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arxiv: 2605.11746 · v1 · submitted 2026-05-12 · 💻 cs.AI

Recognition: no theorem link

When Reasoning Traces Become Performative: Step-Level Evidence that Chain-of-Thought Is an Imperfect Oversight Channel

Wenkai Li , Fan Yang , Ananya Hazarika , Shaunak A. Mehta , Koichi Onoue
Authors on Pith no claims yet

Pith reviewed 2026-05-13 06:24 UTC · model grok-4.3

classification 💻 cs.AI
keywords chain-of-thoughtreasoning tracesmodel oversightlatent commitmentconfabulationlanguage modelsinterpretabilityalignment
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The pith

Chain-of-thought traces often keep generating text after models have internally committed to an answer.

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

The paper tests whether visible chain-of-thought reasoning stays in sync with the model's internal process of settling on an answer. Using a step-by-step detection method validated across multiple probes, it finds that the trace and the latent commitment match on only about 62 percent of steps on average. The most common mismatch is that the model has already decided internally while the trace continues to produce seemingly thoughtful steps that do not change the committed answer. This matters because chain-of-thought is used both to boost performance and to inspect or oversee model decisions, yet the visible text can be performative rather than a faithful record of when the answer was formed.

Core claim

Across nine models and seven benchmarks, latent commitment and explicit answer arrival align on only 61.9% of steps on average. The dominant mismatch pattern is confabulated continuation: 58.0% of detected mismatch events occur after the answer-commitment proxy has already stabilized while the trace continues producing deliberative-looking text, and a vacuousness analysis shows that the committed answer does not change during these steps.

What carries the argument

A step-level Detect-Classify-Compare framework built around an answer-commitment proxy that is cross-validated with Patchscopes, tuned-lens probes, and causal direction ablation.

Load-bearing premise

The answer-commitment proxy accurately identifies the internal step at which the model forms its final answer.

What would settle it

A truncation experiment that stops generation right after the proxy signals commitment and checks whether the final answer stays the same in the great majority of cases; frequent changes would indicate the proxy is not capturing the true commitment point.

Figures

Figures reproduced from arXiv: 2605.11746 by Ananya Hazarika, Fan Yang, Koichi Onoue, Shaunak A. Mehta, Wenkai Li.

Figure 1
Figure 1. Figure 1: Overview of the empirical picture. Left: on a ProsQA example, DS-R1-32B commits [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Representative belief trajectories. (a) Faithful (Gemma 3 27B/GPQA-D, BCA = 0.75): latent belief and CoT converge simultaneously. (b) Unfaithful (Qwen 3.5 9B/GPQA-D, BCA = 0.00): latent belief locks by step 2 while CoT continues exploring, yielding a large convergence timing gap. Model MATH PrOnto ProsQA MMLU-P BBH-LD BBH-TSO GPQA-D Mean Llama 3.1 8B 0.738 0.914 0.280 0.904 0.669 0.807 0.779 0.727 Qwen 3.5… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of the five unfaithfulness types across easy and hard benchmarks ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Direction ablation at selected layer depths. Removing the answer-token direction changes [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Architecture-controlled pipeline comparison. Left panels show [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: BCA versus CoT utility (∆ accuracy, pp) across 35 configurations (5 models × 7 bench￾marks). The Pearson correlation is significantly negative (r = −0.42, p = 0.012; bootstrap 95% CI [−0.64, −0.13]). ProsQA (high-leverage cluster at low BCA, high utility) is interpretable as the clearest capability–oversight trade-off regime (see Appendix I). Qwen 3.5 9B: amplification ranges from 516× (MMLU-Pro) to 2,466×… view at source ↗
Figure 7
Figure 7. Figure 7: (A) Pearson correlation matrix over 12,004 instance-level peak severities. The CT↔PC pair is highlighted: r = 0.937, 95% CI [+0.930, +0.943]. (B) Pure-subgroup paired ∆ (treatment minus control) for the three active categories with 95% bootstrap CIs. Hatched bars show ∆vs U (uniform random cut); solid bars show ∆vs N (matched neighbor δ = ±2 step). CS and HR both show significant positive ∆, the opposite o… view at source ↗
read the original abstract

Chain-of-thought (CoT) traces are increasingly used both to improve language model capability and to audit model behavior, implicitly assuming that the visible trace remains synchronized with the computation that determines the answer. We test this assumption with a step-level Detect-Classify-Compare framework built around an answer-commitment proxy that is cross-validated with Patchscopes, tuned-lens probes, and causal direction ablation. Across nine models and seven reasoning benchmarks, latent commitment and explicit answer arrival align on only 61.9% of steps on average. The dominant mismatch pattern is confabulated continuation: 58.0% of detected mismatch events occur after the answer-commitment proxy has already stabilized while the trace continues producing deliberative-looking text, and a vacuousness analysis shows that the committed answer does not change during these steps. In architecture-matched Qwen2.5/DeepSeek-R1-Distill comparisons, the reasoning pipeline changes failure composition more than aggregate alignment, most clearly at 32B where confabulated steps decrease as contradictory states increase. Lower step-level alignment is also associated with larger CoT utility, suggesting that the settings that benefit most from CoT are often the least temporally faithful. Paired truncation and a complementary donor-corruption test further indicate that much post-commitment text is not load-bearing for the final answer. These findings suggest that CoT can remain useful while still being an unreliable report of when the answer was formed.

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 / 3 minor

Summary. The paper claims that Chain-of-Thought (CoT) traces are imperfectly synchronized with the internal computation that forms the final answer. Using a Detect-Classify-Compare framework built on an answer-commitment proxy cross-validated via Patchscopes, tuned-lens probes, and causal-direction ablation, it reports that latent commitment and explicit answer arrival align on only 61.9% of steps across nine models and seven benchmarks. The dominant mismatch is confabulated continuation (58.0% of detected events), in which the trace continues generating deliberative text after the proxy has stabilized; truncation and donor-corruption tests indicate this post-commitment text is not load-bearing. Architecture-matched comparisons show that reasoning-pipeline failure composition changes more than aggregate alignment, and lower step-level alignment correlates with greater CoT utility.

Significance. If the results hold, the work is significant for interpretability and oversight research. It supplies step-level empirical evidence that CoT cannot be treated as a faithful report of when an answer is formed, with direct implications for using CoT in auditing or capability enhancement. The multi-method validation of the proxy, the load-bearing tests, and the finding that CoT utility is highest when temporal faithfulness is lowest are concrete contributions that could shift how CoT is deployed and evaluated.

major comments (2)
  1. The answer-commitment proxy is load-bearing for the 61.9% alignment and 58.0% confabulated-continuation statistics, yet the manuscript provides insufficient methodological detail on its exact construction, threshold rules, and quantitative agreement rates among the three validation methods (Patchscopes, tuned-lens, causal ablation). This absence prevents full assessment of the proxy's reliability and reproducibility of the central percentages.
  2. The reported association between lower step-level alignment and larger CoT utility is presented without error bars, sample sizes per comparison, or statistical controls, weakening the claim that the settings benefiting most from CoT are the least temporally faithful.
minor comments (3)
  1. The abstract and results sections report concrete percentages (61.9%, 58.0%) but omit confidence intervals or standard errors, which would help readers gauge variability across models and benchmarks.
  2. Data availability and code-release statements are missing; given the empirical nature of the work, these should be added to support verification of the alignment metrics and truncation experiments.
  3. Tables or figures summarizing per-model or per-benchmark alignment rates should include the number of steps or examples underlying each percentage to allow readers to assess statistical power.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for these constructive comments on methodological transparency and statistical presentation. Both points identify areas where the manuscript can be strengthened for reproducibility and rigor, and we will incorporate the requested details in revision.

read point-by-point responses

  1. Referee: The answer-commitment proxy is load-bearing for the 61.9% alignment and 58.0% confabulated-continuation statistics, yet the manuscript provides insufficient methodological detail on its exact construction, threshold rules, and quantitative agreement rates among the three validation methods (Patchscopes, tuned-lens, causal ablation). This absence prevents full assessment of the proxy's reliability and reproducibility of the central percentages.

    Authors: We agree that additional detail is needed. In the revised manuscript we will insert a dedicated Methods subsection that specifies the exact construction of the answer-commitment proxy (including token-level scoring rules and stabilization criteria), the precise threshold values chosen, and quantitative agreement statistics (pairwise concordance percentages and Cohen's kappa) among Patchscopes, tuned-lens, and causal-ablation validations. These metrics were computed during the study but summarized for brevity; they will now be reported in full with supporting tables. revision: yes

  2. Referee: The reported association between lower step-level alignment and larger CoT utility is presented without error bars, sample sizes per comparison, or statistical controls, weakening the claim that the settings benefiting most from CoT are the least temporally faithful.

    Authors: We accept this critique. The revision will augment the relevant results section and figure with error bars (standard errors across seeds), explicit per-comparison sample sizes, and statistical controls (e.g., partial correlation or regression models that account for model scale and benchmark difficulty). These additions will allow readers to assess the robustness of the alignment-utility relationship. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's derivation relies on empirical measurement of alignment between an answer-commitment proxy and explicit CoT steps, with the proxy cross-validated via Patchscopes, tuned-lens probes, causal-direction ablation, truncation experiments, and donor-corruption tests. These checks are independent of the target statistics (61.9% alignment, 58% confabulated continuation) and do not reduce any reported quantity to a fitted parameter or self-referential definition. No equations or claims in the abstract or context exhibit self-definitional structure, fitted-input-as-prediction, load-bearing self-citation, or ansatz smuggling; the central findings remain falsifiable observations rather than tautologies.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the unproven accuracy of the answer-commitment proxy and the assumption that the listed probing techniques provide independent validation of internal states.

axioms (2)
  • domain assumption The answer-commitment proxy accurately detects the timing of internal answer formation
    Core to the Detect-Classify-Compare framework and all reported alignment percentages
  • domain assumption Patchscopes, tuned-lens probes, and causal direction ablation provide valid cross-validation of the proxy
    Invoked to support the reliability of the 61.9% alignment figure

pith-pipeline@v0.9.0 · 5582 in / 1335 out tokens · 37066 ms · 2026-05-13T06:24:42.360195+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

70 extracted references · 70 canonical work pages · 14 internal anchors

  1. [1]

    Understanding intermediate layers using linear classifier probes

    Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes.arXiv preprint arXiv:1610.01644, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  2. [2]

    Chain-of-thought reasoning in the wild is not always faithful.arXiv preprint arXiv: 2503.08679, 2025

    Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, and Arthur Conmy. Chain-of-thought reasoning in the wild is not always faithful.arXiv preprint arXiv:2503.08679, 2025

    work page arXiv 2025

  3. [3]

    The internal state of an LLM knows when it’s lying

    Amos Azaria and Tom Mitchell. The internal state of an LLM knows when it’s lying. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 967–976, 2023

    work page 2023

  4. [4]

    Probing classifiers: Promises, shortcomings, and advances.Computational Linguistics, 48(1):207–219, 2022

    Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances.Computational Linguistics, 48(1):207–219, 2022

    work page 2022

  5. [5]

    Eliciting latent predictions from transformers with the tuned lens

    Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. Eliciting latent predictions from transformers with the tuned lens. InAdvances in Neural Information Processing Systems, volume 36, 2023

    work page 2023

  6. [6]

    Reasoning theater: Disentangling model beliefs from chain-of- thought, 2026

    Siddharth Boppana, Annabel Ma, Max Loeffler, Raphael Sarfati, Eric Bigelow, Atticus Geiger, Owen Lewis, and Jack Merullo. Reasoning theater: Disentangling model beliefs from chain-of- thought.arXiv preprint arXiv:2603.05488, 2026

    work page arXiv 2026

  7. [7]

    Discovering latent knowledge in language models without supervision

    Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. InInternational Conference on Learning Representations, 2023

    work page 2023

  8. [8]

    Reasoning Models Don't Always Say What They Think

    Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schul- man, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, Vlad Mikulik, Samuel R Bowman, Jan Leike, Jared Kaplan, and Ethan Perez. Reasoning models don’t always say what they think.arXiv preprint arXiv:2505.05410, 2025

    work page internal anchor Pith review arXiv 2025

  9. [9]

    Training Verifiers to Solve Math Word Problems

    Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  10. [10]

    Faithful reasoning using large language models.arXiv preprint arXiv:2208.14271, 2022

    Antonia Creswell and Murray Shanahan. Faithful reasoning using large language models.arXiv preprint arXiv:2208.14271, 2022

    work page arXiv 2022

  11. [11]

    ERASER: A benchmark to evaluate rationalized NLP models

    Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C Wallace. ERASER: A benchmark to evaluate rationalized NLP models. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4443–4458, 2020

    work page 2020

  12. [12]

    The Llama 3 Herd of Models

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The Llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  13. [13]

    When chain of thought is necessary, language models struggle to evade monitors.arXiv preprint arXiv:2507.05246, 2025

    Scott Emmons, Erik Jenner, David K Elson, Rif A Saurous, Senthooran Rajamanoharan, Heng Chen, Irhum Shafkat, and Rohin Shah. When chain of thought is necessary, language models struggle to evade monitors.arXiv preprint arXiv:2507.05246, 2025

    work page arXiv 2025

  14. [14]

    Causal abstraction: A theoretical foundation for mechanistic interpretability.Journal of Machine Learning Research, 26(83):1–64, 2025

    Atticus Geiger, Duligur Ibeling, Amir Zur, Maheep Chaudhary, Sonakshi Chauhan, Jing Huang, Aryaman Arora, Zhengxuan Wu, Noah Goodman, Christopher Potts, et al. Causal abstraction: A theoretical foundation for mechanistic interpretability.Journal of Machine Learning Research, 26(83):1–64, 2025

    work page 2025

  15. [15]

    Patchscopes: A unifying framework for inspecting hidden representations of language models

    Asma Ghandeharioun, Avi Caciularu, Adam Pearce, Lucas Dixon, and Mor Geva. Patchscopes: A unifying framework for inspecting hidden representations of language models. InProceedings of the 41st International Conference on Machine Learning, 2024

    work page 2024

  16. [16]

    2023 , archivePrefix=

    Nicholas Goldowsky-Dill, Chris MacLeod, Lucas Sato, and Aryaman Arora. Localizing model behavior with path patching.arXiv preprint arXiv:2304.05969, 2023

    work page arXiv 2023

  17. [17]

    Gemma 3 Technical Report

    Google DeepMind. Gemma 3 technical report.arXiv preprint arXiv:2503.19786, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [18]

    Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.Nature, 645(8081):633–638, 2025

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.Nature, 645(8081):633–638, 2025

    work page 2025

  19. [19]

    Kilem L. Gwet. Computing inter-rater reliability and its variance in the presence of high agreement.British Journal of Mathematical and Statistical Psychology, 61(1):29–48, 2008. 10

    work page 2008

  20. [20]

    Training Large Language Models to Reason in a Continuous Latent Space

    Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space.arXiv preprint arXiv:2412.06769, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  21. [21]

    Counterfactual simulation training for chain-of-thought faithfulness

    Peter Hase and Christopher Potts. Counterfactual simulation training for chain-of-thought faithfulness.arXiv preprint arXiv:2602.20710, 2026

    work page arXiv 2026

  22. [22]

    InFindings of the Association for Computational Linguistics: ACL 2025, pages 18974–18988, Vienna, Austria

    Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Tsygankova, Alejandro Baez, et al. Teaching large language models to reason with reinforcement learning.arXiv preprint arXiv:2403.04642, 2024

    work page arXiv 2024

  23. [23]

    Measuring mathematical problem solving with the MATH dataset

    Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the MATH dataset. InThirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2021

    work page 2021

  24. [24]

    Large language models cannot self-correct reasoning yet

    Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xiny- ing Song, and Denny Zhou. Large language models cannot self-correct reasoning yet. In International Conference on Learning Representations, 2024

    work page 2024

  25. [25]

    Language Models (Mostly) Know What They Know

    Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. Language models (mostly) know what they know.arXiv preprint arXiv:2207.05221, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  26. [26]

    Large language models are zero-shot reasoners

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners. InAdvances in Neural Information Processing Systems, volume 35, pages 22199–22213, 2022

    work page 2022

  27. [27]

    Chain of thought monitorability: A new and fragile opportunity for ai safety.arXiv preprint arXiv: 2507.11473, 2025

    Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen, Alan Cooney, Allan Dafoe, Anca Dragan, et al. Chain of thought monitorability: A new and fragile opportunity for ai safety.arXiv preprint arXiv:2507.11473, 2025

    work page arXiv 2025

  28. [28]

    Measuring Faithfulness in Chain-of-Thought Reasoning

    Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, et al. Measuring faithfulness in chain-of-thought reasoning.arXiv preprint arXiv:2307.13702, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  29. [29]

    Inference- time intervention: Eliciting truthful answers from a language model

    Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Inference- time intervention: Eliciting truthful answers from a language model. InAdvances in Neural Information Processing Systems, volume 36, 2023

    work page 2023

  30. [30]

    Persona Non Grata: Single-Method Safety Evaluation Is Incomplete for Persona-Imbued LLMs

    Wenkai Li, Fan Yang, Shaunak A. Mehta, and Koichi Onoue. Persona non grata: Single-method safety evaluation is incomplete for persona-imbued llms, 2026. URL https://arxiv.org/ abs/2604.11120

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  31. [31]

    Bench- marking and improving generator-validator consistency of language models

    Xiang Lisa Li, Vaishnavi Shrivastava, Siyan Li, Tatsunori Hashimoto, and Percy Liang. Bench- marking and improving generator-validator consistency of language models. InInternational Conference on Learning Representations, 2024

    work page 2024

  32. [32]

    Let’s verify step by step

    Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step. In International Conference on Learning Representations, 2024

    work page 2024

  33. [33]

    Reasoning under pressure: How do training incentives influence chain-of-thought monitorability?arXiv preprint arXiv:2512.00218, 2025

    Matt MacDermott, Qiyao Wei, Rada Djoneva, and Francis Rhys Ward. Reasoning under pressure: How do training incentives influence chain-of-thought monitorability?arXiv preprint arXiv:2512.00218, 2025

    work page arXiv 2025

  34. [34]

    Self-refine: Iterative refinement with self-feedback

    Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback. InAdvances in Neural Information Processing Systems, volume 36, 2023

    work page 2023

  35. [35]

    The geometry of truth: Emergent linear structure in large lan- guage model representations of true/false datasets

    Samuel Marks and Max Tegmark. The geometry of truth: Emergent linear structure in large lan- guage model representations of true/false datasets. InFirst Conference on Language Modeling, 2024

    work page 2024

  36. [36]

    Mea- suring chain-of-thought monitorability through faithfulness and verbosity.arXiv preprint arXiv:2510.27378, 2025

    Austin Meek, Eitan Sprejer, Iván Arcuschin, Austin J Brockmeier, and Steven Basart. Mea- suring chain-of-thought monitorability through faithfulness and verbosity.arXiv preprint arXiv:2510.27378, 2025

    work page arXiv 2025

  37. [37]

    Interpreting GPT: The logit lens.LessWrong, 2020

    nostalgebraist. Interpreting GPT: The logit lens.LessWrong, 2020. https://www.lesswrong. com/posts/AcKRB8wDpdaN6v6ru/interpreting-gpt-the-logit-lens. 11

    work page 2020

  38. [38]

    Show Your Work: Scratchpads for Intermediate Computation with Language Models

    Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Biber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al. Show your work: Scratchpads for intermediate computation with language models.arXiv preprint arXiv:2112.00114, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  39. [39]

    Learning to reason with LLMs

    OpenAI. Learning to reason with LLMs. https://openai.com/index/ learning-to-reason-with-llms/, 2024

    work page 2024

  40. [40]

    Performative thinking? the brittle correlation between cot length and problem complexity.arXiv preprint arXiv:2509.07339, 2025

    Vardhan Palod, Karthik Valmeekam, Kaya Stechly, and Subbarao Kambhampati. Performative thinking? the brittle correlation between cot length and problem complexity.arXiv preprint arXiv:2509.07339, 2025

    work page arXiv 2025

  41. [41]

    Making reasoning matter: Measuring and improving faithfulness of chain-of-thought reasoning

    Debjit Paul, Robert West, Antoine Bosselut, and Boi Faltings. Making reasoning matter: Measuring and improving faithfulness of chain-of-thought reasoning. InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 15012–15032, 2024

    work page 2024

  42. [42]

    Ques- tion decomposition improves the faithfulness of model-generated reasoning.arXiv preprint arXiv:2307.11768, 2023

    Ansh Radhakrishnan, Karina Nguyen, Anna Chen, Carol Chen, Carson Denison, Danny Her- nandez, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamil ˙e Lukoši¯ut˙e, et al. Ques- tion decomposition improves the faithfulness of model-generated reasoning.arXiv preprint arXiv:2307.11768, 2023

    work page arXiv 2023

  43. [43]

    GPQA: A graduate-level Google-proof Q&A benchmark

    David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R Bowman. GPQA: A graduate-level Google-proof Q&A benchmark. InFirst Conference on Language Modeling, 2024

    work page 2024

  44. [44]

    Preventing language models from hiding their reasoning

    Fabien Roger and Ryan Greenblatt. Preventing language models from hiding their reasoning. arXiv preprint arXiv:2310.18512, 2023

    work page arXiv 2023

  45. [45]

    Language models are greedy reasoners: A systematic formal analysis of chain-of-thought

    Abulhair Saparov and He He. Language models are greedy reasoners: A systematic formal analysis of chain-of-thought. InInternational Conference on Learning Representations, 2023

    work page 2023

  46. [46]

    Testing the general deductive reasoning capacity of large lan- guage models using OOD examples

    Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Mehran Kazemi, Najoung Kim, and He He. Testing the general deductive reasoning capacity of large lan- guage models using OOD examples. InAdvances in Neural Information Processing Systems, volume 36, 2023

    work page 2023

  47. [47]

    Faithcot-bench: Benchmarking instance-level faithfulness of chain-of-thought reasoning

    Xu Shen, Song Wang, Zhen Tan, Laura Yao, Xinyu Zhao, Kaidi Xu, Xin Wang, and Tianlong Chen. Faithcot-bench: Benchmarking instance-level faithfulness of chain-of-thought reasoning. arXiv preprint arXiv:2510.04040, 2025

    work page arXiv 2025

  48. [48]

    Challenging BIG-Bench tasks and whether chain-of-thought can solve them

    Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, and Jason Wei. Challenging BIG-Bench tasks and whether chain-of-thought can solve them. InFindings of the Association for Computational Linguistics: ACL 2023, pages 13003–13051, 2023

    work page 2023

  49. [49]

    On the hardness of faithful chain-of-thought reasoning in large language models.arXiv preprint arXiv:2406.10625, 2024

    Sree Harsha Tanneru, Dan Ley, Chirag Agarwal, and Himabindu Lakkaraju. On the hardness of faithful chain-of-thought reasoning in large language models.arXiv preprint arXiv:2406.10625, 2024

    work page arXiv 2024

  50. [50]

    Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting

    Miles Turpin, Julian Michael, Ethan Perez, and Samuel Bowman. Language models don’t always say what they think: Unfaithful explanations in chain-of-thought prompting. InAdvances in Neural Information Processing Systems, volume 36, 2023

    work page 2023

  51. [51]

    Measuring chain of thought faithfulness by unlearning reasoning steps

    Martin Tutek, Fateme Hashemi Chaleshtori, Ana Marasovi´c, and Yonatan Belinkov. Measuring chain of thought faithfulness by unlearning reasoning steps. InProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 9946–9971, 2025

    work page 2025

  52. [52]

    Solving math word problems with process- and outcome-based feedback

    Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process- and outcome-based feedback.arXiv preprint arXiv:2211.14275, 2022

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  53. [53]

    Investigating gender bias in language models using causal mediation analysis

    Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. Investigating gender bias in language models using causal mediation analysis. InAdvances in Neural Information Processing Systems, volume 33, pages 12388–12401, 2020

    work page 2020

  54. [54]

    Math-Shepherd: Verify and reinforce LLMs step-by-step without human annotations

    Peiyi Wang, Lei Li, Zhihong Shao, Run-Ze Xu, Damai Dai, Yifei Li, Deli Chen, Yu Wu, and Zhifang Sui. Math-Shepherd: Verify and reinforce LLMs step-by-step without human annotations. InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9426–9439, 2024. 12

    work page 2024

  55. [55]

    Chain-of-thought reasoning without prompting

    Xuezhi Wang and Denny Zhou. Chain-of-thought reasoning without prompting. InAdvances in Neural Information Processing Systems, volume 37, 2024

    work page 2024

  56. [56]

    Self-consistency improves chain of thought reasoning in language models

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. InInternational Conference on Learning Representations, 2023

    work page 2023

  57. [57]

    MMLU-Pro: A more robust and challenging multi-task language understanding benchmark

    Yubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran Arulraj, Xuan He, Ziyan Jiang, et al. MMLU-Pro: A more robust and challenging multi-task language understanding benchmark. InAdvances in Neural Information Processing Systems, volume 37, 2024

    work page 2024

  58. [58]

    CoRR , volume =

    Zhenyu Wang. LogitLens4LLMs: Extending logit lens analysis to modern large language models.arXiv preprint arXiv:2503.11667, 2025

    work page arXiv 2025

  59. [59]

    Chain-of-thought prompting elicits reasoning in large language models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. Chain-of-thought prompting elicits reasoning in large language models. InAdvances in Neural Information Processing Systems, volume 35, pages 24824–24837, 2022

    work page 2022

  60. [60]

    Teach me to explain: A review of datasets for explainable NLP

    Sarah Wiegreffe and Ana Marasovic. Teach me to explain: A review of datasets for explainable NLP. InProceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, 2021

    work page 2021

  61. [61]

    Qwen2.5 Technical Report

    An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  62. [62]

    Qwen3 Technical Report

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  63. [63]

    Tree of thoughts: Deliberate problem solving with large language models

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models. In Advances in Neural Information Processing Systems, volume 36, 2023

    work page 2023

  64. [64]

    Mechanistic evidence for faithfulness decay in chain-of-thought reasoning.arXiv preprint arXiv:2602.11201,

    Donald Ye, Max Loffgren, Om Kotadia, and Linus Wong. Mechanistic evidence for faithfulness decay in chain-of-thought reasoning.arXiv preprint arXiv:2602.11201, 2026

    work page arXiv 2026

  65. [65]

    Lie to me: How faithful is chain-of-thought reasoning in reasoning models?arXiv preprint arXiv:2603.22582,

    Richard J Young. Lie to me: How faithful is chain-of-thought reasoning in reasoning models? arXiv preprint arXiv:2603.22582, 2026

    work page arXiv 2026

  66. [66]

    Why models know but don’t say: Chain-of-thought faithfulness diver- gence between thinking tokens and answers in open-weight reasoning models.arXiv preprint arXiv:2603.26410, 2026

    Richard J Young. Why models know but don’t say: Chain-of-thought faithfulness diver- gence between thinking tokens and answers in open-weight reasoning models.arXiv preprint arXiv:2603.26410, 2026

    work page arXiv 2026

  67. [67]

    STaR: Bootstrapping reasoning with reasoning

    Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. STaR: Bootstrapping reasoning with reasoning. InAdvances in Neural Information Processing Systems, volume 35, 2022

    work page 2022

  68. [68]

    Can reasoning models obfuscate reasoning? stress-testing chain-of-thought monitorability.arXiv preprint arXiv:2510.19851, 2025

    Artur Zolkowski, Wen Xing, David Lindner, Florian Tramèr, and Erik Jenner. Can reasoning models obfuscate reasoning? stress-testing chain-of-thought monitorability.arXiv preprint arXiv:2510.19851, 2025

    work page arXiv 2025

  69. [69]

    Representation Engineering: A Top-Down Approach to AI Transparency

    Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, et al. Representation engineering: A top-down approach to AI transparency.arXiv preprint arXiv:2310.01405, 2023. A Model and Benchmark Specifications Table 2 provides the full architectural specifications for all mo...

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  70. [70]

    We therefore conservatively answer no because the annotation study is mentioned but review status is not described

    Institutional review board (IRB) approvals or equivalent for research with human subjects Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or ...

    work page