THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models

Yi Chang; Yuan Wu; Zhiyuan Li

REVIEW 4 major objections 7 minor 2 cited by

THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Think-Bench measures how much of a reasoning model's chain of thought is waste, and finds most models overthink easy questions.

desk verdict Useful benchmark artifact, but the headline overthinking finding rests on an undefined difficulty split and a circular LLM-judge pipeline; the data and protocol need to be released before I'd trust the numbers. read the letter →

arxiv 2505.22113 v1 pith:6V67FFXI submitted 2025-05-28 cs.CL

Zhiyuan Li , Yi Chang , Yuan Wu This is my paper

classification cs.CL

keywords largereasoningmodelsoverthinkingchain-of-thoughtthinkingefficiencymetricqualitybenchmarkevaluationfirst-correct-answerdetection

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

Think-Bench asks a practical question: when a large reasoning model writes out its chain of thought, how much of that writing is actually needed? The paper argues that most leading reasoning models systematically overthink, especially on easy questions, producing long chains that continue well past the point where the correct answer has already appeared. To make this measurable, it introduces an efficiency metric, the share of reasoning tokens generated before the first correct answer, and evaluates 11 models on 1,375 mathematics, physics, and chemistry problems. The results show that for 10 of the 11 models this share stays below 0.55, and that most models are even less efficient on simple questions than on difficult ones. If the benchmark is right, it gives researchers a direct way to compare and eventually reduce wasteful inference.

What carries the argument

The load-bearing machinery is the efficiency ratio $\text{Efficiency} = \frac{1}{N}\sum_{i=1}^N \hat{T}_i / T_i$, where $\hat{T}_i$ is the number of tokens the model generates before the first occurrence of the correct answer and $T_i$ is the total number of reasoning tokens for instance $i$; a model that finds the answer early and then keeps writing scores low. Around this ratio the paper builds a full evaluation protocol: human-annotated key steps for each question, reference-based recall and precision of those steps, reflection-quality scoring that counts only reflections that find errors or add confirming insight, and six efficiency-related indicators including first-correct tokens, reflection tokens, and thought-switch counts. A commercial large language model, Claude 3.7 Sonnet, serves as both the step annotator and the judge that decides when the first correct answer and valid reflections occur.

What would settle it

Re-annotate the 1,375 Think-Bench questions with human difficulty ratings given blind to model outputs, and have independent human judges mark the first correct answer in each model chain; then recompute each model's simple-question efficiency. If the simple-vs-difficult gap shrinks or reverses, or if human first-answer boundaries differ substantially from the judge model's, the overthinking-on-easy-questions claim would not survive.

Watch

Extended reading notes

Core claim

The paper's central claim is that large reasoning models exhibit a measurable overthinking problem: they keep generating tokens after a correct answer has already been reached, and this redundancy is worst on the easiest questions. Quantitatively, on Think-Bench the average ratio of tokens before the first correct answer to total reasoning tokens is below 0.55 for 10 of the 11 evaluated models, with only one model exceeding that level, and the per-difficulty comparison shows most models are less efficient on simple questions than on difficult ones. At the same time, the paper finds that chain-of-thought quality, measured by recall and precision of key steps, is often high even when efficiency is low, so the two dimensions come apart.

Load-bearing premise

The claim that models overthink on easy questions assumes that the Simple/Difficult labels are meaningful and not confounded with token count; the paper never defines how difficulty was assigned, so arbitrary or length-correlated labels would undo the conclusion.

Editorial extensions

If this is right

On Think-Bench, 10 of the 11 large reasoning models score an average efficiency ratio below 0.55, meaning the majority of their reasoning tokens are written after the first correct answer.
Most models show lower efficiency on simple questions than on difficult ones, so the waste is concentrated exactly where extra reasoning adds least value.
High chain-of-thought quality does not guarantee efficiency: the models with the best reflection quality also use the most tokens per response.
The benchmark's nine metrics let future systems be compared on thinking efficiency and reasoning quality rather than final accuracy alone, giving early-exit and concise-reasoning methods a quantitative target.

Reading between the lines

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

A direct test the paper leaves implicit: efficiency should predict real-world cost, so report per-question token-to-accuracy trade-offs on a held-out set to see whether the 0.55 threshold maps onto meaningful latency and compute savings.
Because the same model is used to annotate key steps, judge step matches, and locate the first correct answer, an independent judge or human spot-check on a random subset would show how much of the gap is a property of the models versus a property of the judge.
If difficulty labels were assigned by answer length or source benchmark, a training or prompting intervention that simply truncates reasoning at the first correct answer might look artificially beneficial; matching models on latent difficulty would test whether overthinking is truly task-dependent.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague—

The useful part of this paper is the artifact: 1,375 questions across math/physics/chemistry with key-step annotations, and a cleanly defined efficiency metric (first-correct-token position over total tokens). Evaluating 11 LRMs with that metric is a reasonable service to the community, and the limitations section is more honest than most. The overthinking trend they report—most models below 0.55 efficiency—is plausible and consistent with earlier work like Chen et al. on o1 overthinking.

The soft spots are concentrated where the headline claim lives. The Simple/Difficult split is load-bearing: Table 3, which shows lower efficiency on simple questions for most models, is the evidence for "overthinking easy questions." But Section 2.1 never says how difficulty was assigned, only that the counts are approximately equal. No protocol, no mapping from source datasets, no inter-annotator agreement. If "simple" correlates with short reference chains, the first correct answer will appear early in any model output by construction, and the efficiency gap becomes an artifact. That concern isn't hypothetical; it's exactly what the paper would need to rule out.

Second, the evaluation pipeline is circular in a way that directly touches the numbers. Claude 3.7 Sonnet generates the key-step annotations, judges semantic matches, identifies first correct tokens, and is also one of the evaluated models. Scores for Claude in particular could be self-consistent artifacts. The authors acknowledge this in the Limitations, but it's not just a caveat—it's the measurement instrument.

Third, there are no error bars or inter-annotator agreement stats. The prompt-based judges are deterministic given the prompt, but the prompts themselves are only as reliable as the LLM, and skipping empty outputs automatically (Section 4.3) could bias results upward for the smaller distilled models.

What's genuinely new: the compact dataset and the aggregate efficiency comparison across 11 models. That's worth having, if the data and prompts are released. The headline claim itself is not new—prior work already showed o1-like models overthink simple arithmetic.

Who's this for? Researchers working on LRM inference efficiency or early-exit mechanisms; they'd want the dataset and metrics. A serious referee should engage, but the paper needs major revision: define the difficulty labels, validate the key-step annotations with human agreement, release the data and prompts, and report confidence intervals. I'd send it to review rather than desk reject, because the artifact is useful and the problems are fixable.

Referee Report

4 major / 7 minor

Summary. The paper introduces Think-Bench, a 1,375-question benchmark spanning mathematics, physics, and chemistry, with human-curated (LLM-generated) key-step annotations for each question. It proposes six efficiency-related metrics and two CoT-quality metrics, evaluates eleven large reasoning models, and reports that most models exhibit overthinking on easy questions: their Efficiency, defined in Eq. (1) as the ratio of tokens before the first correct answer to total reasoning tokens, is below 0.55 for 10 of 11 models and is often lower on 'Simple' than on 'Difficult' questions (Table 3). The paper frames the benchmark as a reusable tool for measuring reasoning efficiency and CoT quality.

Significance. If the difficulty labels and LLM-based judgments were properly validated, Think-Bench would be a useful contribution: it targets a real and timely problem, covers multiple disciplines, makes the Efficiency metric transparent, and evaluates a broad set of models including proprietary and open-source reasoning models. The central overthinking trend is plausible and is not definitionally forced by Eq. (1); it is a contingent empirical pattern that deserves scrutiny. The paper also gives credit for being explicit about evaluation prompts, which supports reproducibility, although the project page does not state whether data, annotations, or code will be released. The main weaknesses are validation gaps in the difficulty labels, the dual role of Claude 3.7 Sonnet as annotator/judge and evaluated model, the automatic skipping of empty outputs, and the absence of uncertainty quantification; each of these affects the strength of the headline claims but is addressable in revision.

major comments (4)

[§2.1, Table 3, §4.2] The Simple/Difficult split is load-bearing for the headline claim that 'most LRMs exhibit overthinking in handling easy questions,' but the paper never defines how difficulty is assigned. Section 2.1 only states that the numbers of simple and difficult questions are approximately equal, with no annotation protocol, criteria, per-source mapping, or inter-annotator agreement. The conclusion in §4.2 that models show 'significantly lower average efficiency on simple questions' is therefore not reproducible or falsifiable as written. The authors must provide the exact labeling procedure, the distribution of labels across the source datasets, and evidence of label reliability; without this, the simple-versus-difficult efficiency gap in Table 3 could reflect label construction or correlation with reference-chain length rather than a model behavior.
[§2.3, §3.2, §4.1, Limitations] Claude 3.7 Sonnet generates the key-step annotations (Prompt 1), judges semantic matches between model steps and reference steps (Prompts 2 and 3), evaluates reflection quality (Prompt 6), and is itself one of the eleven evaluated models. This creates a risk of self-consistent artifacts for Claude-specific scores and unvalidated reference annotations for all models. The Limitations paragraph acknowledges this concern, but acknowledgment is not a substitute for validation. The paper should report human agreement on a sample of key-step annotations and judge decisions, or at minimum compare the Claude judge against a second independent judge, and should discuss how judge disagreement propagates into Recall, Precision, and the Efficiency metric. Without such validation, the absolute CoT-quality numbers and the cross-model comparisons in Tables 2 and 3 are not firmly supported.
[§4.3] The paper states that samples with empty outputs or missing final answers were 'automatically skipped,' but it does not report how many samples were skipped, for which models, or on which difficulty levels. If smaller or weaker models skip more difficult questions, then the per-difficulty and per-model efficiency estimates in Tables 2 and 3 are systematically biased. The authors should report skip counts per model, per difficulty, and per subject, and should include a sensitivity analysis in which empty outputs are treated as failures (Efficiency = 0) to verify that the headline overthinking trend is robust to this choice.
[§4.2, Tables 2 and 3] No confidence intervals, standard errors, or significance tests are reported for any of the metrics. The claim that efficiency is 'significantly lower' on simple questions is not backed by a statistical test, and several Table 3 differences are small (e.g., 0.49 vs. 0.50 for DeepSeek-r1-distill-qwen-32b, 0.52 vs. 0.53 for DeepSeek-r1-distill-qwen-14b). The authors should report bootstrap confidence intervals over questions and paired tests (e.g., Wilcoxon signed-rank or a bootstrap test of the paired difference) for the simple-versus-difficult efficiency comparison, and should temper the wording accordingly.

minor comments (7)

[References and §2.1] The citation for Math500 appears to be wrong: Hendrycks et al. 2020b is listed as the MMLU paper, but Math500 refers to a different MATH dataset; the correct reference should be added or the dataset name corrected.
[Appendix B, Tables 4 and 5] The caption abbreviations for the DeepSeek-R1-distill variants are mismatched: the entry 'Ds-r1-distill-qwen-14b' is described as deepseek-r1-distill-qwen-32b, and 'Ds-r1-distill-qwen-32b' is described as deepseek-r1-distill-qwen-7b. These labels should be corrected.
[§3 and Table 2] Accuracy is reported in Table 2 but is not defined in Section 3; the paper should state how final answers are scored, especially for free-form questions, and whether scoring is exact-match or judge-based.
[§3.1, Eq. (1), Figure 7] The computation of $\hat{T}_i$ is underspecified: Prompt 5 returns a paragraph index, and Figure 7 reports Efficiency = 278/819, but the text does not explain how a paragraph index is converted to a token count or whether the calculation includes tokens from the question or only generated tokens. This should be clarified for the metric to be reproducible.
[§3.1, Thought Num] The Thought Num metric is estimated by counting discourse markers such as 'alternatively' and 'wait a moment,' but no marker list is provided and the heuristic is not validated against human counts. Reporting the exact marker set and a small validation sample would strengthen this metric.
[§2.1, Table 1] The paper should report the exact number of simple and difficult questions per subject and per source dataset, rather than only stating that the numbers are approximately equal.
[§1 and Project Page] For a benchmark paper, the availability of data, annotations, prompts, and evaluation code should be stated explicitly; the text points to a project page but does not specify what will be released under what license.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: efficiency is anchored to external gold answers; undefined difficulty labels and Claude-as-judge are validity concerns, not by-construction reductions.

full rationale

The headline overthinking result is an empirical measurement, not a by-construction identity. Efficiency (Eq. 1) is the ratio of tokens before the first occurrence of the gold answer to total reasoning tokens; the gold answer comes from source datasets, so the metric is anchored externally rather than in the benchmark's own key-step annotations. The Simple/Difficult comparison in Table 3 is load-bearing, but Section 2.1 never defines how difficulty is assigned; this is a reproducibility and falsifiability gap, but no equation ties the labels to the efficiency outcome, so it is not a demonstrated circular reduction. The Limitations section explicitly acknowledges that Claude 3.7 Sonnet is used both as an evaluated model and as the judge, which can bias Recall, Precision, and Reflection Quality; this self-reference weakens independence of those secondary scores, but the paper flags it and it does not force the efficiency values. The self-citations are contextual and not load-bearing. Overall, no specific step reduces to its input by construction; score 2 reflects minor self-reference without central circularity.

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

The central evaluation rests on LLM-generated key steps and LLM-judged matching, and neither is independently verified. No numeric parameters are fitted to data, so the free-parameter count is zero.

assumptions (3)

ad hoc to paper Claude 3.7 Sonnet's generated key steps are complete and correct for all 1,375 questions.
Section 2.3 uses Claude 3.7 Sonnet to generate reference reasoning chains and derive key steps; no human verification or inter-annotator agreement is reported.
ad hoc to paper Claude 3.7 Sonnet as judge correctly determines semantic matches between model steps and reference steps.
Section 3.2 and Appendix D prompts rely on this judge; the paper does not validate judge output against human labels.
ad hoc to paper Simple versus Difficult labels are meaningful and correctly assigned.
Section 2.1 states approximate balance but gives no definition of difficulty; Table 3 uses these labels for the main overthinking-on-easy-questions conclusion.

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Cite this review

Pith. "Pith review of THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models." pith.science (2026). https://pith.science/paper/6V67FFXI

@misc{pith2026250522113,
  author       = {Pith},
  title        = {Pith review of: THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6V67FFXI}},
  note         = {Machine review of arXiv:2505.22113}
}

read the original abstract

Large reasoning models (LRMs) have achieved impressive performance in complex tasks, often outperforming conventional large language models (LLMs). However, the prevalent issue of overthinking severely limits their computational efficiency. Overthinking occurs when models generate excessive and redundant tokens that contribute little to accurate outcomes, especially in simple tasks, resulting in a significant waste of computational resources. To systematically investigate this issue, we introduce Think-Bench, a benchmark designed to evaluate the reasoning efficiency of LRMs. We also propose novel efficiency metrics and conduct a comprehensive evaluation of various LRMs across multiple dimensions, including the reasoning process, outcome quality, and chain-of-thought (CoT) characteristics. Our analysis reveals that most LRMs exhibit overthinking in handling easy questions, generating unnecessarily lengthy reasoning chains. While many LRMs demonstrate high CoT quality, several suffer from low efficiency. We hope that Think-Bench can serve as a robust foundation for advancing research into LRMs.

Figures

Figures reproduced from arXiv: 2505.22113 by the authors.

**Figure 1.** The performance of various LRMs on Think-Bench. The results suggest that these prominent LRMs face a challenge of overthinking. reasoning capabilities of LLMs, constructing highquality multidisciplinary datasets and conducting systematic evaluations has become critically important (Chang et al., 2024; Xia et al., 2024). Recent research have introduced several highquality multidisciplinary benchmarks, such as MML… view at source ↗

**Figure 2.** Overview of Think-Bench. Our benchmark contains a comprehensive efficiency evaluation framework with curated datasets across three categories. capabilities and operational reliability. Existing evaluation paradigms that ignore CoT quality risk masking systemic deficiencies, such as erroneous premises, logical fallacies, or context misinterpretations, which may propagate through the reasoning chain. Consequently, es… view at source ↗

**Figure 3.** Category and Subcategory Distribution of Think [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Illustration of Thinking Efficiency and CoT Quality Evaluation. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]

**Figure 5.** Figure 5: Example of Thinking Process Analysis in a [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]

**Figure 6.** Figure 6: Comparative Performance of Models in Chemistry, Physics, and Math. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Examples of Efficiency Evaluation. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]

**Figure 8.** Figure 8: Failure Case Example in the Deepseek-r1-distill-qwen-32b Answering Process. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

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Forward citations

Cited by 2 Pith papers

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

Strategic Reflectivism In Intelligent Systems
cs.AI 2025-05 conditional novelty 4.0 of 10

Strategic Reflectivism holds that intelligent systems should allocate reflective reasoning tactically, weighing its benefits against its costs.
Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey
cs.AI 2025-07 conditional novelty 3.0 of 10

A comprehensive review that categorizes methods for shortening and adaptively triggering chain-of-thought reasoning in large language models.

Reference graph

Works this paper leans on

31 extracted references · 15 canonical work pages · cited by 2 Pith papers

[1]

Only include the essential key steps, and don’t output unnecessary words

work page
[2]

For each solution, record: -logical_conclusion: The set of each key step of the solution, from Step 1 all the way to the answer

work page
[3]

A problem may contain more than one way of reasoning, so make sure you don’t miss any possible solutions

work page
[4]

Important: Output only JSON array with no additional information

work page
[5]

solution1

Don’t add useless words to the process Here is the json output format: ## Output format [ {{ "solution1": {{ 15 "logical_conclusion": ["step1:","step2:","step3:",...] }} }} ] Recall Evaluation Prompt # Task Overview You are an expert system for verifying solutions to text-based problems. Your task is to match the ground truth middle steps with the provide...

work page
[9]

Problem: The original question/task
[10]

A Solution of a model
[11]

step_index

Ground Truth: Essential steps required for a correct answer # MATCHING PROCESS: You need to evaluate each ground truth middle step against the solution, following these criteria: ## Match Criteria: - **Exact Match or Equivalent Logical Step**: A ground truth step is considered **Matched** if: - It appears exactly in the solution **OR** - The same logical ...

work page

Show all 31 references

[12]

**Strict JSON Output**: Output only the JSON array with no additional text or explanations
[13]

correct_answer

**No Omitted Steps**: Every step in ‘Ground Truth‘ must be evaluated. # EDGE CASE HANDLING: - If a step is conceptually equivalent but reworded, it is still considered **Matched**. - If numerical transformations are equivalent (e.g., same formula in a different form), it is **...
[14]

Logical Inference Steps - Contains exactly one logical deduction - Must produce a new derived conclusion - Cannot be just a summary or observation
[15]

Match": Aligns with ground truth -

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...
[16]

The output format MUST be in valid JSON format without ANY other content
[17]

For highly repetitive patterns, output it as a single step
[18]

Always include the final step that contains the answer

Output maximum 35 steps. Always include the final step that contains the answer
[19]

step_type

correct_answer: Whether the whole reasoning process produces the right answer. Here is the json output format: ## Output Format [ {{ "step_type": "logical inference|background information", "premise": "Evidence", "conclusion": "Step result", "judgment": "Match|Reasonable|Wrong...
[20]

Reach accurate conclusions aligned with ground truth
[21]

19 Invalid reflections include:

Use new insights to find the mistake of the previous conclusion or verify its correctness. 19 Invalid reflections include:
[22]

Repetition - Restating previous content or method without new insights
[23]

Wrong Conclusion - Reaching incorrect conclusions vs ground truth
[24]

Incompleteness - Proposing but not executing new analysis methods
[25]

Other - Additional error types # Input Format [Problem] {question} [Think Content] {think_content} [Ground Truth] {gt_annotation} # Output Requirements
[26]

The output format must be in valid JSON format without any other content
[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 1. Preserve orig...
[28]

No new interpretations
[29]

Include ALL reflection steps
[30]

Empty list if no reflections found
[31]

Direct JSON output without any other output 20
[2023]

p\" and the cost of a jumbo eraser \

Arb: Advanced reasoning benchmark for large language models. arXiv preprint arXiv:2307.13692. Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, An- drew Wen, Shaochen Zhong, Hanjie Chen, and 1 others. 2025. Stop overthinking: A survey o...

2025 arXiv
[2024]

arXiv preprint arXiv:2412.21187

Do not think that much for 2+ 3=? on the overthinking of o1-like llms. arXiv preprint arXiv:2412.21187. Alejandro Cuadron, Dacheng Li, Wenjie Ma, Xingyao Wang, Yichuan Wang, Siyuan Zhuang, Shu Liu, Luis Gaspar Schroeder, Tian Xia, Huanzhi Mao, and 1 others. 2025. The danger of...

2025 arXiv
[2025]

Kehua Feng, Keyan Ding, Weijie Wang, Xiang Zhuang, Zeyuan Wang, Ming Qin, Yu Zhao, Jianhua Yao, Qiang Zhang, and Huajun Chen

Missing premise exacerbates overthinking: Are reasoning models losing critical thinking skill? arXiv preprint arXiv:2504.06514. Kehua Feng, Keyan Ding, Weijie Wang, Xiang Zhuang, Zeyuan Wang, Ming Qin, Yu Zhao, Jianhua Yao, Qiang Zhang, and Huajun Chen. 2024. Sciknoweval: Eval...

2024 arXiv

Pith tools

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

[1] [1]

Only include the essential key steps, and don’t output unnecessary words

work page

[2] [2]

For each solution, record: -logical_conclusion: The set of each key step of the solution, from Step 1 all the way to the answer

work page

[3] [3]

A problem may contain more than one way of reasoning, so make sure you don’t miss any possible solutions

work page

[4] [4]

Important: Output only JSON array with no additional information

work page

[5] [5]

solution1

Don’t add useless words to the process Here is the json output format: ## Output format [ {{ "solution1": {{ 15 "logical_conclusion": ["step1:","step2:","step3:",...] }} }} ] Recall Evaluation Prompt # Task Overview You are an expert system for verifying solutions to text-based problems. Your task is to match the ground truth middle steps with the provide...

work page

[6] [9]

Problem: The original question/task

[7] [10]

A Solution of a model

[8] [11]

step_index

Ground Truth: Essential steps required for a correct answer # MATCHING PROCESS: You need to evaluate each ground truth middle step against the solution, following these criteria: ## Match Criteria: - **Exact Match or Equivalent Logical Step**: A ground truth step is considered **Matched** if: - It appears exactly in the solution **OR** - The same logical ...

work page