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REVIEW 5 major objections 6 minor 29 references

Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models

T0 review · 5 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Residual-demand tracking, not just more reasoning, drives LLM gains.

desk verdict Novel but overclaimed: the residual-demand mechanism is the paper's main selling point, yet its own ablation shows it adds little over a fixed-effort iterative loop; worth reviewing, not worth citing as proven. read the letter →

arxiv 2608.01319 v1 pith:3T7O5XED submitted 2026-08-02 cs.AI

John Scoville , Shengzhuang Chen , Yejin Bang , Stefan Winzeck , Jonathan Richard Schwarz This is my paper
classification cs.AI
keywords meta-reasoningcognitivedemandtest-timescalingchain-of-thoughtresidualLLMreasoningadaptiveinferenceeffortallocation
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

This paper claims that large language models improve at reasoning when the control loop tracks how much cognitive demand remains on the task, rather than simply reviewing the last step's mistakes. It introduces Cognitive Demand Steering (CDS), a training-free framework that profiles each problem across 16 cognitively grounded dimensions, then repeatedly estimates residual demand on the active dimensions and uses that forward-looking signal to pick the next action and effort level. Across three frontier models and six reasoning benchmarks, CDS reports average accuracy gains of 21.9% over direct calls and 9% over standard chain-of-thought, concentrated on hard mathematics and coding tasks. The paper also shows that the iterative loop itself carries much of the benefit: removing the demand signal and fixing maximum effort leaves average accuracy nearly unchanged, which the authors interpret as evidence that demand tracking is a useful but not yet uniformly decisive mechanism.

What carries the argument

The residual-demand signal: at each round an LLM re-scores the active cognitive dimensions (from the 16-dimension General Scales taxonomy) on a 0–5 scale to forecast how much demand remains, rather than judging the previous step. This forward-looking state drives the effort scheduler (Eq. 2: weighted max and mean residual demand, uncertainty, contradiction/loop risk mapped to a 1–5 effort tier), the free-form controller actions, dimension-specific exemplar retrieval, and adaptive early stopping.

What would settle it

Run the demand-ablated variant (no profiling, no residual-demand assessment, no exemplars, effort fixed at level 5) against full CDS over all six benchmarks and three models; if the ablated variant matches or beats CDS on most benchmarks—the paper's own Table 3 already shows average gaps near 0.1–1.1 points—the central claim that the residual-demand signal is a useful control collapses.

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Extended reading notes

Core claim

CDS replaces discrete action sets with an explicit multi-dimensional residual-demand state. A profiler scores the task on 16 cognitive dimensions and keeps the top four active. Each round, a progress evaluator flags uncertainty, contradiction, loop risk, missing information, and verification targets; a residual-demand assessor re-scores active dimensions to forecast what remains; an effort scheduler maps max and mean demand plus risk to an effort tier; a controller issues free-form next-step instructions targeting the highest residual dimensions. The loop stops when the evaluator says solved or the round cap is hit. The central claim: this forward-looking demand-gap signal, not mere iteratio

Load-bearing premise

The LLM's own residual-demand, uncertainty, and risk scores are accurate enough to steer reasoning better than a fixed high-effort loop.

Editorial extensions

If this is right

  • If CDS holds, test-time compute can be allocated in proportion to the hardest unresolved dimension, so easy tasks stop early and hard tasks deepen automatically.
  • The same residual-demand representation can generate training traces for supervised or reinforcement learning without a trained controller, which the paper notes as a direct use.
  • Because CDS is training-free and zero-shot, it should transfer across new models and task distributions without per-model adaptation, only the exemplar dictionary.
  • On easy coding tasks CDS can fall below direct calls, so the framework implies controllers should also learn when not to loop.
  • The demand-gap trajectory itself is an interpretable audit log: a round-by-round story of what reasoning was missing and when confidence collapsed.

Reading between the lines

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

  • We infer that the largest untested upside is whether residual-demand scores can serve as training labels: fine-tuning a model to predict its own demand gaps could make the loop cheaper and stronger than hand-authored exemplars.
  • The paper's own ablation suggests the demand signal adds only a small average increment (roughly 0.1–1.1 points per model) over a fixed maximum-effort loop; an editorial reading is that the current heuristic form of demand tracking is not yet close to its ceiling.
  • A testable extension would replace the fixed 16-dimension taxonomy with a learned or adaptive demand space for multimodal and tool-use tasks, where the paper's stated limitations concede the hand-authored exemplars may not generalize.
  • Because gains concentrate on hard tasks, one further step would be to feed residual demand into a learned early-stopping policy so the loop's iteration count is itself optimized per instance.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 6 minor

Summary. The paper introduces Cognitive Demand Steering (CDS), a training-free meta-reasoning framework that wraps LLM generation in an iterative controller–worker loop. At each round, a progress evaluator summarizes the trace, a residual-demand assessor estimates remaining demand along 16 cognitive dimensions, an effort scheduler converts this demand into an effort level, and a controller issues a free-form next action for the worker. The method is evaluated on six benchmarks (AIME, MATH500, GPQA, LiveCodeBench Easy/Medium/Hard) with Gemini 3.1 Pro, Claude Sonnet 4.6, and GPT-5.4. The reported headline results are a 21.9% average accuracy improvement over direct calls and 9% over chain-of-thought, with the largest gains on AIME and LCB-Hard. The paper also reports iteration-scaling curves and a demand-ablation study, and it claims that residual-demand tracking, rather than the iterative loop or fixed maximum effort, is the key mechanism.

Significance. If the empirical claims held, CDS would be a useful contribution: it is training-free, transfers zero-shot across models and tasks, uses an interpretable cognitive-demand state, and is evaluated on several frontier backbones against strong baselines. The appendices include full prompts and hand-authored exemplars, which is a real reproducibility strength. The central scientific claim—that a forward-looking residual-demand signal, not merely iterative reasoning or fixed high effort, drives the gains—is important because it would distinguish CDS from generic self-refinement loops. However, the current evidence does not establish that claim: the demand ablation shows only small, unreplicated differences, the comparisons lack error bars and significance tests, and baseline implementations are underspecified. The idea is promising and the paper is candid about limitations, but the empirical support needs substantial strengthening before the central mechanism claim can be accepted.

major comments (5)
  1. [Table 3; §5.1] The paper's central mechanism claim—that residual-demand tracking, not iterative reasoning or fixed max effort, drives the gains (Abstract; contribution 3)—is not supported by Table 3. Averaged over six benchmarks, full CDS beats the demand-ablated variant by only 0.26 pts (Gemini), 0.52 (Claude), and 0.90 (GPT-5.4); the ablated variant wins several rows (AIME 100 vs 98.33 on Gemini; LCB-Easy 82.92 vs 82.30; LCB-Med 65.27 vs 65.01). Because no error bars, seeds, or paired tests are reported, these deltas are within noise on a 60-item AIME split. This directly weakens the 'Evidence that residual demand is a useful control signal' contribution and is consistent with the Limitations section's admission that demand exposure 'may expand the context window without meaningfully improving performance.'
  2. [§5.1 and Eq. (2)] The ablation is confounded. Removing demand profiling also removes (a) adaptive effort allocation via Eq. (2), since effort is fixed at level 5, and (b) demand-conditioned exemplar retrieval (§3.5). The ablated variant retains iterative progress evaluation and controller–worker interaction. Thus any observable difference could be due to effort scheduling or exemplar retrieval rather than to the residual-demand signal itself. A clean ablation should vary the demand-signal injection while holding effort and exemplars constant (or vice versa). Without such a design, the marginal value of the forward-looking residual signal is unidentified.
  3. [§4.1, Table 2] All accuracy numbers are point estimates. There is no mention of repeated runs, random seeds, temperature, confidence intervals, or significance tests. The headline comparisons (21.9% over Direct, 9% over CoT) are averages over six benchmarks and three models whose per-cell differences are often only a few points; on AIME (60 items) a 1.67-point difference is one item. I recommend paired per-instance tests (e.g., McNemar or bootstrap) and reporting of variance across runs before the empirical claims can be evaluated.
  4. [§4.1 Baselines] The implementations of ToT and Meta-Reasoner are underspecified: no tree width/depth, number of samples, token budgets, rollouts, prompt templates, or exact strategy sets are given. Since CDS runs a 12-iteration loop with multiple LLM calls per round, a fair comparison requires matching compute or at least reporting token/latency budgets. Without these details, the comparisons in Table 2 are not reproducible and the 9% over CoT claim cannot be independently verified.
  5. [§3.5; §A.1] The residual-demand scores are self-reports by the same backbone LLM whose reasoning is being steered, and the paper provides no evidence that these scores track true remaining difficulty. There is no calibration, no agreement with human labels, no stability analysis across prompt variants, and no demonstration that top-k selection is reliable. This is load-bearing because controller actions and effort are conditioned on this signal. A concrete test would compare demand ratings against an independent difficulty measure or show that demand-conditioned control outperforms a generic 'keep reasoning' control at matched compute.
minor comments (6)
  1. [§5.1] The sentence 'On GPT-5.4, full CDS averaged over the six datasets outperforms the ablated variant on each of the three backbone models' is garbled; it should say 'Across all three backbone models, full CDS outperforms the ablated variant on average over the six datasets, though the per-benchmark differences are mixed.'
  2. [Figure 2 caption] The caption refers to 'GPT-5.5' while the rest of the paper and Table 2 use 'GPT-5.4'. Please make the model labels consistent.
  3. [Eq. (2)] The symbol D_t is used both for the residual-demand vector and for the mean residual demand. Define the mean explicitly (e.g., \bar D_t) to avoid ambiguity.
  4. [§3.3; Algorithm 1] The statement that 'the active-set dimension with the lowest demand at each step is omitted from the working set' appears to conflict with the fixed top-k=4 active set and with Algorithm 1, where A is defined after line 2 but used on that line. Please clarify the dynamic active-set rule and reorder the pseudocode.
  5. [§4.1] Please report the number of items in each benchmark, especially AIME and GPQA, so that per-point differences can be contextualized as fractions of the test set.
  6. [§3.6] The effort score is rounded to an integer tier, but the paper does not specify the tie-breaking rule or the exact behavior of 'max(1,min(5, floor(...)))' for half-integer values. A one-line clarification would remove ambiguity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CDS's reported gains are measured against fixed external benchmarks, and no central prediction is forced by construction or by self-citation.

full rationale

The paper's derivation chain is empirical, not analytical. The headline claim (CDS improves average accuracy by 21.9% over direct calls and 9% over CoT) is backed by Table 2, which reports measured accuracies of three frontier models on six external benchmarks (AIME, MATH500, GPQA, LiveCodeBench Easy/Med/Hard). No equation in the paper computes these numbers from the framework's definitions, and no parameter is fitted to the benchmark labels and then reported as a prediction. The only self-referential component is the cognitive-scale taxonomy taken from Scales++ [1], on which two of the present authors are co-authors; but the 16 dimensions are used as a prompt vocabulary, and the paper's own demand-ablation (Table 3) shows that removing them costs only 0.26-0.90 points on average, so the central accuracy result does not stand or fall on the taxonomy's validity. The ablation is a genuine empirical comparison, not a construction: 'CDS (ablated)' removes demand profiling and residual demand assessment and fixes effort at 5, yet full CDS still wins on average; the small deltas are a weakness in the mechanism claim, but they are exactly the kind of falsifiable result that circularity analysis looks for. Hand-tuned constants (k=4, alpha=(0.45,0.25,0.15,0.15)) and the self-assessed LLM demand scores are methodological risks, not circular reductions. The Limitations section explicitly concedes demand exposure 'may expand the context window without meaningfully improving performance,' which is a substantive limitation, not an artifact. No uniqueness theorem is imported, no known result is renamed, and no equation reduces to itself. Therefore no significant circularity.

Assumptions & free parameters 6 free parameters · 4 assumptions · 1 invented entities

The framework's claimed contribution is the residual-demand signal; its values are inferred from the same model it steers and are not externally validated. The only empirical support is a single ablation with mixed per-benchmark results. Several control constants (k, alpha, R, theta, effort budgets) are set by the authors, and theta and the effort budgets are not even specified.

free parameters (6)
  • k (active top-k dimensions) = 4
    Number of retained cognitive dimensions in the active working set; selected by hyperparameter search (Section 3.3) without a disclosed validation protocol.
  • alpha weights in effort scheduler = (0.45, 0.25, 0.15, 0.15)
    Weights in Eq. (2) chosen by hand to balance max residual demand, mean residual demand, uncertainty, and risk (Section 3.6).
  • Maximum iterations R = 12
    Loop cap for all evaluations (Section 4.1); no sensitivity analysis reported.
  • Exemplar retrieval threshold theta
    Algorithm 1 line 12 retrieves guidance for dims with r_d > theta, but theta is never defined anywhere in the paper.
  • Top-k residual demand dims for steering (A*) = 3
    Algorithm 1 line 11 uses TOPK(r,3) to select the highest residual-demand dimensions for exemplar retrieval and controller context; fixed without sensitivity analysis.
  • Worker effort-budget settings
    max_new_steps, branch_budget, and verification_mode are referenced as placeholders in the worker prompts (Appendix A.1) with no values reported; these directly affect chunk length and verification behavior.
assumptions (4)
  • domain assumption The 16-dimensional cognitive scales taxonomy provides a valid and approximately complete characterization of the cognitive demands of a task.
    Invoked in Sections 3.3 and A.2, grounded in General Scales [27] and Scales++ [1] rather than established for meta-reasoning control.
  • domain assumption The same LLM under test can accurately self-assess residual demand, uncertainty, contradiction risk, and loop risk via prompted evaluations.
    Sections 3.4 and 3.5 rely on M's self-reports to drive control; no external calibration is provided, and the ablation shows these signals add little.
  • ad hoc to paper The hand-authored exemplars in Appendix A.3 are effective and transfer across models and tasks.
    These exemplars are static text written by the authors; no evidence is given that they improve performance beyond generic guidance.
  • domain assumption The evaluation protocol (12 rounds, no tools, 3-second judge for LiveCodeBench) is a fair and representative setting for the claims.
    Section 4.1 sets these choices; no sensitivity analysis or comparison with other judge configurations is provided.
invented entities (1)
  • Residual demand profile D_t (16-dimensional vector of remaining cognitive demand)
    purpose: Serves as the control state for the meta-controller, replacing discrete actions; motivates effort scheduling, exemplar retrieval, and early stopping.
    Its values are produced by prompted self-reports of the same model being controlled; there is no external or objective measurement of the quantities. The ablation (Table 3) shows its incremental contribution is small and inconsistent, so it is not an independently verified state.

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Pith. "Pith review of Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models." pith.science (2026). https://pith.science/paper/3T7O5XED

@misc{pith2026260801319,
  author       = {Pith},
  title        = {Pith review of: Cognitive Demand Steering for Adaptive Meta-Reasoning in Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3T7O5XED}},
  note         = {Machine review of arXiv:2608.01319}
}
abstract

Recent meta-reasoning frameworks improve LLM reasoning by wrapping chain-of-thought generation in an iterative control loop, allowing more effective backtracking, termination of reasoning loops, and injection of promising reasoning patterns, among other strategy adjustments. Despite promising results, methods often rely on backward-looking reward functions, utilize coarse search actions, or require additional reasoning controller training requiring many-shot supervision. We introduce Cognitive Demand Steering (CDS), a training-free meta-reasoning framework equipped with residual demand assessment: at each step, an LLM-based progress evaluator characterizes the residual reasoning required to arrive at a solution rather than merely evaluating the previous step. This allows a meta-controller to select reasoning interventions comprising both general-purpose exemplars and actions (e.g., general guidance for quantitative reasoning) that directly tackle this forward-looking demand signal. This shift eliminates the need for any trained component while enabling zero-shot transfer across models and tasks with no adaptation. Rather than relying on coarse characterizations, we employ cognitive scales to both design interventions as well as profile initial problem complexity and residual demand signal over 16 dimensions motivated by cognitive science (e.g., attention and scan, learning and abstraction, spatio-physical reasoning), giving the controller a fine-grained vocabulary for diagnosing. Averaged across three frontier LLMs and six reasoning benchmarks, CDS improves accuracy by $21.9\%$ over direct calls and $9\%$ over standard CoT reasoning, with the largest gains on difficult mathematics and coding tasks.

Figures

Figures reproduced from arXiv: 2608.01319 by the authors.

Figure 1
Figure 1. The CDS pipeline. A task is initially profiled by the demand profile to produce an initial [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. (a) Accuracy vs. iteration count across three backbone models, measured by direct prompting with the context accumulated at each reasoning round. Accuracy rises with additional iterations before plateauing; GPT-5.5 benefits most, consistent with its heavier use of multiple rounds (panel b). (b) Complementary CDF of loop counts. Gemini Pro 3.1 resolves the vast majority of example (≈96%) in a single round, while GPT-… view at source ↗
Figure 3
Figure 3. Demand-risk dynamics for arc191-d. The model initially attempts to solve the problem [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

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Reference graph

Works this paper leans on

29 extracted references · 5 canonical work pages

  1. [1]

    Bean, Nabeel Seedat, Shengzhuang Chen, and Jonathan Richard Schwarz

    Andrew M. Bean, Nabeel Seedat, Shengzhuang Chen, and Jonathan Richard Schwarz. Scales++: Compute efficient evaluation subset selection with cognitive scales embeddings.arXiv preprint arXiv:2510.26384, 2025

    arXiv 2025

  2. [2]

    Do not think that much for 2+ 3=? on the overthinking of o1-like llms.arXiv preprint arXiv:2412.21187, 2024

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

    arXiv 2024

  3. [3]

    Large reasoning mod- els are not thinking straight: on the unreliability of thinking trajectories.arXiv preprint arXiv:2507.00711, 2025

    Jhouben Cuesta-Ramirez, Samuel Beaussant, and Mehdi Mounsif. Large reasoning mod- els are not thinking straight: on the unreliability of thinking trajectories.arXiv preprint arXiv:2507.00711, 2025

    arXiv 2025

  4. [4]

    Deepseek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning.Nature, 645:633–638, 2025

    DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning.Nature, 645:633–638, 2025

    work page 2025

  5. [5]

    Meta reasoning for large language models.arXiv preprint arXiv:2406.11698, 2024

    Peizhong Gao, Ao Xie, Shaoguang Mao, Wenshan Wu, Yan Xia, Haipeng Mi, and Furu Wei. Meta reasoning for large language models.arXiv preprint arXiv:2406.11698, 2024

    arXiv 2024

  6. [6]

    Livecodebench: Holistic and contamination free evaluation of large language models for code.arXiv preprint arXiv:2403.07974, 2024

    Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Ar- mando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code.arXiv preprint arXiv:2403.07974, 2024. 19

    arXiv 2024

  7. [7]

    Chain of mindset: Reasoning with adaptive cognitive modes.arXiv preprint arXiv:2602.10063, 2026

    Tianyi Jiang, Arctanx An, Hengyi Feng, Naixin Zhai, Haodong Li, Xiaomin Yu, Jiahui Liu, Hanwen Du, Shuo Zhang, Zhi Yang, Jie Huang, Youhua Li, Yongxin Ni, Huacan Wang, and Ronghao Chen. Chain of mindset: Reasoning with adaptive cognitive modes.arXiv preprint arXiv:2602.10063, 2026

    arXiv 2026

  8. [8]

    Cognitive foundations for reasoning and their manifestation in llms.arXiv preprint arXiv:2511.16660, 2025

    Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang, Jinu Lee, Shan Chen, Orevaoghene Ahia, Dean Light, Thomas L Griffiths, Max Kleiman-Weiner, Jiawei Han, et al. Cognitive foundations for reasoning and their manifestation in llms.arXiv preprint arXiv:2511.16660, 2025

    arXiv 2025

Show all 29 references
  1. [9]

    Let’s verify step by step

    Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison 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, volume 2024, pages 39578–39601, 2024

    2024

  2. [10]

    Metascale: Test-time scaling with evolving meta-thoughts.arXiv preprint arXiv:2503.13447, 2025

    Qin Liu, Wenxuan Zhou, Nan Xu, James Y Huang, Fei Wang, Sheng Zhang, Hoifung Poon, and Muhao Chen. Metascale: Test-time scaling with evolving meta-thoughts.arXiv preprint arXiv:2503.13447, 2025

    2025 arXiv

  3. [11]

    Self-refine: Iterative refinement with self-feedback.arXiv preprint arXiv:2303.17651, 2023

    Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Self-refine: Iterativ...

    2023 arXiv

  4. [12]

    Learning to reason with LLMs

    OpenAI. Learning to reason with LLMs. https://openai.com/index/ learning-to-reason-with-llms/, 2024. Accessed: 2026-07-29

    2024

  5. [13]

    Gpqa: A graduate-level google-proof q&a benchmark.arXiv preprint arXiv:2311.12022, 2023

    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.arXiv preprint arXiv:2311.12022, 2023

    2023 arXiv

  6. [14]

    Reflexion: Language agents with verbal reinforcement learning.arXiv preprint arXiv:2303.11366, 2023

    Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language agents with verbal reinforcement learning.arXiv preprint arXiv:2303.11366, 2023

    2023 arXiv

  7. [15]

    Scaling llm test-time compute optimally can be more effective than scaling parameters for reasoning

    Charlie Victor Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling llm test-time compute optimally can be more effective than scaling parameters for reasoning. InThe Thirteenth International Conference on Learning Representations, 2025

    2025

  8. [16]

    Large language model reasoning failures

    Peiyang Song, Pengrui Han, and Noah Goodman. Large language model reasoning failures. arXiv preprint arXiv:2602.06176, 2026

    2026

  9. [17]

    Between underthinking and overthinking: An empirical study of reasoning length and correctness in llms.arXiv preprint arXiv:2505.00127, 2025

    Jinyan Su, Jennifer Healey, Preslav Nakov, and Claire Cardie. Between underthinking and overthinking: An empirical study of reasoning length and correctness in llms.arXiv preprint arXiv:2505.00127, 2025

    2025 arXiv

  10. [18]

    Meta-reasoner: Dynamic guidance for optimized inference-time reasoning in large language models.arXiv preprint arXiv:2502.19918, 2025

    Yuan Sui, Yufei He, Tri Cao, Simeng Han, Yulin Chen, and Bryan Hooi. Meta-reasoner: Dynamic guidance for optimized inference-time reasoning in large language models.arXiv preprint arXiv:2502.19918, 2025

    2025 arXiv

  11. [19]

    Self-consistency improves chain of thought reasoning in language models.arXiv preprint arXiv:2203.11171, 2022

    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.arXiv preprint arXiv:2203.11171, 2022

    2022 arXiv

  12. [20]

    Chain-of-thought prompting elicits reasoning in large language models.arXiv preprint arXiv:2201.11903, 2022

    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.arXiv preprint arXiv:2201.11903, 2022

    2022 arXiv

  13. [21]

    From efficiency to adaptivity: A deeper look at adaptive reasoning in large language models.arXiv preprint arXiv:2511.10788, 2025

    Chao Wu, Baoheng Li, Mingchen Gao, Yu Tian, and Zhenyi Wang. From efficiency to adaptivity: A deeper look at adaptive reasoning in large language models.arXiv preprint arXiv:2511.10788, 2025. 20

    2025

  14. [22]

    Position: Llms need a bayesian meta-reasoning framework for more robust and generalizable reasoning

    Hanqi Yan, Linhai Zhang, Jiazheng Li, Zhenyi Shen, and Yulan He. Position: Llms need a bayesian meta-reasoning framework for more robust and generalizable reasoning. InForty- second International Conference on Machine Learning Position Paper Track, 2025

    2025

  15. [23]

    Buffer of thoughts: thought-augmented reasoning with large language models (2024).URL https://arxiv

    Ling Yang, Zhaochen Yu, Tianjun Zhang, Shiyi Cao, Minkai Xu, Wentao Zhang, Joseph E Gonzalez, and Bin Cui. Buffer of thoughts: thought-augmented reasoning with large language models (2024).URL https://arxiv. org/abs/2406.04271, 2024

    2024 arXiv

  16. [24]

    Griffiths, Yuan Cao, and Karthik Narasimhan

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models.arXiv preprint arXiv:2305.10601, 2023

    2023 arXiv

  17. [25]

    A survey on test-time scaling in large language models: What, how, where, and how well?arXiv preprint arXiv:2503.24235, 2025

    Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Wenyue Hua, Haolun Wu, Zhihan Guo, Yufei Wang, Niklas Muennighoff, et al. A survey on test-time scaling in large language models: What, how, where, and how well?arXiv preprint arXiv:2503.24235, 2025

    2025 arXiv

  18. [26]

    Least-to-most prompting enables complex reasoning in large language models.arXiv preprint arXiv:2205.10625, 2022

    Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, and Ed Chi. Least-to-most prompting enables complex reasoning in large language models.arXiv preprint arXiv:2205.10625, 2022

    2022 arXiv

  19. [27]

    Collins, Yael Moros-Daval, Seraphina Zhang, Qinlin Zhao, Yitian Huang, Luning Sun, Jonathan E

    Lexin Zhou, Lorenzo Pacchiardi, Fernando Martínez-Plumed, Katherine M. Collins, Yael Moros-Daval, Seraphina Zhang, Qinlin Zhao, Yitian Huang, Luning Sun, Jonathan E. Prunty, Zongqian Li, Pablo Sánchez-García, Kexin Jiang Chen, Pablo A. M. Casares, Jiyun Zu, John Burden, Behzad...

    2025 arXiv

  20. [28]

    Scaling test-time compute for llm agents.arXiv preprint arXiv:2506.12928, 2025

    King Zhu, Hanhao Li, Siwei Wu, Tianshun Xing, Dehua Ma, Xiangru Tang, Minghao Liu, Jian Yang, Jiaheng Liu, Yuchen Eleanor Jiang, et al. Scaling test-time compute for llm agents.arXiv preprint arXiv:2506.12928, 2025

    2025 arXiv

  21. [29]

    Beyond meta-reasoning: Metacognitive consolidation for self-improving llm reasoning.arXiv preprint arXiv:2604.17399, 2026

    Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou, and Yulan He. Beyond meta-reasoning: Metacognitive consolidation for self-improving llm reasoning.arXiv preprint arXiv:2604.17399, 2026. 21

    2026 arXiv

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