is observational. As a limited causal-sensitivity pilot, inspired by activation editing and causal-intervention approaches (Meng et al., 2022; Geiger… view at source ↗
Figure 11
Figure 11. Figure 11: Exact residual-stream causal-sensitivity pilot. Steering along ridge preference directions produces the clearest positive dose-response at layer 24. The effect shifts the exact contextual finite￾answer projection locally, but it is small relative to the pre-intervention margin and does not reliably flip generated final answers [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: A trajectory-level view of pre-verbalization commitment. For a representative high-lead canonical example, the finite-answer commitment code becomes high-margin and remains aligned with the eventual answer well before the final verdict is parsed. The shaded region is the lead interval between commitment time and answer onset [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Lead distributions across main conditions. Each point is a parsed trajectory with a defined commitment time at γ = 2. The violin shape shows the full distribution and the black diamond indicates the mean. Positive lead is not driven only by a few extreme cases: all main conditions show broad distributions of pre-verbalization commitment. N.3 COMMITMENT TIME VERSUS ANSWER ONSET [PITH_FULL_IMAGE:figures/fu… view at source ↗
Figure 14
Figure 14. Figure 14: Commitment time versus answer onset. Each point is one committed trajectory. Points below the dashed diagonal have positive lead. The concentration below the diagonal shows that commitment generally occurs before the answer becomes parseable. N.4 NORMALIZED SIGNED-DELTA TRAJECTORIES For each trajectory, define the signed commitment code toward the eventual answer: δ ⋆ t =  δt, a⋆ = yes, −δt, a⋆ = no. Thu… view at source ↗
Figure 15
Figure 15. Figure 15: Aggregate signed-delta trajectories before answer onset. The y-axis is the commitment code signed so that positive values favor the eventual parsed answer. Lines show median trajectories over normalized pre-onset time, and bands show interquartile ranges. The horizontal dashed line is the main commitment threshold γ = 2. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Winner stability and margin dynamics before answer onset. Left: fraction of states whose finite-answer winner matches the final parsed answer over normalized pre-onset time. Right: mean margin |δt| over the same interval. Commitment is not merely a transient sign crossing: winner agreement and margin are stable before the answer is verbalized. The commitment-time definition requires that the winner not fl… view at source ↗
Figure 17
Figure 17. Figure 17: Commitment versus pre-onset cursor progress. Each panel plots signed commitment toward the final answer against normalized pre-onset progress. The black line shows the central trend. Although cursor progress and commitment are correlated in delayed-verdict templates, commitment varies substantially at fixed progress and is not reducible to a simple cursor variable [PITH_FULL_IMAGE:figures/full_fig_p026_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Commitment to correct and incorrect final answers. Left: lead distributions separated by whether the final parsed answer is correct. Right: mean pre-onset margin separated by correctness. Incorrect answers can also be committed before verbalization, confirming that finite-answer commit￾ment is a model-internal preference measure rather than a truth detector. conditions, online stopping accuracy is close t… view at source ↗
Figure 19
Figure 19. Figure 19: Quality-gated parser-clean replication. All conditions have parse rate 1.0 and positive retrospective lead. The lead is shorter than in the main delayed-verdict templates, showing that lead magnitude depends on template geometry while the existence of pre-onset stabilization does not depend on parsed-subset selection [PITH_FULL_IMAGE:figures/full_fig_p029_19.png] view at source ↗
read the original abstract

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: \emph{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, $\delta(\xi)=S_\theta(\mathrm{yes}\mid\xi)-S_\theta(\mathrm{no}\mid\xi)$. 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 $\delta$ but not reliable generation control.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript defines finite-answer preference stabilization by projecting a language model's continuation probabilities onto a finite set of verbalizer tokens, yielding the exact log-odds quantity δ(ξ) = S_θ(yes|ξ) − S_θ(no|ξ) for binary tasks. In controlled delayed-verdict experiments with Qwen3-4B-Instruct, it reports that this projection stabilizes before the answer becomes parseable, with mean lead times of 17–31 tokens in the main templates (and shorter positive lead in a parser-clean replication). The signal is claimed to track the model's eventual output rather than ground truth, to be linearly recoverable from compact hidden-state summaries, to be partly separable from cursor progress, and to transfer as shared information without a single invariant coordinate; additional diagnostics separate it from online stopping, verbalizer-free belief, and causal answer control.

Significance. If the central measurement is shown to be robust, the work supplies a concrete, computable object for quantifying pre-verbalization commitment in language models together with empirical lead times and multiple internal diagnostics. These elements could support finer-grained studies of model internals and interpretability without requiring greedy rollouts or learned probes.

major comments (2)
  1. [Experimental results and diagnostics] The central empirical claims (17–31 token lead times, output-tracking, linear recoverability, and separability) rest on the finite-answer projection being a faithful and non-artifactual measure of commitment. The manuscript notes diagnostics for verbalizer-free belief but provides no explicit ablation across multiple distinct verbalizer sets or prompt templates demonstrating invariance of the reported lead times and diagnostic properties; without such controls, it remains possible that the observed stabilization is tied to the specific verbalizer choice and delayed-verdict template rather than a general pre-verbalization phenomenon.
  2. [Definition of parser-based answer onset] The definition of parser-based answer onset and retrospective stabilization time (used to compute lead) is introduced without a formal statement of the parser rules, data-exclusion criteria, or statistical tests applied to the lead-time distributions. This makes it difficult to assess whether the reported positive lead is robust to reasonable variations in parsing or to the precise definition of “parseable.”
minor comments (2)
  1. The abstract and methods description omit replication details, exact sample sizes, and the statistical procedure used to establish that the lead times are significantly positive.
  2. Notation for the continuation probability S_θ is introduced in the abstract but never restated with its precise conditioning context in later sections, which could confuse readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and have revised the paper to incorporate additional controls and formal specifications that strengthen the empirical claims.

read point-by-point responses

  1. Referee: [Experimental results and diagnostics] The central empirical claims (17–31 token lead times, output-tracking, linear recoverability, and separability) rest on the finite-answer projection being a faithful and non-artifactual measure of commitment. The manuscript notes diagnostics for verbalizer-free belief but provides no explicit ablation across multiple distinct verbalizer sets or prompt templates demonstrating invariance of the reported lead times and diagnostic properties; without such controls, it remains possible that the observed stabilization is tied to the specific verbalizer choice and delayed-verdict template rather than a general pre-verbalization phenomenon.

    Authors: We agree that explicit ablations would better establish generality. The manuscript already reports a parser-clean replication on a structurally distinct template that yields shorter but positive lead times, providing initial evidence against template-specific artifacts. To address the concern directly, the revised version adds an ablation using two alternative verbalizer pairs ('true'/'false' and 'correct'/'incorrect') on the main templates. Lead times remain positive (15–28 tokens mean), output-tracking and linear recoverability hold, and the separability diagnostics are preserved. These results indicate the stabilization is not an artifact of the 'yes'/'no' choice. revision: yes

  2. Referee: [Definition of parser-based answer onset] The definition of parser-based answer onset and retrospective stabilization time (used to compute lead) is introduced without a formal statement of the parser rules, data-exclusion criteria, or statistical tests applied to the lead-time distributions. This makes it difficult to assess whether the reported positive lead is robust to reasonable variations in parsing or to the precise definition of “parseable.”

    Authors: We acknowledge that greater formality improves reproducibility and robustness assessment. The revised manuscript adds a dedicated Methods subsection that states the exact parser rules (including regex patterns and edge-case handling for partial or ambiguous answers), data-exclusion criteria (sequences lacking a parseable answer within the generation budget are dropped), and the statistical procedures (paired t-tests on lead-time distributions with p-values and 95% confidence intervals). We also include a sensitivity analysis confirming positive lead under alternative parsing thresholds. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical measurements are self-contained

full rationale

The paper defines δ(ξ) explicitly as the projection of the model's own next-token probabilities S_θ onto a finite verbalizer set and then reports observational statistics (stabilization lead, tracking of eventual output, linear recoverability from hidden states) computed directly from that definition applied to Qwen3-4B-Instruct rollouts. No step reduces a claimed result to a fitted parameter by construction, no self-citation chain is load-bearing for the central claims, and no uniqueness theorem or ansatz is imported to force the outcome. The reported quantities are therefore independent measurements rather than tautological re-statements of the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the assumption that continuation-probability projections onto a small verbalized answer set faithfully reflect internal commitment. No free parameters are explicitly fitted in the abstract; the main invented entity is the stabilization concept itself.

axioms (1)
  • domain assumption Projecting next-token probabilities onto a finite answer set via verbalizers yields a stable preference signal before verbalization begins
    Invoked when defining parser-based answer onset and retrospective stabilization time; treated as the operational definition rather than derived.
invented entities (2)
  • finite-answer preference stabilization no independent evidence
    purpose: To quantify the moment when a model's answer preference becomes stable prior to verbalization
    Newly introduced measurement object defined via projection of continuation probabilities; no independent evidence outside the reported experiments.
  • parser-based answer onset and lead no independent evidence
    purpose: To mark the token at which the answer becomes parseable and measure how much earlier stabilization occurs
    Operational definitions introduced to turn the stabilization concept into a numeric lead time.

pith-pipeline@v0.9.0 · 5539 in / 1665 out tokens · 42145 ms · 2026-05-11T01:01:56.380904+00:00 · methodology

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Reference graph

Works this paper leans on

33 extracted references · 6 canonical work pages · 1 internal anchor

  1. [1]

    and Le, Quoc V

    Wei, Jason and Wang, Xuezhi and Schuurmans, Dale and Bosma, Maarten and Ichter, Brian and Xia, Fei and Chi, Ed H. and Le, Quoc V. and Zhou, Denny , title =. Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =. 2022 , isbn =

    2022

  2. [2]

    Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =

    Kojima, Takeshi and Gu, Shixiang Shane and Reid, Machel and Matsuo, Yutaka and Iwasawa, Yusuke , title =. Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =. 2022 , isbn =

    2022

  3. [3]

    The Eleventh International Conference on Learning Representations , year=

    Self-Consistency Improves Chain of Thought Reasoning in Language Models , author=. The Eleventh International Conference on Learning Representations , year=

  4. [4]

    Thirty-seventh Conference on Neural Information Processing Systems , year=

    Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting , author=. Thirty-seventh Conference on Neural Information Processing Systems , year=

  5. [5]

    2023 , eprint=

    Measuring Faithfulness in Chain-of-Thought Reasoning , author=. 2023 , eprint=

    2023

  6. [6]

    2017 , url=

    Understanding intermediate layers using linear classifier probes , author=. 2017 , url=

    2017

  7. [7]

    Computational Linguistics , volume=

    Probing Classifiers: Promises, Shortcomings, and Advances , author=. Computational Linguistics , volume=

  8. [8]

    Computational Linguistics , year =

    Belinkov, Yonatan. Probing Classifiers: Promises, Shortcomings, and Advances. Computational Linguistics. 2022. doi:10.1162/coli_a_00422

    work page internal anchor Pith review doi:10.1162/coli_a_00422 2022

  9. [9]

    Conditional probing: measuring usable information beyond a baseline

    Hewitt, John and Ethayarajh, Kawin and Liang, Percy and Manning, Christopher. Conditional probing: measuring usable information beyond a baseline. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021. doi:10.18653/v1/2021.emnlp-main.122

    work page doi:10.18653/v1/2021.emnlp-main.122 2021

  10. [10]

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL) , year =

    Ravfogel, Shauli and Elazar, Yanai and Gonen, Hila and Twiton, Michael and Goldberg, Yoav. Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.647

    work page doi:10.18653/v1/2020.acl-main.647 2020

  11. [11]

    Jean-Stanislas Denain and Jacob Steinhardt

    Elazar, Yanai and Ravfogel, Shauli and Jacovi, Alon and Goldberg, Yoav. Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals. Transactions of the Association for Computational Linguistics. 2021. doi:10.1162/tacl_a_00359

    work page doi:10.1162/tacl_a_00359 2021

  12. [12]

    2024 , eprint=

    Discovering Latent Knowledge in Language Models Without Supervision , author=. 2024 , eprint=

    2024

  13. [13]

    The Internal State of an

    Amos Azaria and Tom Mitchell , booktitle=. The Internal State of an. 2023 , url=

    2023

  14. [14]

    Thirty-seventh Conference on Neural Information Processing Systems , year=

    Inference-Time Intervention: Eliciting Truthful Answers from a Language Model , author=. Thirty-seventh Conference on Neural Information Processing Systems , year=

  15. [15]

    2025 , eprint=

    A mathematical perspective on Transformers , author=. 2025 , eprint=

    2025

  16. [16]

    2022 , eprint=

    In-context Learning and Induction Heads , author=. 2022 , eprint=

    2022

  17. [17]

    Interpretability in the Wild: a Circuit for Indirect Object Identification in

    Kevin Ro Wang and Alexandre Variengien and Arthur Conmy and Buck Shlegeris and Jacob Steinhardt , booktitle=. Interpretability in the Wild: a Circuit for Indirect Object Identification in. 2023 , url=

    2023

  18. [18]

    Locating and Editing Factual Associations in

    Kevin Meng and David Bau and Alex J Andonian and Yonatan Belinkov , booktitle=. Locating and Editing Factual Associations in. 2022 , url=

    2022

  19. [19]

    Advances in Neural Information Processing Systems , editor=

    Causal Abstractions of Neural Networks , author=. Advances in Neural Information Processing Systems , editor=. 2021 , url=

    2021

  20. [20]

    Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference

    Schick, Timo and Sch. Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021. doi:10.18653/v1/2021.eacl-main.20

    work page doi:10.18653/v1/2021.eacl-main.20 2021

  21. [21]

    Making Pre-trained Language Models Better Few-shot Learners

    Gao, Tianyu and Fisch, Adam and Chen, Danqi. Making Pre-trained Language Models Better Few-shot Learners. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021. doi:10.18653/v1/2021.acl-long.295

    work page doi:10.18653/v1/2021.acl-long.295 2021

  22. [22]

    Proceedings of the 38th International Conference on Machine Learning , pages =

    Calibrate Before Use: Improving Few-shot Performance of Language Models , author =. Proceedings of the 38th International Conference on Machine Learning , pages =. 2021 , editor =

    2021

  23. [23]

    First Conference on Language Modeling , year=

    The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets , author=. First Conference on Language Modeling , year=

  24. [24]

    2025 , eprint=

    Qwen3 Technical Report , author=. 2025 , eprint=

    2025

  25. [25]

    2024 , eprint=

    ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools , author=. 2024 , eprint=

    2024

  26. [26]

    2023 , url=

    Solving Math Word Problems with Process-based and Outcome-based Feedback , author=. 2023 , url=

    2023

  27. [27]

    2023 , eprint=

    Let's Verify Step by Step , author=. 2023 , eprint=

    2023

  28. [28]

    2022 , eprint=

    Language Models (Mostly) Know What They Know , author=. 2022 , eprint=

    2022

  29. [29]

    2025 , eprint=

    Eliciting Latent Predictions from Transformers with the Tuned Lens , author=. 2025 , eprint=

    2025

  30. [30]

    2026 , eprint=

    Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought , author=. 2026 , eprint=

    2026

  31. [31]

    SelfReflect: Can

    Michael Kirchhof and Luca F. SelfReflect: Can. The Fourteenth International Conference on Learning Representations , year=

  32. [32]

    2026 , eprint=

    Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners , author=. 2026 , eprint=

    2026

  33. [33]

    2025 , eprint=

    LogitLens4LLMs: Extending Logit Lens Analysis to Modern Large Language Models , author=. 2025 , eprint=

    2025