Pith. sign in

REVIEW 4 major objections 4 minor 3 cited by

Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Late errors, not early ones, are the ones most likely to corrupt an LLM's final answer, and ASCoT prunes redundant steps while verifying that fragile tail.

desk verdict Late-stage fragility is a plausible idea, but this paper's own evidence contradicts itself and the injection protocol can't support the claim; the efficiency method may still have value. read the letter →

arxiv 2508.05282 v6 pith:FGUG2VB5 submitted 2025-08-07 cs.CL

Dongxu Zhang , Yujun Wu , Yiding Sun , Jinnan Yang , Ning Yang , Jihua Zhu , Miao Xin , Baoliang Tian This is my paper
classification cs.CL
keywords chain-of-thoughtreasoninglate-stagefragilityself-correctiontokenpruningpositionalerrorimpactLLMverificationGSM8KMATH-500
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Contrary to the usual cascading-failure intuition, this paper claims that an error in a chain-of-thought reasoning trace is most damaging when it occurs near the end: the model catches and repairs early slips but tends to accept a late miscalculation without scrutiny. The paper calls this 'Late-Stage Fragility' and builds a method, ASCoT (Adaptive Self-Correction Chain-of-Thought), around it. ASCoT first prunes redundant tokens, then scores each remaining step with a position-weighted risk estimate and launches a dual-path self-correction only on the flagged high-risk steps. On GSM8K and MATH-500, ASCoT cuts token use by 21-30% on LLaMA-3.1-8B while keeping the accuracy drop under two points, and the same pattern holds across Qwen2.5 3B/7B/14B models. If the fragility claim is right, verification effort in LLM reasoning should be concentrated at the end of the chain, not spread evenly.

What carries the argument

The load-bearing mechanism is the Positional Impact Score, an exponential function $I(k)=w_a e^{\alpha(k/K)}$ fitted to the error-injection experiments, which makes the danger of an error grow with its position $k$ in a $K$-step chain. It enters a per-step Risk Score $R(t_k)=I(k)(1-Q(t_k))$, where $Q(t_k)$ is a four-part quality assessment with a veto on logical or arithmetic failure. Steps whose risk exceeds a threshold $\tau$ are routed to a Multi-Perspective Self-Correction Engine that generates an intrinsic (self-reflection) and an extrinsic (independent regeneration) candidate and keeps the higher-quality one. The score is what lets ASCoT compress broadly while still protecting the frag

What would settle it

Take correct chains, inject an identical numeric error at an early and at the final step, and equalize the number of post-error steps (by truncating or appending correct steps); if the failure-rate gap between positions disappears, 'late-stage fragility' is an artifact of the protocol's shrinking remaining budget.

Watch

Extended reading notes

Core claim

Introducing controlled numeric and symbolic errors into otherwise correct GSM8K chains, the paper measures how far final accuracy falls as a function of the error's position. For a 4-step chain, a numeric error at step 2 drops accuracy by 14.64 points, while the same error at step 4 drops it by 51.69; a symbolic error at step 3 of a 3-step chain produces a 56.78-point drop. The paper interprets this as 'semantic commitment': as the model builds a coherent path it narrows its generative space, so the final calculation is no longer checked. To act on the finding, ASCoT fits an exponential Positional Impact Score $I(k)=w_a e^{\alpha(k/K)}$ to the injection data, uses it in a risk score $R(t_k)=

Load-bearing premise

The error-injection protocol injects one error and tells the model to continue from that point, so a late error has fewer remaining steps in which a correction could occur; the observed late-vs-early gap may be a property of the protocol rather than of the model's reasoning.

Editorial extensions

If this is right

  • If late steps are the fragile ones, verification budgets should be reallocated to the final steps of the chain rather than spread uniformly.
  • Aggressive compression is safe when the tail is guarded: ASCoT at ratios 0.7-0.9 loses less than 1.8 accuracy points on LLaMA-3.1-8B while cutting tokens by 21-30%.
  • Self-correction can be selective rather than global: only steps with position-weighted risk above threshold are corrected, avoiding the cost of rewriting the whole chain.
  • The positional pattern is not a quirk of one model: at 50% token reduction the Qwen2.5-14B variant keeps 91.6% accuracy, a 1.5-point loss.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct test of the proposed mechanism: if 'semantic commitment' is real, the model's token-level uncertainty on a correct chain should drop monotonically toward the final step, and an injected late error should no longer trigger the normal doubt signal.
  • The same position-weighted idea could be transferred to step-level reward models, giving verification weight to terminal steps instead of treating all reasoning steps equally.
  • If the exponential Positional Impact Score is stable across benchmarks and model families, it could serve as a reusable prior for where to spend verification compute in long-chain reasoning.
  • The flagged-step correction could be paired with an external calculator or code interpreter, making the correction verifiable instead of relying only on the model's self-evaluation.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper challenges the commonly held "cascading failure" hypothesis in chain-of-thought reasoning, claiming to identify a counter-intuitive "Late-Stage Fragility": errors injected later in a reasoning chain are more likely to corrupt the final answer than identical errors injected earlier. Based on this phenomenon, the authors propose ASCoT, a pipeline that prunes redundant CoT tokens, uses a verification manager with a position-dependent risk score to flag high-risk steps, and invokes a dual-path self-correction engine only for flagged steps. Experiments on GSM8K and MATH-500 with LLaMA-3.1-8B and Qwen2.5 models report token reductions of 21--30% with small accuracy drops, and the abstract claims new state-of-the-art performance. The central empirical phenomenon and the method's risk weighting both rest on the error-injection experiments in Section 4.3 and the fitted Positional Impact Score of Eq. (8).

Significance. If the late-stage fragility finding were valid, it would be a practically useful design principle: verification and correction budgets should be skewed toward later reasoning steps. The paper also makes a plausible efficiency contribution by demonstrating that trained compression plus selective correction can preserve accuracy while cutting tokens. The modular architecture is clearly described, with pseudocode, a case study, and detailed hyperparameters. However, the central measurement is confounded, and the main text and appendix contain irreconcilable numerical claims. As it stands, the phenomenon is not established, and the position-dependent risk score used by ASCoT is not empirically grounded.

major comments (4)
  1. [Section 4.3 / Appendix A.2.1, Table 4] The error-injection protocol confounds error position with the amount of remaining chain available for correction. After injecting an error at step k of a K-step chain, the model is prompted to resume from that point; an error at step k leaves K−k subsequent steps, while an error at step K leaves none. The monotonic increase in accuracy drop (14.64 → 29.56 → 51.69 for steps 2, 3, 4 in Table 4) is exactly what one expects from decreasing remaining-chain length alone, even with no position-dependent fragility. The "semantic commitment" explanation in Sections 4.4 and A.3 is an untested hypothesis; no experiment manipulates or measures it. Since Eq. (8) fits I(k) to these data, the central phenomenon and the AVM's risk weighting are not empirically established.
  2. [Table 4 / Appendix A.2.2 / Eq. (8) / Appendix A.1] The empirical foundation contains direct numerical contradictions. Table 4 reports numeric-error accuracy drops of 14.64, 29.56, and 51.69 percentage points for steps 2, 3, and 4 of 4-step chains. Appendix A.2.2 reports "relatively uniform" drops of 59.92%, 60.47%, and 61.70% for the same positions. These two sets of numbers cannot both describe the same experiment. Additionally, Eq. (8) defines I(k) = w_a·e^{α(k/K)}, while Appendix A.1 defines I(k) = w_a·e^{α(k−1)}. The fitted values of w_a and α are never reported. This makes the risk score R(t_k) in Eq. (9) non-reproducible and its behavior impossible to verify.
  3. [Abstract / Section 4.2 (Table 1)] The abstract claims "new sota performance," but Table 1 compares ASCoT only against two trivial baselines: length-reduction prompting and hard truncation. There is no comparison with existing self-correction, verification, or CoT-compression methods (e.g., self-consistency, verifier-guided decoding, LLMLingua-2, or other position-aware verification). The claimed superiority is therefore unsupported. Furthermore, the method is evaluated on GSM8K, the same benchmark used to fit the positional impact score, and no cross-benchmark transfer of the fitted weights is demonstrated.
  4. [Section 4.2 / Tables 1--2] Reported differences near the full-budget regime are small (0.1--0.7 percentage points at Ratio = 1.0 in Table 1, and similar in Table 2), yet no variance, confidence intervals, significance tests, or number of runs are reported. Without such information, it is not possible to assess whether ASCoT preserves accuracy at the full budget or whether the differences are within noise. This is especially important because the abstract's efficiency claim depends on small accuracy deltas being meaningful.
minor comments (4)
  1. [Figure 2 caption] The caption contains the non-English fragment "Figure2 ASCoT的pipeline", which appears to be an editing artifact. The caption should be rewritten.
  2. [Appendix A.2.2] The sentence "at step 4 a drop of 61.70%a miscalculation" is grammatically garbled and should be corrected. Also, the relation between Table 4 and the numbers reported in this appendix needs to be clarified or reconciled.
  3. [Section 4.3 / Table 4] The error-injection experiments do not state the number of problems used at each position, nor the standard deviation across repeated injections. Reporting only aggregate accuracies makes it impossible to assess the stability of the reported drops.
  4. [General] The phrase "Figure 3 illustrates this evaluation process" appears twice (once in Section 3.2 and once in Section 4.4), creating the impression of duplicated text. The paper also contains several typos (e.g., "ASCoTreduces" in the abstract, "a illustration" in the Figure 6 caption).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the fitted positional-impact score is an empirical model of the observed phenomenon, and benchmark evaluations are external measures.

full rationale

The paper's central claim, Late-Stage Fragility, is presented as an empirical observation from controlled error-injection experiments (Section 4.3, Table 4), not as a result derived from a fitted parameter. The Positional Impact Score I(k) in Eq. 8 is explicitly described as an empirical model fitted to the error-injection data, and the risk score R(t_k)=I(k)*(1-Q(t_k)) is a post-hoc calibration for the AVM module. This is model fitting, not a derivation that reduces to its own output. The downstream evaluations on GSM8K and MATH-500 report measured accuracy and token counts against Prompt and Truncation baselines; these outcomes are not statistically forced by the fitted I(k), because the correction decisions and final answers depend on separate LLM quality assessments and the MSCE engine. There are no load-bearing self-citations: the cited tools (LLMLingua-2, TokenSkip, LoRA, etc.) are external implementations, and no author-overlapping uniqueness theorem is invoked. The error-injection protocol's confound between step position and remaining chain length is a validity threat, and the inconsistency between Table 4 and Appendix A.2.2 weakens the empirical foundation, but neither constitutes circularity: the measurements are external evidence, not definitional equivalents of the conclusions. Therefore no circular step is exhibited, and the derivation chain is self-contained in the circularity sense.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central method depends on several fitted or unreported parameters (w_a, alpha, tau, w_c, w_u) and on a protocol assumption that confounds error position with remaining correction steps. The papers explanatory construct 'semantic commitment' is an invented mechanism without independent evidence. The benchmarks and the pruning model are imported from prior work.

free parameters (5)
  • w_a = not reported
    Base impact coefficient in the Positional Impact Score I(k), fitted to error-injection data (Eq. 8).
  • alpha = not reported
    Propagation rate in I(k), fitted to error-injection data (Eq. 8, Appendix A.1).
  • tau = not reported
    Risk threshold in the AVM; tunable and not reported in the paper.
  • w_c, w_u = not reported
    Weights balancing clarity and utility in the quality score Q(t_k) (Eq. 7).
  • gamma = 0.5-1.0 per experiment
    Compression ratio, chosen by hand for each run; the method's accuracy/token trade-off depends on it.
assumptions (5)
  • domain assumption The error-injection protocol measures error impact faithfully.
    The model resumes from the error point, so later errors have fewer remaining steps to be corrected; this confounds position with recovery distance (Section 4.3, A.2.1).
  • ad hoc to paper Early errors trigger a latent self-correction mechanism in the model.
    Invoked to explain why early errors are less harmful, but no direct evidence or measurement of this mechanism is provided (Section 4.3, Figure 6).
  • ad hoc to paper Semantic commitment narrows the model's generative space as the chain progresses.
    Proposed in Appendix A.3 as an explanation for late-stage fragility; no independent test or quantitative support is given.
  • domain assumption LLMLingua-2 token importance scores are a reliable basis for pruning reasoning chains.
    Taken from prior work [40] without verification in this setting; the pruning step depends entirely on this assumed reliability.
  • domain assumption GSM8K and MATH-500 ground-truth answers are correct and unambiguous.
    Standard benchmark assumption; required for the accuracy and utility measurements.
invented entities (1)
  • Semantic commitment
    purpose: Explains why the model fails to correct late-stage errors despite succeeding with early errors.
    Introduced in Appendix A.3 as a post-hoc conceptual mechanism with no direct measurement, no controlled test, and no external handle. It is presented as the cause of the observed fragility but is not independently evidenced.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning." pith.science (2026). https://pith.science/paper/FGUG2VB5

@misc{pith2026250805282,
  author       = {Pith},
  title        = {Pith review of: Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FGUG2VB5}},
  note         = {Machine review of arXiv:2508.05282}
}
abstract

While Chain-of-Thought (CoT) prompting empowers Large Language Models (LLMs), ensuring reasoning reliability remains an open challenge. Contrary to the prevailing cascading failure hypothesis which posits that early errors are most detrimental, we identify a counter-intuitive phenomenon termed \textbf{Late-Stage Fragility}: errors introduced in later reasoning stages are significantly more prone to corrupting final answers. To address this, we introduce ASCoT (Adaptive Self-Correction Chain-of-Thought), a method harmonizing efficiency with robust verification. ASCoT first employs semantic pruning to compress redundant steps, then utilizes an Adaptive Verification Manager (AVM) to prioritize high risk, late-stage steps via a positional impact score, triggering a Multi-Perspective Self-Correction Engine (MSCE) only when necessary. Experiments on GSM8K and MATH-500 demonstrate that ASCoT effectively reallocates computational resources: it reduces token usage by 21\%--30\% for LLaMA-3.1-8B with negligible accuracy drops ($<1.8\%$), achieving a superior trade-off between inference efficiency and reasoning fidelity.

Figures

Figures reproduced from arXiv: 2508.05282 by the authors.

Figure 1
Figure 1. The top part (a) illustrates the standard CoT rea [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of ASCoT Pipeline. ASCoT generates the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Performance of ASCoT on the Qwen2.5-Instruct [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: Performance comparison of ASCoT with varying maximum length constraints. We adjust the maximum length budget [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: A illustration of “Late-Stage Fragility". The model recovers from an early-stage calculation error, but fails when an [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Example generations by ASCoT, using Qwen2.5-Instruct 7B. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Parameter-Efficient Fine-Tuning for Spiking Point Cloud Models

    cs.CV 2026-07 conditional novelty 6.0 of 10

    SpikePEFT matches or beats full fine-tuning on spiking point-cloud benchmarks while updating only ~5% of parameters via neuron-dynamics and silent-state adapters.

  2. From Canonical to Complex: Benchmarking LLM Capabilities in Undergraduate Thermodynamics

    physics.ed-ph 2025-08 conditional novelty 6.0 of 10

    On a new 50-item thermodynamics benchmark, the best LLM scored 82%, below the authors' 95% tutoring-safety threshold, with diagram-based questions near chance.

  3. Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning

    cs.CL 2026-07 conditional novelty 5.0 of 10

    Warm-starting on-policy self-distillation with a correctness-filtered, prompt-switched SFT bootstrap improves compression and accuracy of reasoning traces across math benchmarks.

Reference graph

Works this paper leans on

59 extracted references · 12 canonical work pages · cited by 3 Pith papers

  1. [1]

    Panatchakorn Anantaprayoon, Masahiro Kaneko, and Naoaki Okazaki. 2025. Intent-Aware Self-Correction for Mitigating Social Biases in Large Language Models.arXiv preprint arXiv:2503.06011(2025)

    arXiv 2025

  2. [2]

    Linjiang Cao, Maonan Wang, and Xi Xiong. 2025. A Large Language Model- Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Win- dows.arXiv preprint arXiv:2505.06178(2025)

    arXiv 2025

  3. [3]

    Liang Chen, Haozhe Zhao, Tianyu Liu, Shuai Bai, Junyang Lin, Chang Zhou, and Baobao Chang. 2024. An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models. InEuropean Conference on Computer Vision. Springer, 19–35

    2024

  4. [4]

    Qiguang Chen, Libo Qin, Jinhao Liu, Dengyun Peng, Jiannan Guan, Peng Wang, Mengkang Hu, Yuhang Zhou, Te Gao, and Wanxiang Che. 2025. Towards reason- ing era: A survey of long chain-of-thought for reasoning large language models. arXiv preprint arXiv:2503.09567(2025)

    arXiv 2025

  5. [5]

    Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Lin- feng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, et al . 2024. Do not think that much for 2+ 3=? on the overthinking of o1-like llms.arXiv preprint arXiv:2412.21187(2024)

    arXiv 2024

  6. [6]

    Jishnu Ray Chowdhury and Cornelia Caragea. 2025. Zero-shot verification-guided chain of thoughts.arXiv preprint arXiv:2501.13122(2025)

    arXiv 2025

  7. [7]

    Yu-Neng Chuang, Tianwei Xing, Chia-Yuan Chang, Zirui Liu, Xun Chen, and Xia Hu. 2024. Learning to compress prompt in natural language formats.arXiv preprint arXiv:2402.18700(2024)

    arXiv 2024

  8. [8]

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

    arXiv 2021

Show all 59 references
  1. [9]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human...

    2019

  2. [10]

    Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, and Yue Zhang

  3. [11]

    Yifu Ding, Wentao Jiang, Shunyu Liu, Yongcheng Jing, Jinyang Guo, Yingjie Wang, Jing Zhang, Zengmao Wang, Ziwei Liu, Bo Du, et al . 2025. Dynamic parallel tree search for efficient llm reasoning.arXiv preprint arXiv:2502.16235 (2025)

    2025 arXiv

  4. [12]

    Qichen Fu, Minsik Cho, Thomas Merth, Sachin Mehta, Mohammad Rastegari, and Mahyar Najibi. 2024. Lazyllm: Dynamic token pruning for efficient long context llm inference.arXiv preprint arXiv:2407.14057(2024)

    2024 arXiv

  5. [13]

    Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, and Furu Wei. 2023. In- context autoencoder for context compression in a large language model.arXiv preprint arXiv:2307.06945(2023)

    2023 arXiv

  6. [14]

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al . 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948(2025)

    2025 arXiv

  7. [15]

    Hengdao Guo, Ciyan Zheng, Herbert Ho-Ching Iu, and Tyrone Fernando. 2017. A critical review of cascading failure analysis and modeling of power system. Renewable and Sustainable Energy Reviews80 (2017), 9–22

    2017

  8. [16]

    Tingxu Han, Zhenting Wang, Chunrong Fang, Shiyu Zhao, Shiqing Ma, and Zhenyu Chen. 2024. Token-budget-aware llm reasoning.arXiv preprint arXiv:2412.18547(2024)

    2024 arXiv

  9. [17]

    Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the math dataset.arXiv preprint arXiv:2103.03874(2021)

    2021 arXiv

  10. [18]

    Bairu Hou, Yang Zhang, Jiabao Ji, Yujian Liu, Kaizhi Qian, Jacob Andreas, and Shiyu Chang. 2025. Thinkprune: Pruning long chain-of-thought of llms via reinforcement learning, 2025.URL https://arxiv. org/abs/2504.01296(2025)

    2025 arXiv

  11. [19]

    Le Hou, Richard Yuanzhe Pang, Tianyi Zhou, Yuexin Wu, Xinying Song, Xiaodan Song, and Denny Zhou. 2022. Token dropping for efficient bert pretraining.arXiv preprint arXiv:2203.13240(2022)

    2022 arXiv

  12. [20]

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2022. Lora: Low-rank adaptation of large language models.ICLR1, 2 (2022), 3

    2022

  13. [21]

    Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al. 2024. Openai o1 system card.arXiv preprint arXiv:2412.16720(2024)

    2024 arXiv

  14. [22]

    Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019. What does BERT learn about the structure of language?. InACL 2019-57th Annual Meeting of the Association for Computational Linguistics

    2019

  15. [23]

    Youhe Jiang, Fangcheng Fu, Wanru Zhao, Stephan Rabanser, Nicholas D Lane, and Binhang Yuan. 2025. Cascadia: A Cascade Serving System for Large Language Models.arXiv preprint arXiv:2506.04203(2025)

    2025

  16. [24]

    Ryo Kamoi, Yusen Zhang, Nan Zhang, Jiawei Han, and Rui Zhang. 2024. When can llms actually correct their own mistakes? a critical survey of self-correction of llms.Transactions of the Association for Computational Linguistics12 (2024), 1417–1440

    2024

  17. [25]

    Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, et al. 2025. A survey of frontiers in llm reasoning: Inference scaling, learning to reason, and agentic systems.arXiv preprint arXiv:2504.09037(2025)

    2025

  18. [26]

    Minsu Kim, Jean-Pierre Falet, Oliver E Richardson, Xiaoyin Chen, Moksh Jain, Sungjin Ahn, Sungsoo Ahn, and Yoshua Bengio. 2025. Search-Based Correction of Reasoning Chains for Language Models.arXiv preprint arXiv:2505.11824(2025)

    2025

  19. [27]

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners.Advances in neural information processing systems35 (2022), 22199–22213

    2022

  20. [28]

    Abhinav Kumar, Jaechul Roh, Ali Naseh, Marzena Karpinska, Mohit Iyyer, Amir Houmansadr, and Eugene Bagdasarian. 2025. Overthink: Slowdown attacks on reasoning llms.arXiv preprint arXiv:2502.02542(2025)

    2025

  21. [29]

    Loka Li, Zhenhao Chen, Guangyi Chen, Yixuan Zhang, Yusheng Su, Eric Xing, and Kun Zhang. 2024. Confidence matters: Revisiting intrinsic self-correction capabilities of large language models.arXiv preprint arXiv:2402.12563(2024)

    2024 arXiv

  22. [30]

    Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023. Let’s verify step by step. InThe Twelfth International Conference on Learning Representations

    2023

  23. [31]

    Dancheng Liu, Amir Nassereldine, Ziming Yang, Chenhui Xu, Yuting Hu, Jiajie Li, Utkarsh Kumar, Changjae Lee, Ruiyang Qin, Yiyu Shi, et al. 2024. Large language models have intrinsic self-correction ability.arXiv preprint arXiv:2406.15673 (2024)

    2024 arXiv

  24. [32]

    Guangliang Liu, Haitao Mao, Jiliang Tang, and Kristen Marie Johnson. 2024. In- trinsic self-correction for enhanced morality: An analysis of internal mechanisms and the superficial hypothesis.arXiv preprint arXiv:2407.15286(2024)

    2024 arXiv

  25. [33]

    Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Cheng Jiayang, Yue Zhang, Xipeng Qiu, and Zheng Zhang. 2024. Can language models learn to skip steps?Advances in Neural Information Processing Systems37 (2024), 45359–45385

    2024

  26. [34]

    Yue Liu, Jiaying Wu, Yufei He, Hongcheng Gao, Hongyu Chen, Baolong Bi, Ruihan Gong, Jiaheng Zhang, Zhiqi Huang, and Bryan Hooi. 2025. Efficient inference for large reasoning models: A survey.arXiv preprint arXiv:2503.23077 (2025)

    2025 arXiv

  27. [35]

    Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101(2017)

    2017 arXiv

  28. [36]

    Xinyin Ma, Gongfan Fang, and Xinchao Wang. 2023. Llm-pruner: On the struc- tural pruning of large language models.Advances in neural information processing systems36 (2023), 21702–21720

    2023

  29. [37]

    Yiran Ma, Zui Chen, Tianqiao Liu, Mi Tian, Zhuo Liu, Zitao Liu, and Weiqi Luo

  30. [38]

    Emanuele Marconato, Stefano Teso, Antonio Vergari, and Andrea Passerini. 2023. Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts.Advances in Neural Information Processing Systems36 (2023), 72507–72539

    2023

  31. [39]

    Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, and William Yang Wang. 2023. Automatically correcting large language models: Surveying the landscape of diverse self-correction strategies.arXiv preprint arXiv:2308.03188(2023)

    2023 arXiv

  32. [40]

    Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, et al. 2024. Llmlingua-2: Data distillation for efficient and faithful task-agnostic prompt compression. arXiv preprint arXiv:2403.12968(2024)

    2024 arXiv

  33. [41]

    Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023. Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems36 (2023), 8634–8652

    2023

  34. [42]

    Zhendong Tan, Xingjun Zhang, Chaoyi Hu, Yancheng Pan, and Shaoxun Wang

  35. [43]

    Qwen Team. 2024. Qwen2 technical report.arXiv preprint arXiv:2407.10671 (2024)

    2024 arXiv

  36. [44]

    Gladys Tyen, Hassan Mansoor, Victor Cărbune, Peter Chen, and Tony Mak. 2023. LLMs cannot find reasoning errors, but can correct them given the error location. arXiv preprint arXiv:2311.08516(2023)

    2023 arXiv

  37. [45]

    Adaptive rectification sampling for test-time compute scaling.arXiv preprint arXiv:2504.01317(2025)

    2025

  38. [46]

    Peifeng Wang, Zhengyang Wang, Zheng Li, Yifan Gao, Bing Yin, and Xiang Ren. 2023. Scott: Self-consistent chain-of-thought distillation.arXiv preprint arXiv:2305.01879(2023)

    2023 arXiv

  39. [47]

    Xin Wang, Yu Zheng, Zhongwei Wan, and Mi Zhang. 2024. Svd-llm: Truncation- aware singular value decomposition for large language model compression.arXiv preprint arXiv:2403.07378(2024)

    2024 arXiv

  40. [48]

    Somin Wadhwa, Silvio Amir, and Byron C Wallace. 2024. Investigating mysteries of cot-augmented distillation.arXiv preprint arXiv:2406.14511(2024)

    2024 arXiv

  41. [49]

    Heming Xia, Chak Tou Leong, Wenjie Wang, Yongqi Li, and Wenjie Li. 2025. Tokenskip: Controllable chain-of-thought compression in llms.arXiv preprint arXiv:2502.12067(2025)

    2025

  42. [50]

    Silei Xu, Wenhao Xie, Lingxiao Zhao, and Pengcheng He. 2025. Chain of draft: Thinking faster by writing less.arXiv preprint arXiv:2502.18600(2025)

    2025 arXiv

  43. [51]

    David Wingate, Mohammad Shoeybi, and Taylor Sorensen. 2022. Prompt com- pression and contrastive conditioning for controllability and toxicity reduction in language models.arXiv preprint arXiv:2210.03162(2022)

    2022 arXiv

  44. [52]

    Zhaohui Yang, Chenghua He, Xiaowen Shi, Linjing Li, Qiyue Yin, Shihong Deng, and Daxin Jiang. 2025. Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning.arXiv preprint arXiv:2505.14391(2025)

    2025 arXiv

  45. [53]

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems36 (2023), 11809–11822

    2023

  46. [54]

    Ning Xuefei, Lin Zinan, Zhou Zixuan, et al. 2023. Skeleton-ofthought: Prompting LLMs for Efficient Parallel Generation

    2023

  47. [55]

    Zhenyu Zhang, Ying Sheng, Tianyi Zhou, Tianlong Chen, Lianmin Zheng, Ruisi Cai, Zhao Song, Yuandong Tian, Christopher Ré, Clark Barrett, et al. 2023. H2o: Heavy-hitter oracle for efficient generative inference of large language models. Advances in Neural Information Processing...

    2023

  48. [56]

    $\\boxed{72}$

    Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Zhangchi Feng, and Yongqiang Ma. 2024. Llamafactory: Unified efficient fine-tuning of 100+ language models.arXiv preprint arXiv:2403.13372(2024). A APPENDIX This document provides supplementary material, includi...

    2024 arXiv

  49. [57]

    Yadong Zhang, Shaoguang Mao, Tao Ge, Xun Wang, Adrian de Wynter, Yan Xia, Wenshan Wu, Ting Song, Man Lan, and Furu Wei. 2024. Llm as a mastermind: A survey of strategic reasoning with large language models.arXiv preprint arXiv:2404.01230(2024)

    2024 arXiv

  50. [2024]

    Break the chain: Large language models can be shortcut reasoners.arXiv preprint arXiv:2406.06580(2024)

    2024 arXiv

  51. [2025]

    InProceedings of the AAAI Conference on Artificial Intelligence, Vol

    What are step-level reward models rewarding? counterintuitive findings from mcts-boosted mathematical reasoning. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. 24812–24820

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.