MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning

Chaoran Hu; Guiyao Tie; Lichao Sun; Mengqu Sun; Pan Zhou; Ruihang Zhang; Sizhe Zhang; Tianhe Gu; Xueyang Zhou; Yan Zhang

REVIEW 5 major objections 8 minor 1 cited by

MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning

T0 review · 5 major / 8 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A new multimodal benchmark scores the thinking trace as well as the answer and finds that correct answers frequently hide incoherent reasoning in the strongest models.

desk verdict Useful accuracy benchmark, but the reasoning-quality headline rests on an unvalidated GPT-4o judge, and the abstract cites Gemini-2.5 Pro without trace-level data. read the letter →

arxiv 2505.16459 v4 pith:5R4KTUKL submitted 2025-05-22 cs.AI

Guiyao Tie , Xueyang Zhou , Tianhe Gu , Ruihang Zhang , Chaoran Hu , Sizhe Zhang , Mengqu Sun , Yan Zhang

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Pan Zhou Lichao Sun

This is my paper · ORCID

classification cs.AI

keywords MMLU-Reasonbenchmarkmultimodalreasoningtraceevaluationchain-of-thoughtmulti-modallargelanguagemodelsthinkingqualitymetricsoverthinkingerrortaxonomy

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

MMLU-Reason is a new benchmark of 1,083 difficult multimodal questions spanning logic, math, code, maps, science, and space-time reasoning. Its contribution is a Reasoning Trace Evaluation Pipeline (RTEP) that looks inside the intermediate thinking a model emits before its final answer, scoring how relevant that thinking is to the question, how consistent it is with the answer, and what kinds of errors it contains. The paper's central empirical claim is that models with explicit thinking traces beat non-thinking models on accuracy, but even the strongest thinking models routinely produce incoherent, overlong, or irrelevant reasoning. The result is a measurable gap between accuracy and reasoning quality, which the authors argue makes trace-level evaluation necessary rather than optional.

What carries the argument

The Reasoning Trace Evaluation Pipeline (RTEP) is the central instrument. It takes the intermediate thinking trace a model produces before answering and scores it on three 0-10 scales: RTQ (how relevant the thinking is to the question), RTA (how logically consistent the thinking is with the final answer), and RSC (whether the reasoning steps stay logically coherent with each other). It also classifies thinking errors (inconsistency, overthinking, irrelevant thinking, repetitive thinking) and answer errors (reasoning error, perceptual error, format error, extraction error, reject to answer). RTEP is automated through GPT-4o prompts, and its role is to make trace-level reasoning quality measurable and comparable across models and tasks.

What would settle it

Select a random sample of, say, 100 reasoning traces from MMLU-Reason, have independent human raters score them on the same RTQ, RTA, and RSC rubrics, and compare with GPT-4o's scores. If the human-model agreement is low or the judge systematically favors or penalizes certain model families, the reported accuracy-reasoning gap and error-type frequencies would be artifacts of the evaluator rather than properties of the models.

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Extended reading notes

Core claim

The paper claims that a model can answer correctly while reasoning badly, and that this is common at the current frontier: Claude-3.7-Sonnet and Gemini-2.5 Pro, among the strongest evaluated models, show inconsistency and overthinking in their traces, and the single most frequent thinking error for Claude-3.7-Sonnet is internal contradiction at 41.5%. The authors also find that models that emit explicit thinking traces (MLLMs-T) outperform standard MLLMs overall, with Gemini-2.5 Pro reaching 42.36% test accuracy while the human-with-GPT-4o upper bound sits at 52.85%. RTEP quantifies the gap through three trace-level metrics (RTQ, RTA, RSC) and an error taxonomy, and the paper argues this exposes a critical misalignment between surface-level correctness and reasoning fidelity.

Load-bearing premise

The entire diagnosis of reasoning quality rests on trusting GPT-4o's automatic scores to reflect real reasoning quality, and the paper provides no human-agreement check to show those scores are not biased toward or against particular model families.

Editorial extensions

If this is right

Answer accuracy alone is no longer a sufficient evaluation metric for multimodal reasoning models; any assessment that claims to measure reasoning must also score the trace.
Current thinking models are not uniformly better: their advantage over non-thinking models is real on aggregate, but task-level performance varies widely, with code and map reasoning showing the lowest ceilings.
Longer thinking traces do not imply better reasoning, since the paper's modular Dual system produced traces several times longer than Claude-3.7-Sonnet's yet scored lower on trace relevance and consistency despite sometimes higher accuracy.
Because reasoning errors outnumber perceptual errors in the failure distribution, the practical bottleneck for current multimodal models is symbolic structure and multi-step inference, not basic vision.
If RTEP scores are adopted, future model development can be guided by trace quality alongside accuracy, which the paper argues should push architectures toward more cognitively aligned reasoning.

Reading between the lines

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

Beyond the paper: if trace-quality scoring becomes routine, training objectives could shift from final-answer reward toward step-consistency and relevance pressure, a concrete architectural change the paper does not propose.
Beyond the paper: a natural next experiment is to use RTEP scores as a filtering signal during decoding or as a reward model in reinforcement learning for reasoning models, testing whether the accuracy-reasoning gap is trainable or inherent.
Beyond the paper: the error taxonomy suggests concrete interventions such as length-control or self-consistency checks that could be injected mid-generation to suppress overthinking and contradiction, though the paper does not test them.

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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

Worth reading for the accuracy comparison, no more than that until the trace pipeline is validated. The 1,083-question set spanning six reasoning domains is a real contribution, and the paper does something prior benchmarks mostly skip: it tries to score the reasoning trace, not just the final answer. The accuracy table is internally consistent, and the finding that thinking-enabled models generally beat non-thinking ones is supported within the paper's own setup. The validation/test split, expert baselines, and per-task breakdowns make this a usable diagnostic tool.

What is new is the combination of that dataset with the RTQ/RTA/RSC trace metrics and error-type labels. The idea of evaluating trace quality is not original—MME-CoT already benchmarks CoT quality in multimodal models—but the six-domain coverage plus the relevance/consistency decomposition is a convenient packaging and could be useful.

The soft spot is load-bearing. Section 4.2 says GPT-4o scores the traces, but there is no human-agreement study for RTQ/RTA/RSC, no adversarial check, and no bias analysis. The Krippendorff alpha in Appendix A is for answer-label annotation, not trace-quality judgments. If GPT-4o is biased toward its own style or against long traces, the reported inconsistency and overthinking pathologies are artifacts. That directly hits the abstract's main claim. On top of that, the abstract names Gemini-2.5 Pro as a model with reasoning pathologies, but Table 4 only reports trace metrics for Claude-3.7-sonnet and the Dual configuration. I cannot verify the Gemini claim from the presented evidence. There are also no confidence intervals, and no dataset or code is attached to the paper despite a project page.

The accuracy half holds up. The reasoning-quality half is conditional on releasing the benchmark and validating the judge against human ratings. I would send this to reviewers, because the dataset deserves scrutiny and judge validation is exactly what referees should demand, but I would not cite the reasoning-quality finding as established.

Referee Report

5 major / 8 minor

Summary. This paper introduces MMLU-Reason, a multi-modal reasoning benchmark of 1,083 questions across six domains (logic, math, space-time, code, map, science), and RTEP, a pipeline that uses GPT-4o to score the intermediate reasoning traces of MLLMs on relevance, answer-relevance, and step consistency, and to classify thinking and answer errors. The authors report accuracy for 17 models, trace-quality metrics for Claude-3.7-sonnet and a custom 'Dual' (GPT-4V + DeepSeek-R1) configuration, and an error-type analysis on Claude-3.7-sonnet's validation set. The central claim is that MLLMs with thinking (MLLMs-T) outperform non-thinking MLLMs on accuracy while still exhibiting reasoning pathologies such as inconsistency and overthinking, producing a persistent gap between answer accuracy and reasoning quality. The paper positions this as a new direction for benchmark evaluation.

Significance. If the reasoning-quality metrics were properly validated, MMLU-Reason would be a genuinely useful instrument: the task taxonomy is broad, the questions require non-trivial inference, and the focus on the reasoning trace is still rare in the multi-modal benchmark literature. The accuracy table is internally consistent and the dataset description is detailed. However, the paper's headline finding — an accuracy-reasoning gap — rests on a single unvalidated GPT-4o judge, and the human-gap comparison uses a validation-only expert baseline. The paper also lacks confidence intervals or significance tests. These gaps are fixable, but they currently prevent the empirical conclusions from being treated as established.

major comments (5)

[Section 4.2, Tables 4 and Figure 5] RTQ, RTA, RSC, and the Err-T/Err-A classifications are produced entirely by GPT-4o as the automated evaluator, yet no human-agreement study, rubric calibration, or adversarial validation is reported for these trace-level judgments. The only inter-annotator agreement in the paper (Krippendorff's alpha 0.84, Appendix A) concerns the Expert (Human only) answer labels on the validation set. Because the same judge scores traces produced by models that include GPT-4V, o4-mini, and Gemini-2.5 Pro, a systematic stylistic preference of GPT-4o (e.g., for concise traces or for phrasing similar to its own style) would be reported as 'inconsistency,' 'overthinking,' or 'irrelevant thinking.' The accuracy-reasoning gap that drives the paper's conclusions could therefore be a judge artifact. Please add a human-annotation agreement study on a representative sample, a per-model bias analysis, and a qualitative inspection of GPT-4o's judgments.
[Abstract and Section 4.3, Table 4] The abstract states that 'even top models like Claude-3.7-Sonnet and Gemini-2.5 Pro suffer from reasoning pathologies such as inconsistency and overthinking,' but the paper reports trace-quality metrics (RTQ, RTA, RSC, ThinkErr) only for Claude-3.7-sonnet and the Dual configuration in Table 4; no trace-level results for Gemini-2.5 Pro appear anywhere. The error-type distributions in Figure 5 are also limited to Claude-3.7-sonnet on the validation set. Thus the specific claim about Gemini-2.5 Pro is not supported by the evidence presented.
[Section 1 and Table 3] The human-gap comparison is based on a mismatch between validation-set and test-set numbers. The text says 'Gemini-2.5 Pro achieves a test accuracy of 42.45%,' but Table 3 shows 42.45 for the validation set (106 questions) and 42.36 for the test set (977 questions). The Expert (Human only) and Expert (Human + GPT-4o) rows are reported only under the Validation column; the 52.85% figure used in the abstract and Figure 1 is therefore a validation-set result. Comparing a validation-set human baseline to test-set model accuracy is not an apples-to-apples comparison. Please report the expert baselines on the test set, or explicitly label all numbers in this comparison as validation-set performance and avoid the word 'test'.
[Table 3] No confidence intervals, significance tests, or repeated-run variance are provided for any accuracy number. With 977 test questions, a 42% accuracy has a standard error of roughly 1.6 percentage points, and the per-task columns (141-212 questions) have standard errors of 3-4 points. Consequently, claims such as 'MLLMs-T overall outperform MLLMs' and the ranking of Gemini-2.5 Pro above Claude-3.7-sonnet rest on differences that may be within sampling noise. Please add error bars or a statistical comparison (e.g., a per-question paired test across models).
[Section 2.2 and Table 4] The metric scales are inconsistent and the OS definition is under-justified. Section 2.2 says RTQ and RTA are 'normalized within the [0,1] interval,' but Table 4 reports values on a 0-10 scale (e.g., RTQ 9.39). The OS formula, 0.3*RTQ + 0.3*RTA + 0.3*RSC + 0.1*(ACC*0.1), mixes a 0-10 scale with a percentage; the weights are arbitrary. Please unify the scales, state the OS computation clearly, and justify or drop the OS aggregation when drawing conclusions about relative reasoning quality.

minor comments (8)

[Section 4.2] The word 'Pipline' should be 'Pipeline.'
[Figure 5] The label 'F ormat Error' should be 'Format Error.'
[Table 2] The entries 'Average Reasoning Depth 4.15 levels' and 'Reasoning Steps per Question 3.42' are reported without definitions of how depth and step count were computed; please add a short annotation protocol.
[Section 2.1] The difficulty split (Easy:Medium:Hard = 30%:40%:30%) is stated without a rubric or agreement measure; a brief description of the difficulty annotation would help reproducibility.
[References] References [28] and [29] list arXiv IDs 2405.67890 and 2405.12345; these identifiers do not resolve to publicly verifiable papers. Please confirm the citations or replace them with correct sources.
[General] The paper does not provide a data or code availability statement beyond the project page URL. For a benchmark contribution, a persistent link to the dataset and evaluation scripts is expected.
[Table 3] The 'Dual' model is a custom pipeline (GPT-4V + DeepSeek-R1), not an off-the-shelf model; the table should mark it as a configuration rather than a model to avoid confusion with the other rows.
[Appendix A] The 'Expert (Human only)' baseline reaches only 29.23% on the validation set, which is close to the Frequent Choice baseline (26.8%); a brief note on why the human baseline is so low (e.g., question difficulty, time pressure, or grading strictness) would preempt confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark and its metrics are externally anchored, and the main weakness is an unvalidated GPT-4o judge, which is a measurement-validity risk rather than a circular derivation.

full rationale

I walked the paper's claimed derivation chain: dataset construction (Section 2.1), RTEP metrics RTQ/RTA/RSC (Section 2.2), main accuracy results (Table 3), trace-quality comparisons (Table 4), and error-type analysis (Section 4.3). Accuracy is computed by comparing model-selected options with annotated gold answers; no accuracy number is fitted to or derived from the paper's conclusions. RTQ, RTA, and RSC are produced by GPT-4o from each model's reasoning trace through standardized prompts, so they are externally measured quantities, not parameters recovered from the target claim. The closest concern is that the same judge family (OpenAI) evaluates traces from GPT-4V and o4-mini, and the abstract asserts that Gemini-2.5 Pro suffers pathologies even though Table 4 reports trace metrics only for Claude-3.7-sonnet and the Dual configuration. These are legitimate validity and evidentiary gaps, but they are not circularity: no equation reduces an output to an input, and there is no fitted parameter renamed as a prediction. The paper also reports no human-agreement study for trace-quality scores, and the only Krippendorff's alpha (0.84) applies to answer-label annotation, not trace judgments. This means the headline accuracy-reasoning gap could be a judge artifact, but that is a correctness risk, not a self-referential derivation. I therefore find no circular step and assign a score of 0, consistent with the rule that honest non-finding is the default when the central derivation is self-contained against external benchmarks.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The benchmark and its empirical claims rest on the correctness of dataset labels, the validity of the GPT-4o judge, and the stability of proprietary model behavior. None of these is independently verified in the manuscript. The only hand-chosen numeric input that affects a reported ranking is the OS weighting in Table 4.

free parameters (1)

OS weights for overall score = 0.3 / 0.3 / 0.3 / 0.1 (RTQ, RTA, RSC, ACC)
The overall score in Table 4 weights RTQ, RTA, RSC, and ACC by hand-chosen coefficients; the ranking of Claude-3.7-Sonnet versus the Dual model depends on this arbitrary weighting.

assumptions (3)

domain assumption Dataset questions have correct, unambiguous gold answers.
The benchmark's accuracy results assume the 977 test labels are reliable; no independent human verification of test labels is reported, and expert baselines were run only on the 106 validation questions (Appendix A).
domain assumption GPT-4o judgments of RTQ/RTA/RSC are valid proxies for reasoning quality.
Section 4.2 uses GPT-4o as an automated evaluator with no reported agreement against human annotations or calibration on known-bad traces.
domain assumption Proprietary model outputs are stable enough for comparison.
Section 3 evaluates closed APIs at a single point in time; model updates can change traces and accuracy, so exact numbers may not replicate.

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Cite this review

Pith. "Pith review of MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning." pith.science (2026). https://pith.science/paper/5R4KTUKL

@misc{pith2026250516459,
  author       = {Pith},
  title        = {Pith review of: MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5R4KTUKL}},
  note         = {Machine review of arXiv:2505.16459}
}

read the original abstract

Recent advances in Multi-Modal Large Language Models (MLLMs) have enabled unified processing of language, vision, and structured inputs, opening the door to complex tasks such as logical deduction, spatial reasoning, and scientific analysis. Despite their promise, the reasoning capabilities of MLLMs, particularly those augmented with intermediate thinking traces (MLLMs-T), remain poorly understood and lack standardized evaluation benchmarks. Existing work focuses primarily on perception or final answer correctness, offering limited insight into how models reason or fail across modalities. To address this gap, we introduce the MMLU-Reason, a new benchmark designed to rigorously evaluate multi-modal reasoning with explicit thinking. The MMLU-Reason comprises 1) a high-difficulty dataset of 1,083 questions spanning six diverse reasoning types with symbolic depth and multi-hop demands and 2) a modular Reasoning Trace Evaluation Pipeline (RTEP) for assessing reasoning quality beyond accuracy through metrics like relevance, consistency, and structured error annotations. Empirical results show that MLLMs-T overall outperform non-thinking counterparts, but even top models like Claude-3.7-Sonnet and Gemini-2.5 Pro suffer from reasoning pathologies such as inconsistency and overthinking. This benchmark reveals persistent gaps between accuracy and reasoning quality and provides an actionable evaluation pipeline for future model development. Overall, the MMLU-Reason offers a scalable foundation for evaluating, comparing, and improving the next generation of multi-modal reasoning systems.

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