Pith. sign in

REVIEW 26 references

CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection

T0 review · reviewed 2026-08-04 · deepseek-v4-flash

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

Pith's one-line read Clustering LLM-generated chain-of-thought trajectories, rather than raw question text, selects coresets that estimate full-benchmark scores with lower error than text-only selection on some benchmarks.

desk verdict A plausible, well-scoped coreset-selection idea whose 'drastic cost reduction' claim is undercut by missing end-to-end cost accounting; the method itself is honest about where it helps. read the letter →

arxiv 2608.00014 v1 pith:QUIUDW2B submitted 2026-06-24 cs.AI

Qihua Pan , Zhenheng Tang , Peijie Dong , Xiang Liu , Huacan Wang , Bo Li , Xiaowen Chu This is my paper · ORCID
classification cs.AI
keywords cot-coreevaluationselectioncoresetllmsquestionsreasoningunderlying
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

Evaluating a large language model usually means running it on every question in a benchmark, which costs a lot of compute. CoT-Core tries to pick a small set of questions that still gives an accurate estimate of the model's full score.

The idea: instead of grouping questions by their wording, ask another LLM to 'think step by step' for each question, producing a chain of thought. Then embed those reasoning traces into a vector space and cluster them. Questions that are worded differently but need the same logic should end up in the same cluster, because their reasoning traces look similar. From each cluster, CoT-Core keeps the question closest to the cluster center.

The paper tests this on GSM8K, MMLU, MMLU-Pro, and GPQA, comparing against random sampling, text-embedding k-means, k-center, and IRT-based methods. The results are mixed: on harder benchmarks like GPQA, CoT-Core often has lower error than text-based selection, but on easier math (GSM8K) it sometimes does worse than a simple text-based baseline. The authors also find that using a smaller LLM to generate the reasoning traces works about as well as a huge one, and that cleaning away conversational filler helps. However, the method requires generating reasoning traces for the whole benchmark once, which is itself a significant compute cost, and the paper's main tables do not show uncertainty bars or provide code.

Extended reading notes

Core claim

CoT-Core, by clustering CoT trajectories rather than raw text, yields coresets that provide high-fidelity full-dataset score estimation under a strictly training-free pipeline. From the abstract: 'Projecting these paths into a latent space effectively clusters questions by intrinsic logical equivalence rather than superficial text similarity. Extensive experiments on GSM8K, MMLU, MMLU-Pro, and GPQA demonstrate that CoT-Core drastically reduces evaluation costs while maintaining high-fidelity score estimation.'

Load-bearing premise

The method assumes that CoT trajectories generated by a separate generator LLM (Phi-4) expose an 'underlying reasoning manifold' that is stable across arbitrary target models, and that embedding these trajectories with BGE-M3 yields a metric space where k-means clusters correspond to score-prediction equivalence. If the generator's reasoning style is not aligned with the target models' actual reasoning (or if the encoder fails to capture structural equivalence), the selected coreset may not be representative. This enters in Section 3.3 (Stage 1) and Section 3.4 (Stage 2).

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.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method rests on the assumption that LLM-generated reasoning trajectories define a task-relevant similarity space for question selection. This is plausible but not directly validated; the paper provides anecdotal t-SNE and case studies rather than a clustering-quality metric.

free parameters (3)
  • Generator LLM Mgen (Phi-4)
    Trajectories depend on the choice of generator model; ablation in §4.4 shows 1B to 72B models give similar results, but the main results use Phi-4.
  • CoT trigger prompt pcot = "Let's think step by step"
    Used for all trajectory generation (§3.3); no sensitivity analysis is provided for this prompt choice.
  • Generation hyperparameters (temperature T=0.7, max_tokens=1024)
    Set in Appendix A.1; effects of temperature or token limit on clustering quality are not analyzed.
assumptions (4)
  • domain assumption CoT trajectories expose underlying logical structure better than raw text.
    Central premise of §3.3; supported only by qualitative case studies (Appendix B) and t-SNE, not by a quantitative comparison of clustering validity.
  • domain assumption Embedding concatenated question+CoT with BGE-M3 yields a metric space where k-means clusters correspond to reasoning equivalence classes.
    Required for §3.4-3.5; no validation that the embedding metric aligns with reasoning similarity.
  • domain assumption An unweighted mean of scores on centroid-closest instances is a valid proxy for the full-benchmark score.
    Used by the SA estimator in Tables 1,2; the coreset is assumed representative by construction rather than by proof.
  • domain assumption The generator LLM's trajectories need not be correct for the method to work.
    Claimed in §4.4 ('trajectory correctness is secondary') based on generator-scale ablation; not proven generally.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection." pith.science (2026). https://pith.science/paper/QUIUDW2B

@misc{pith2026260800014,
  author       = {Pith},
  title        = {Pith review of: CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QUIUDW2B}},
  note         = {Machine review of arXiv:2608.00014}
}
read the original abstract

Evaluating Large Language Models (LLMs) incurs prohibitive computational overhead during continuous development processes. While coreset selection accelerates evaluation, existing methods either suffer from a severe ``cold start'' bottleneck requiring massive historical logs (e.g., Item Response Theory) or exhibit a surface lexical bias that misses the underlying reasoning manifold of tasks. We propose CoT-Core, a novel training-free core question selection framework. Recognizing that lexically disparate questions can share equivalent underlying logic, CoT-Core prompts LLMs to unroll zero-shot Chain-of-Thought (CoT) reasoning trajectories. Projecting these paths into a latent space effectively clusters questions by intrinsic logical equivalence rather than superficial text similarity. Extensive experiments on GSM8K, MMLU, MMLU-Pro, and GPQA demonstrate that CoT-Core drastically reduces evaluation costs while maintaining high-fidelity score estimation, and delineate the boundary conditions of reasoning-aware pruning, revealing that its efficacy is intrinsically gated by task complexity.

Figures

Figures reproduced from arXiv: 2608.00014 by the authors.

Figure 1
Figure 1. The motivation and overview of CoT-Core. To overcome the limitations of history￾dependent metrics and shallow lexical fea￾tures, we propose CoT-Core, a novel training￾free core question selection framework. Our approach is motivated by a critical observa￾tion: lexically disparate questions—which ap￾pear entirely unrelated in standard text em￾bedding spaces—can share equivalent under￾lying logic or reasoning structur… view at source ↗
Figure 2
Figure 2. Overview of CoT-Core. Our framework unrolls LLM reasoning trajectories, embeds them, and applies isomorphic clustering to select coresets. In contrast to raw text embeddings which fall into lexical traps (falsely grouping Q1 & Q2), CoT-Core effectively aligns lexically disparate problems that share an identical latent logical structure (Q1 & Q3). training-free setting. The resulting coreset is inherently flexible: i… view at source ↗
Figure 3
Figure 3. t-SNE of GPQA embeddings. (Left) Question-only embeddings are hindered by surface lexical traps and domain-specific vocabulary. (Right) CoT-Core reorganizes the latent space via logical isomorphisms, capturing underlying reasoning structures that transcend superficial text similarities. Exposing Cognitive Isomorphism via Logical Trajectories To understand how CoT-Core externalizes implicit reasoning paths into expli… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 12 linked inside Pith

  1. [1]

    Phi-4 technical report

    Marah Abdin, Jyoti Aneja, Harkirat Behl, S \'e bastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, Russell J Hewett, Mojan Javaheripi, Piero Kauffmann, et al. Phi-4 technical report. arXiv preprint arXiv:2412.08905, 2024

    arXiv 2024

  2. [2]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023

    arXiv 2023

  3. [3]

    Open llm leaderboard

    Edward Beeching, Cl \'e mentine Fourrier, Nathan Habib, Sheon Han, Nathan Lambert, Nazneen Rajani, Omar Sanseviero, Lewis Tunstall, and Thomas Wolf. Open llm leaderboard. https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard, 2023

    2023

  4. [4]

    Pythia: A suite for analyzing large language models across training and scaling

    Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al. Pythia: A suite for analyzing large language models across training and scaling. In International conference on machine learning, pp.\ 2397--2430. PMLR, 2023

    2023

  5. [5]

    Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation

    Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216, 4 0 (5), 2024

    arXiv 2024

  6. [6]

    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

    arXiv 2021

  7. [7]

    Olmo: Accelerating the science of language models

    Dirk Groeneveld, Iz Beltagy, Evan Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, et al. Olmo: Accelerating the science of language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.\ 15789--15809, 2024

    2024

  8. [8]

    Measuring massive multitask language understanding

    Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020

    arXiv 2009

Show all 26 references
  1. [9]

    Adaptive testing for llm evaluation: A psychometric alternative to static benchmarks

    Peiyu Li, Xiuxiu Tang, Si Chen, Ying Cheng, Ronald Metoyer, Ting Hua, and Nitesh V Chawla. Adaptive testing for llm evaluation: A psychometric alternative to static benchmarks. arXiv preprint arXiv:2511.04689, 2025

    2025 arXiv

  2. [10]

    Holistic evaluation of language models

    Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110, 2022

    2022 arXiv

  3. [11]

    Opencompass: A universal evaluation platform for foundation models

    OpenCompass Contributors . Opencompass: A universal evaluation platform for foundation models . https://github.com/open-compass/opencompass, 2023

    2023

  4. [12]

    tinybenchmarks: evaluating llms with fewer examples

    Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin. tinybenchmarks: evaluating llms with fewer examples. arXiv preprint arXiv:2402.14992, 2024

    2024 arXiv

  5. [13]

    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. In First conference on language modeling, 2024

    2024

  6. [14]

    Active learning for convolutional neural networks: A core-set approach

    Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489, 2017

    2017 arXiv

  7. [15]

    Large language models can be easily distracted by irrelevant context

    Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Sch \"a rli, and Denny Zhou. Large language models can be easily distracted by irrelevant context. In International Conference on Machine Learning, pp.\ 31210--31227. PMLR, 2023

    2023

  8. [16]

    Unnatural language inference

    Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, and Adina Williams. Unnatural language inference. In Proceedings of the 59th annual meeting of the Association for Computational Linguistics and the 11th international joint conference on natural language processing (Volume...

    2021

  9. [17]

    Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

    Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adri \`a Garriga-Alonso, et al. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions ...

    2023

  10. [18]

    Dataset cartography: Mapping and diagnosing datasets with training dynamics

    Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi. Dataset cartography: Mapping and diagnosing datasets with training dynamics. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Proce...

    2020

  11. [19]

    Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models

    Nandan Thakur, Nils Reimers, Andreas R \"u ckl \'e , Abhishek Srivastava, and Iryna Gurevych. Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models. arXiv preprint arXiv:2104.08663, 2021

    2021 arXiv

  12. [20]

    Llama 2: Open foundation and fine-tuned chat models

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023

    2023 arXiv

  13. [21]

    Rethinking llm evaluation: Can we evaluate llms with 200x less data? arXiv preprint arXiv:2510.10457, 2025

    Shaobo Wang, Cong Wang, Wenjie Fu, Yue Min, Mingquan Feng, Isabel Guan, Xuming Hu, Conghui He, Cunxiang Wang, Kexin Yang, et al. Rethinking llm evaluation: Can we evaluate llms with 200x less data? arXiv preprint arXiv:2510.10457, 2025

    2025

  14. [22]

    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. Advances in Neural Information Processing Systems, 37: ...

    2024

  15. [23]

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

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35: 0 24824--24837, 2022

    2022

  16. [24]

    Explainable chain-of-thought reasoning: An empirical analysis on state-aware reasoning dynamics

    Sheldon Yu, Yuxin Xiong, Junda Wu, Xintong Li, Tong Yu, Xiang Chen, Ritwik Sinha, Jingbo Shang, and Julian McAuley. Explainable chain-of-thought reasoning: An empirical analysis on state-aware reasoning dynamics. arXiv preprint arXiv:2509.00190, 2025

    2025

  17. [25]

    Automatic chain of thought prompting in large language models

    Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. Automatic chain of thought prompting in large language models. arXiv preprint arXiv:2210.03493, 2022

    2022 arXiv

  18. [26]

    Redundancy principles for mllms benchmarks

    Zicheng Zhang, Xiangyu Zhao, Xinyu Fang, Chunyi Li, Xiaohong Liu, Xiongkuo Min, Haodong Duan, Kai Chen, and Guangtao Zhai. Redundancy principles for mllms benchmarks. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pap...

    2025

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.