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REVIEW 5 major objections 4 minor 22 cited by

MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency

T0 review · 5 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read MME-CoT is a benchmark that evaluates chain-of-thought in large multimodal models, claiming that reflection-equipped models reason best, CoT often degrades perception tasks, and reflective models are inefficient.

desk verdict Genuinely useful multimodal CoT evaluation suite, but the Kimi-vs-GPT-4o headline is undercut by a 150-question subset comparison. read the letter →

arxiv 2502.09621 v1 pith:Q2EG34XD submitted 2025-02-13 cs.CV cs.AIcs.CL

Dongzhi Jiang , Renrui Zhang , Ziyu Guo , Yanwei Li , Yu Qi , Xinyan Chen , Liuhui Wang , Jianhan Jin
show 6 more authors
Claire Guo Shen Yan Bo Zhang Chaoyou Fu Peng Gao Hongsheng Li
This is my paper · ORCID
classification cs.CVcs.AIcs.CL
keywords chain-of-thoughtmultimodalreasoningbenchmarklargemodelsreflectionqualityperceptionrobustnessevaluation
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

MME-CoT is a benchmark for how large multimodal models reason step by step. It measures not just whether final answers are right but whether each intermediate step is informative, faithful, relevant, and whether reflecting on previous steps helps. Across six visual domains, the authors find that reflection-equipped models such as Kimi k1.5 produce the highest-quality reasoning, that forcing step-by-step thinking often degrades pure perception tasks, and that reflective models waste large fractions of their output. The paper's value is a process-level view of where chain-of-thought helps and where it backfires.

What carries the argument

The evaluation suite is built on human-annotated key steps—inference conclusions plus image captions that a correct solution must contain—and on a GPT-4o-based step partitioner that splits each model output into atomic steps typed as logical inference, image caption, or background. Quality is recall (fraction of key steps matched) and precision (fraction of generated key steps correct), combined as F1. Robustness compares direct-answer versus step-by-step prompting on perception tasks (stability) and reasoning tasks (efficacy). Efficiency is relevance rate (share of steps that work toward the solution, rescaled with a floor of 0.8) and reflection quality (share of identified reflection steps judged valid). These three pairs of metrics are the mechanism that turns raw model outputs into the paper's findings.

What would settle it

Take a random sample of the judged outputs (say 200 model responses), have human annotators redo the step matching, relevance, and reflection-validity judgments, and measure agreement with GPT-4o's judgments; if agreement is below the paper's annotation reliability level, or if the re-judged F1 changes the Kimi k1.5 vs GPT-4o order, the central ranking claim is not settled.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that process-level evaluation changes the ranking of vision-language models relative to answer-only benchmarks. Kimi k1.5 edges GPT-4o on CoT quality (F1 64.2 vs 64.0), mostly through precision rather than recall; GPT-4o retains the highest recall. Most tested models lose accuracy on perception tasks when asked to reason step by step (e.g., InternVL2.5-8B drops 6.8%), while only some gain on reasoning tasks. On efficiency, even the best reflective models earn low relevance rates and reflection quality around 60-72%, meaning roughly a quarter to two-fifths of reflection steps do not advance the solution. The authors interpret these as actionable failure modes: overthinking, distraction by irrelevant image detail, and reflection that repeats, degrades, or aborts.

Load-bearing premise

Every trust in the rankings rests on GPT-4o correctly partitioning outputs into steps and faithfully matching and judging those steps; if it systematically misjudges, the model order can change, especially where scores are close.

Editorial extensions

If this is right

  • If step quality is the criterion, reflection-based training is a promising direction: Kimi k1.5 and QVQ outscore their non-reflective counterparts on F1.
  • Deploying CoT universally is unsafe for perception-heavy workloads until stability is fixed; the paper's stability metric can be used as a regression test.
  • Efficiency budgets in long-CoT systems can be set by relevance and reflection-quality scores rather than by output length alone.
  • Answer-only benchmarks may overstate reasoning ability when models reach correct answers by skipping key steps.

Reading between the lines

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

  • One testable extension is to rerun the quality metrics with a second judge (e.g., a different model or human raters) and measure judge-induced rank shifts; the close Kimi/GPT-4o gap makes this meaningful.
  • The stability result suggests a potential training signal: penalizing CoT-generated steps that are irrelevant to perception questions could recover the lost accuracy; the authors do not propose this.
  • The relevance-rate rescaling with alpha=0.8 amplifies small differences; a sensitivity analysis over alpha would show whether the efficiency rankings are an artifact of that choice.
  • Because the benchmark samples 150 questions for Kimi k1.5, comparing its scores to models evaluated on all 1,130 questions assumes subset representativeness; re-running on a matched subset would test this.
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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 / 4 minor

Summary. MME-CoT introduces a 1,130-question benchmark for evaluating chain-of-thought (CoT) reasoning in large multimodal models, spanning six domains (math, science, OCR, logic, space-time, general scenes) with human-verified key-step annotations and reference image captions. The paper proposes three evaluation axes: CoT quality (recall and precision of predicted steps against annotated key steps, aggregated as F1), CoT robustness (stability on perception tasks and efficacy on reasoning tasks, computed as accuracy differences between direct and CoT prompting), and CoT efficiency (relevance rate, rescaled by Eq. (10) with alpha=0.8, and reflection quality). Thirteen LMMs are evaluated, leading to three headline findings: reflection-equipped models achieve the highest CoT quality with Kimi k1.5 slightly above GPT-4o, CoT prompting degrades perception-heavy tasks for most models, and reflection models are inefficient with a large fraction of invalid reflection steps.

Significance. If the evaluation pipeline is trustworthy, MME-CoT would be a valuable community resource: it is the first multimodal CoT benchmark to move beyond final-answer accuracy and to measure process-level quality, robustness, and efficiency at a fine-grained step resolution. The dataset curation is a genuine strength: key steps and reference captions were human-verified, statistics are reported, and the full evaluation prompts are provided in Appendix E, which supports reproducibility. The error analysis of reflection failure modes (ineffective reflection, incompleteness, repetition, interference) is also useful and actionable. However, the paper's central ranking claim and several secondary comparisons rest on an unvalidated GPT-4o judge pipeline and on an unevaluated 150-question subset for Kimi k1.5, so the quantitative conclusions are currently not established at the claimed precision.

major comments (5)
  1. [Section 4.1, Table 2, abstract] The headline claim that Kimi k1.5 outperforms GPT-4o in CoT quality is not supported by the reported evidence. Section 4.1 states that Kimi k1.5 was evaluated on 150 sampled questions (115 reasoning, 35 perception) due to access limits, yet Table 2 reports Kimi's F1 of 64.2 against GPT-4o's 64.0 computed on the full benchmark. Since CoT quality is computed only on reasoning questions, this is effectively Kimi on about 115 reasoning items versus GPT-4o on 837. The sampling procedure is not described, no per-category or per-domain results for Kimi are given (Kimi is absent from Table 3), and no confidence intervals are reported. A gap of 0.2 F1 points is far smaller than the sampling error expected for roughly 115 items, so the ranking could easily be an artifact of subset selection. Please report the sampling method, per-category breakdown for Kimi, and uncertainty estimates, or remove the superiority claim from the abstract and Section 4.2.
  2. [Sections 3.1, 3.3, Appendix E] Every process-level metric depends on GPT-4o's ability to partition model outputs into atomic steps, match predicted steps to human key steps, judge step correctness, judge relevance, and identify and validate reflection steps. The authors validate the dataset annotations but do not report any human validation or agreement study for these GPT-4o judgments. Because the key ranking differences are small (Kimi k1.5 F1 64.2 vs GPT-4o 64.0; reflection quality 72.2 vs 61.7 for QVQ), systematic judge bias could reorder the conclusions. Please add a human agreement study on a sample of the step-matching, relevance, and reflection judgments, or at least report the judge's self-consistency and show that the qualitative findings are robust to a noisy judge.
  3. [Eq. (10), Section 3.3] The relevance rate is rescaled via (r - alpha)/(1 - alpha) with a hand-picked alpha=0.8, but no justification or sensitivity analysis is provided. This transform nonlinearly amplifies small differences near the top of the raw distribution and can change model rankings. Please report the raw relevance rates alongside the rescaled ones and show that the conclusions (e.g., InternVL2.5-8B having the highest relevance, long-CoT models being more distractible) are stable across a range of alpha values.
  4. [Section 4.2, Table 2] Reflection quality is set to 100 for all models that do not reflect, as stated in Section 4.2 ('we define the reflection quality as 100 on models incapable of reflection'). This makes 'no reflection' numerically indistinguishable from 'perfect reflection' and biases the efficiency comparison in favor of non-reflective models. The paper's third claim, that reflection models are inefficient, is thus partially built into the metric definition. Please either exclude non-reflective models from the reflection-quality analysis, report the number of reflection steps separately, or clearly relabel the 100 entries as 'not applicable' rather than a score of 100.
  5. [Section 4.2, Table 2 footnote] The robustness scores for Mulberry, LLaVA-CoT, Virgo, QVQ, and Kimi k1.5 are flagged with asterisks as unreliable because these models refuse to answer directly and instead generate extended rationales. Nevertheless, the paper still reports their stability and efficacy values as if comparable, and the text states that 'GPT-4o achieves the highest robustness score' after acknowledging that several other scores may be misleading. Mixing unreliable and reliable numbers in the same table and in the summary statistics can mislead readers. Please either exclude these rows from the quantitative robustness comparison and discuss them only qualitatively, or provide a corrected scoring protocol for models that ignore the direct prompt.
minor comments (4)
  1. [Throughout] The paper contains several typographical issues, including 'MME-C OT' in Table 1 and Table 2 captions, 'Rea.' in Section 4.2, and 'Nan-Multiple-Choice' in Appendix E.3. These should be corrected before publication.
  2. [Table 3] Kimi k1.5 is missing from the per-category results in Table 3, even though the overall quality and efficiency numbers for Kimi are central to the paper's claims. Adding per-category results (or a clear explanation of why they cannot be provided) is necessary to assess the 150-question subset.
  3. [Section 2.1] The two-stage perception/reasoning classification uses GPT-4o and Qwen2-VL-7B accuracy differences to guide the preliminary categorization, and GPT-4o later serves as the judge for the process-level metrics. This is not circular in a formal sense because final labels are human-verified, but the potential dependence of the task split on the same model family used for evaluation should be acknowledged explicitly.
  4. [Figure 1] Figure 1 contains many numbers and arrows in a small space and is difficult to parse; consider simplifying it or moving the detailed scores to a table.

Circularity Check

1 steps flagged · score 4.0 of 10

The robustness finding is partly baked into the perception/reasoning split; the CoT quality and efficiency metrics are otherwise self-contained.

  1. self definitional [Section 2.1, 'Data Composition and Categorization', with Section 3.2, Eq. (6)]
    "Initially, we leverage LMMs to guide the preliminary categorization by comparing their performance with and without CoT prompting. We employ GPT-4o and Qwen2-VL-7B to answer questions using both direct and CoT approaches. Superior performance with CoT indicates a reasoning-dominant subcategory, while comparable or inferior CoT performance suggests either perception-focused content or insufficient model reasoning capabilities. ... Stability = AccP_COT − AccP_DIR."

    The headline robustness finding ('CoT prompting often degrades LMM performance on perception-heavy tasks') is measured by Eq. (6) as the CoT-vs-direct accuracy gap on the perception set P. But the perception set P was preliminarily constructed by the very same comparison: subcategories where CoT is comparable or inferior to direct prompting are labeled perception-focused. For those subcategories, a negative Stability score is therefore partly a restatement of the labeling rule rather than an independent empirical discovery.

full rationale

Aside from the perception/reasoning categorization issue, the paper's CoT quality and efficiency metrics do not reduce to their inputs. The key-step annotations are human-verified; recall/precision are defined against those annotations; relevance rate is a fixed rescaling (alpha = 0.8) applied uniformly rather than a fitted parameter; reflection quality is judged against a stated validity definition. The Kimi k1.5 versus GPT-4o comparison is statistically problematic because Kimi is scored on 150 sampled questions while GPT-4o is scored on all 1,130, but this is a subset-comparability flaw, not a circular derivation. No load-bearing self-citation chain or imported uniqueness theorem appears. The central benchmark has substantial independent content, so the circularity is partial and localized rather than wholesale.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central evaluation rests on assumptions about GPT-4o judging reliability, task taxonomy, and prompt compliance; none are machine-checked.

free parameters (2)
  • alpha (Relevance Rate scaling) = 0.8
    Chosen by hand in Eq. 10 to amplify differences between models; changing it rescales all reported relevance rates.
  • Reflection Quality default for non-reflecting models = 100
    The authors assign 100 to models incapable of reflection instead of treating the metric as undefined; this inflates efficiency comparisons for models that never reflect.
assumptions (5)
  • domain assumption GPT-4o can reliably partition model outputs into atomic steps and judge them against human annotations.
    Quality metrics (Eqs. 1-5) use GPT-4o for step partition and key-step matching; no human validation of judge outputs is reported.
  • domain assumption GPT-4o accurately identifies relevant steps and valid reflection steps.
    Relevance Rate and Reflection Quality (Eqs. 8-11) rely on GPT-4o judgments; the reflection indicator list is heuristic.
  • domain assumption The perception vs reasoning task classification is valid for all evaluated models.
    The taxonomy is initially derived from GPT-4o and Qwen2-VL-7B CoT performance differences (Section 2.1), then human-reviewed.
  • domain assumption Models comply with the direct-answer prompt when measuring stability.
    Section 4.2 reports that Mulberry, LLaVA-CoT, Virgo, QVQ, and Kimi k1.5 sometimes ignore the direct prompt; the authors mark those rows unreliable.
  • domain assumption Ground-truth answers and key step annotations from the 15 source datasets are correct.
    The benchmark reuses questions from MathVerse, MMMU-Pro, OlympiadBench, etc.; errors in source labels propagate.

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Cite this review

Pith. "Pith review of MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency." pith.science (2026). https://pith.science/paper/Q2EG34XD

@misc{pith2026250209621,
  author       = {Pith},
  title        = {Pith review of: MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q2EG34XD}},
  note         = {Machine review of arXiv:2502.09621}
}
read the original abstract

Answering questions with Chain-of-Thought (CoT) has significantly enhanced the reasoning capabilities of Large Language Models (LLMs), yet its impact on Large Multimodal Models (LMMs) still lacks a systematic assessment and in-depth investigation. In this paper, we introduce MME-CoT, a specialized benchmark evaluating the CoT reasoning performance of LMMs, spanning six domains: math, science, OCR, logic, space-time, and general scenes. As the first comprehensive study in this area, we propose a thorough evaluation suite incorporating three novel metrics that assess the reasoning quality, robustness, and efficiency at a fine-grained level. Leveraging curated high-quality data and a unique evaluation strategy, we conduct an in-depth analysis of state-of-the-art LMMs, uncovering several key insights: 1) Models with reflection mechanism demonstrate a superior CoT quality, with Kimi k1.5 outperforming GPT-4o and demonstrating the highest quality results; 2) CoT prompting often degrades LMM performance on perception-heavy tasks, suggesting a potentially harmful overthinking behavior; and 3) Although the CoT quality is high, LMMs with reflection exhibit significant inefficiency in both normal response and self-correction phases. We hope MME-CoT serves as a foundation for advancing multimodal reasoning in LMMs. Project Page: https://mmecot.github.io/

Figures

Figures reproduced from arXiv: 2502.09621 by the authors.

Figure 1
Figure 1. Chain-of-Thought Performance of Leading LMMs in MME-CoT. Our evaluation suite assesses LMMs using three novel metrics that yield six distinct scores. Re￾sults reveal that current open-source models, including those with reflection capabilities, still lag behind closed-source models like GPT-4o and Kimi k1.5 in key aspects of chain￾of-thought reasoning. by the recent OpenAI o1 (OpenAI, 2024a) and DeepSeek￾R1 (Guo et … view at source ↗
Figure 2
Figure 2. An Overview of MME-CoT. Our benchmark contains a comprehensive CoT evaluation suite with three novel aspects and a meticulously curated dataset encompassing six categories. In parallel, the multimodal extensions of LLMs, termed Large Multimodal Models (LMMs), have demonstrated re￾markable proficiency across diverse visual domains, e.g., general image recognition (Zhang et al., 2023; Zhu et al., 2023; OpenAI, 2023; Z… view at source ↗
Figure 3
Figure 3. Category and Subcategory Distribution of MME-CoT. Statistic Number Total questions 1,130 - Reasoning questions 837 (74.1%) Multiple-choice questions 431 Free-form questions 406 - Perception questions 293 (25.9%) Multiple-choice questions 275 Free-form questions 18 Total key step annotation 3,865 - Total inference conclusions 2,667 - Average inference conclusions 3.2 - Total image captions 1,198 - Average image capti… view at source ↗
Figures from the paper (17 more)
Figure 4
Figure 4. Figure 4: Illustration of Step Partition. We instruct GPT-4o to divide each step into three categories: image caption, background information, or logical inference. The step partition result is later used to perform step-wise reasoning evaluation. We focus on evaluating the imag…
Figure 5
Figure 5. Figure 5: Illustration of CoT Quality Evaluation. For recall, we prompt GPT-4o to match each key step annotation in the prediction. For precision, GPT-4o is instructed to split the prediction into steps and determine the correctness of all the image caption and logical inference…
Figure 6
Figure 6. Figure 6: Illustration of CoT Robustness Evaluation. We compare the performance of applying CoT prompt and direct prompt on two types of tasks: perception and reason￾ing. The stability score measures whether CoT interferes with perception, while the efficacy score assesses the p…
Figure 8
Figure 8. Figure 8: Distribution of Reflection Error Types. We identify four types of error: ineffective reflection, incom￾pleteness, repetition, and interference. ductive reflection. These patterns are illustrated in [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Data Source Distribution of MME-CoT. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Examples of Reflection Error Types. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Examples of Precision and Recall Evaluation. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]
Figure 12
Figure 12. Figure 12: Examples of Precision and Recall Evaluation. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]
Figure 13
Figure 13. Figure 13: Examples of Precision and Recall Evaluation. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: Examples of Precision and Recall Evaluation. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: Examples of Precision and Recall Evaluation. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_15.png]
Figure 16
Figure 16. Figure 16: Examples of Precision and Recall Evaluation. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_16.png]
Figure 17
Figure 17. Figure 17: Examples of Precision and Recall Evaluation. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_17.png]
Figure 18
Figure 18. Figure 18: Examples of Relevance Rate Evaluation. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_18.png]
Figure 19
Figure 19. Figure 19: Examples of Relevance Rate Evaluation. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_19.png]
Figure 20
Figure 20. Figure 20: Examples of Relevance Rate Evaluation. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_20.png]
Figure 21
Figure 21. Figure 21: Examples of Reflection Quality Evaluation. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_21.png]

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Reference graph

Works this paper leans on

42 extracted references · 25 canonical work pages · cited by 22 Pith papers

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    Problem: The original question/task

  2. [2]

    A Solution of a model

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    step_index

    Ground Truth: Essential steps required for a correct answer MATCHING PROCESS: You need to match each ground truth middle step with the solution: Match Criteria: - The middle step should exactly match in the content or is directly entailed by a certain content in the solution - All the details must be matched, including the specific value and content - You...

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    Other - Additional error types # Input Format [Problem] {question} [Solution] {solution} 33 MME-CoT: Benchmarking Chain-of-Thought in LMMs for Reasoning Quality, Robustness, and Efficiency [Ground Truth] {gt annotation} # Output Requirements

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    Direct Evaluation Prompt Answer Extraction Prompt You are an AI assistant who will help me to extract an answer of a question

    Direct JSON output without any other output E.3. Direct Evaluation Prompt Answer Extraction Prompt You are an AI assistant who will help me to extract an answer of a question. You are provided with a question and a response, and you need to find the final answer of the question. Extract Rule: [Multiple choice question]

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    Only output the JSON array with no additional information

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    Judge each ground truth middle step in order without omitting any step. Here are the problem, answer, solution, and ground truth middle steps: [Problem] {question} [Answer] {answer} [Solution] {solution} [Ground Truth Information] {gt annotation} 29 MME-CoT: Benchmarking Chain-of-Thought in LMMs for Reasoning Quality, Robustness, and Efficiency Precision ...

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    Logical Inference Steps - Contains exactly one logical deduction - Must produce a new derived conclusion - Cannot be just a summary or observation

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  3. [12]

    For highly repetitive patterns, output it as a single step

  4. [13]

    step_type

    Output maximum 40 steps. Always include the final step that contains the answer. Here is the json output format: ## Output Format [ { "step_type": "image observation|logical inference|background information", "premise": "Evidence (only for logical inference)", "conclusion": "S...

  5. [14]

    Logical Inference Steps - Contains exactly one logical deduction 31 MME-CoT: Benchmarking Chain-of-Thought in LMMs for Reasoning Quality, Robustness, and Efficiency - Must produce a new derived conclusion - Cannot be just a summary or observation

  6. [15]

    Image Description Steps - Pure visual observations - Only includes directly visible elements - No inferences or assumptions

  7. [16]

    IMPORTANT NOTE: Evaluate relevancy independent of correctness

    Background Information Steps - External knowledge or question context - No inference process involved ## Step Requirements - Each step must be atomic (one conclusion per step) - No content duplication across steps - Initial analysis counts as background information - Final ans...

  8. [17]

    help to obtain a conclusion helpful to solve the question later

  9. [18]

    help to identify certain patterns in the image later

  10. [19]

    step_type

    directly contributes to the answer v. Depicting or analyzing the options of the question is also relevant. vi. Repeating previous relevant steps are also considered relevant. The following behaviour is considered as irrelevant: i. Depicting image information that does not rela...

  11. [20]

    Reach accurate conclusions aligned with ground truth

  12. [21]

    Invalid reflections include:

    Use new insights to find the mistake of the previous conclusion or verify its correctness. Invalid reflections include:

  13. [22]

    Repetition - Restating previous content or method without new insights

  14. [23]

    Wrong Conclusion - Reaching incorrect conclusions vs ground truth

  15. [24]

    Incompleteness - Proposing but not executing new analysis methods

  16. [26]

    The output format must be in valid JSON format without any other content

  17. [27]

    conclusion

    Output maximum 30 reflection steps. Here is the json output format: ## Output Format [ { "conclusion": "One-sentence summary of reflection outcome", "judgment": "Correct|Wrong", "error_type": "N/A|Repetition|Wrong Conclusion|Incompleteness|Other" } ] # Rules

  18. [28]

    Preserve original content and order

  19. [29]

    No new interpretations

  20. [30]

    Include ALL reflection steps

  21. [31]

    Empty list if no reflections found

  22. [33]

    You should directly output the choice letter of the answer

    The answer could be answering the option letter or the value. You should directly output the choice letter of the answer

  23. [34]

    You should output a single uppercase character in A, B, C, D, E, F, G, H, I (if they are valid options), and Z

  24. [35]

    [Non Multiple choice question]

    If the meaning of all options are significantly different from the final answer, output Z. [Non Multiple choice question]

  25. [36]

    It could be hidden inside the last step of calculation or inference

    Output the final value of the answer. It could be hidden inside the last step of calculation or inference. Pay attention to what the question is asking for to extract the value of the answer

  26. [37]

    The final answer could also be a short phrase or sentence

  27. [38]

    Output Format: Directly output the extracted answer of the response

    If the response doesn’t give a final answer, output Z. Output Format: Directly output the extracted answer of the response. {In Context Examples} Question: {question} Answer: {response} 34 MME-CoT: Benchmarking Chain-of-Thought in LMMs for Reasoning Quality, Robustness, and Ef...

  28. [39]

    If the [Model Answer] is the option letter, then it must completely matches the [Standard Answer]

  29. [40]

    [Nan-Multiple-Choice questions]

    If the [Model Answer] is not an option letter, then the [Model Answer] must completely match the option content of [Standard Answer]. [Nan-Multiple-Choice questions]

  30. [41]

    The [Model Answer] and [Standard Answer] should exactly match

  31. [42]

    Output Format: 1

    If the meaning is expressed in the same way, it is also considered consistent, for example, 0.5m and 50cm. Output Format: 1. If they are consistent, output 1; if they are different, output 0

  32. [43]

    {In Context Examples} Question: {question} [Model Answer]: {extract answer} [Standard Answer]: {gt answer} Your output: 35

    DIRECTLY output 1 or 0 without any other content. {In Context Examples} Question: {question} [Model Answer]: {extract answer} [Standard Answer]: {gt answer} Your output: 35

  33. [2021]

    org/CorpusID:231591445

    URL https://api.semanticscholar. org/CorpusID:231591445. Sprague, Z., Yin, F., Rodriguez, J. D., Jiang, D., Wadhwa, M., Singhal, P., Zhao, X., Ye, X., Mahowald, K., and Durrett, G. To cot or not to cot? chain-of-thought helps mainly on math and symbolic reasoning. arXiv prepri...

    2024 arXiv

  34. [2024]

    a" and "b

    URL https://qwenlm.github.io/blog/ qvq-72b-preview/. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F., et al. Llama: Open and efficient foundation lan- guage models. arXiv preprint arXiv:2302.13971,...

    2023 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.