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REVIEW 4 major objections 4 minor 31 references

MaRVL-QA, a new benchmark of visual math questions about surface plots, shows that state-of-the-art multimodal language models often guess rather than reason, scoring far below what robust spatial reasoning would require.

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MaRVL-QA is a new benchmark that tests multimodal AI systems on mathematical reasoning over surface plots, with two tasks: counting topological features and recognizing geometric transformations.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Sensible benchmark idea with a genuinely new task pair, but the abstract alone can't carry the claim that MLLM failures reflect reasoning rather than rendering artifacts. the 4 major comments →

arxiv 2508.17180 v2 pith:SGE7DMG7 submitted 2025-08-24 cs.AI cs.CVcs.LG

MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes

Nilay Pande , Sahiti Yerramilli , Jayant Sravan Tamarapalli , Rynaa Grover This is my paper
classification cs.AI cs.CVcs.LG
keywords multimodal large language modelsmathematical reasoningspatial reasoningsurface plotsbenchmarktopological countingtransformation recognitionvisual question answering
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces MaRVL-QA, a benchmark of questions about 3D surface plots of mathematical functions, designed to test whether multimodal large language models (MLLMs) can perform genuine spatial and mathematical reasoning when reading images. The benchmark includes two tasks: Topological Counting, which asks models to enumerate features such as local maxima, and Transformation Recognition, which asks models to identify geometric transformations applied to a plot. The authors argue that surface plots are a clean testbed because they isolate reasoning from the semantic noise of natural images. Their evaluation shows that even the strongest current MLLMs perform poorly on this benchmark, often falling back on superficial heuristics instead of analyzing the plot's geometry.

Core claim

The paper introduces MaRVL-QA, a benchmark of multiple-choice questions about 3D surface plots of mathematical functions. The benchmark has two tasks: Topological Counting, which asks a model to enumerate features such as local maxima, and Transformation Recognition, which asks a model to identify geometric transformations applied to a plot. All questions are generated from a curated library of functions and filtered for ambiguity so that each question has a single correct answer. In evaluations, the paper finds that state-of-the-art MLLMs perform well below human-level reasoning on these tasks, frequently relying on surface-level heuristics rather than on actual spatial and mathematical ana

What carries the argument

The central object is the MaRVL-QA benchmark itself: a question-generation pipeline built on a curated library of two-variable functions. The pipeline produces surface-plot images paired with automatically generated questions and answers; a rigorous ambiguity-filtering stage removes questions whose ground truth is not unambiguous. The two task types, Topological Counting and Transformation Recognition, force a model to reason about the geometry of the surface rather than identify objects or scenes. The benchmark's work is to isolate mathematical-spatial reasoning from the semantic noise of natural images and to make model performance measurable and comparable.

Load-bearing premise

The benchmark is only as valid as its ground truth: the paper assumes that after ambiguity filtering, every remaining question has one correct answer that can be read off the plotted surface, and that the rendered plots faithfully represent the underlying functions; if that assumption fails, poor model scores may be artifacts of the data instead of evidence of weak reasoning.

What would settle it

A concrete disconfirming result would be a state-of-the-art MLLM scoring near or above human accuracy on a held-out MaRVL-QA split after training on similar surface-plot question-answer pairs; alternatively, showing that human annotators disagree on a substantial fraction of the filtered questions at the same rate models do would suggest the ambiguity filter does not guarantee unique answers.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • MaRVL-QA gives the research community a repeatable, quantitative way to track progress in mathematical reasoning from images.
  • Because the benchmark filters ambiguous questions, poor performance on it is evidence of a reasoning deficit rather than noisy labels.
  • The two task types isolate specific reasoning skills, so failures can be attributed to particular abilities such as counting local features or recognizing transformations.
  • The benchmark can be used to compare models directly and to guide training-data selection for improving mathematical reasoning.
  • The results indicate that surface plots are a harder test for current MLLMs than natural images, making progress on MaRVL-QA a meaningful milestone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the benchmark is as clean as claimed, a natural extension is to apply the same question-generation pipeline to contour plots, vector fields, or higher-dimensional slices; this would test whether the observed reasoning gap is specific to surface plots or generalizes to other visual mathematical forms.
  • The two tasks could be converted into a pretraining objective: training models on automatically generated topological-counting questions might improve downstream spatial reasoning, a testable prediction from the paper's framing.
  • The reported failure modes suggest that architectures with explicit geometric inductive biases, rather than purely text-pretrained visual encoders, might close part of the gap; the paper itself does not test this hypothesis.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The manuscript introduces MaRVL-QA, a proposed benchmark for evaluating multimodal large language models (MLLMs) on mathematical and spatial reasoning over surface plots. It describes two tasks: Topological Counting, which involves identifying and enumerating features such as local maxima, and Transformation Recognition, which involves recognizing geometric transformations. The abstract states that the benchmark is generated from a curated library of functions with rigorous ambiguity filtering and that evaluation reveals state-of-the-art MLLMs struggle significantly, often resorting to superficial heuristics. However, the submitted manuscript contains only the abstract; the full text, including all methodological details, dataset information, experimental results, and analysis, is absent.

Significance. If the benchmark is realized with unambiguous, visually answerable questions, it could be a valuable testbed for MLLM spatial reasoning, as surface plots isolate mathematical reasoning from the semantic noise of natural images. The two proposed tasks are sensible and potentially challenging. The claim that MLLMs resort to heuristics rather than robust reasoning would be an important result if substantiated. At present, however, the significance cannot be assessed: no evidence is provided that the benchmark is valid, reproducible, or correctly evaluated.

major comments (4)
  1. [Abstract (entire)] The central empirical claim—'even state-of-the-art MLLMs struggle significantly'—is made without any numerical results, model names, baselines, error bars, or experimental protocol. For a benchmark paper, this evaluation is the main deliverable. Its absence makes the claim unverifiable. A results table and statistical analysis must be provided.
  2. [Abstract, 'rigorous ambiguity filtering'] The ambiguity filtering procedure is not described. If filtering is based on analytic properties of the functions (e.g., a unique global maximum in the domain) rather than properties of the rendered plot (e.g., visible extrema at the plot mesh resolution, clipping boundaries, contour artifacts), then a question may have a unique mathematical answer but no visually determinate answer. This would undermine the interpretation of MLLM failures as reasoning deficits. The filtering algorithm and validation must be included.
  3. [Full text (absent)] The manuscript body is missing. There is no description of the function library, question-generation templates, task demonstrations, evaluation procedure, or dataset release link. Without these, the benchmark is not reproducible and its construct validity cannot be checked. This is a load-bearing omission that prevents any substantive review.
  4. [Abstract, 'often resorting to superficial heuristics'] The claim that MLLMs resort to superficial heuristics is a specific behavioral conclusion that requires qualitative or controlled evidence—for example, response-pattern analysis, ablation of visual features, or error-type categorization. The abstract does not indicate such analysis. Please provide concrete examples and quantitative indicators.
minor comments (4)
  1. [Abstract] The phrase 'deep mathematical and spatial reasoning' is undefined. Consider operationalizing the target abilities to make the benchmark's scope precise.
  2. [Abstract] The task names 'Topological Counting' and 'Transformation Recognition' are introduced without examples. A figure or sample question would clarify the tasks.
  3. [Abstract] No comparison to existing multimodal reasoning benchmarks (e.g., MathVista, CLEVR) is provided; the novelty claim needs related-work context.
  4. [Abstract] The 'curated library of functions' is vague. Specify the class of functions, generation constraints, and any exclusion criteria beyond ambiguity.

Circularity Check

0 steps flagged

No significant circularity detected in MaRVL-QA based on available evidence.

full rationale

The provided material is an abstract for a benchmark paper; it contains no mathematical derivation chain, no fitted parameters presented as predictions, and no load-bearing self-citations. The benchmark's construct validity depends on the assumption that its generated questions, after ambiguity filtering, have unambiguous ground-truth answers that can be determined from the rendered surface plots. This is an empirical/validity assumption, not a circular step: the benchmark's evaluation results (MLLMs struggling) are not derived from the filtering procedure, nor are the filtering criteria shown to be defined in terms of the model outputs being evaluated. Without access to the full paper, the ambiguity-filtering methodology cannot be inspected, but absence of evidence of circularity is not evidence of circularity. No quoted passage exhibits a reduction of a claimed result to its own inputs. Therefore the correct finding is no significant circularity, score 0.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

No new physical entities or mathematical constants are introduced. The benchmark's validity rests on design assumptions about what the tasks measure and what the filtering guarantees.

axioms (3)
  • domain assumption Surface plots isolate the task of reasoning from semantic noise.
    The abstract states this as the motivation for using surface plots; if untrue, benchmark scores could conflate image analysis with reasoning.
  • domain assumption The ambiguity filtering ensures that every question has a unique, intended answer.
    The abstract claims 'rigorous ambiguity filtering' but the filtering criteria are not described in the abstract; the correctness of labels is essential to the benchmark.
  • domain assumption MLLM performance on MaRVL-QA is a measure of mathematical/spatial reasoning capability.
    The paper interprets low scores as evidence of 'superficial heuristics instead of robust spatial reasoning', an inference that depends on the construct validity of the tasks.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes." pith.science (2026). https://pith.science/paper/SGE7DMG7

@misc{pith2026250817180,
  author       = {Pith},
  title        = {Pith review of: MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SGE7DMG7}},
  note         = {Machine review of arXiv:2508.17180}
}
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read the original abstract

A key frontier for Multimodal Large Language Models (MLLMs) is the ability to perform deep mathematical and spatial reasoning directly from images, moving beyond their established success in semantic description. Mathematical surface plots provide a rigorous testbed for this capability, as they isolate the task of reasoning from the semantic noise common in natural images. To measure progress on this frontier, we introduce MaRVL-QA (Mathematical Reasoning over Visual Landscapes), a new benchmark designed to quantitatively evaluate these core reasoning skills. The benchmark comprises two novel tasks: Topological Counting, identifying and enumerating features like local maxima; and Transformation Recognition, recognizing applied geometric transformations. Generated from a curated library of functions with rigorous ambiguity filtering, our evaluation on MaRVL-QA reveals that even state-of-the-art MLLMs struggle significantly, often resorting to superficial heuristics instead of robust spatial reasoning. MaRVL-QA provides a challenging new tool for the research community to measure progress, expose model limitations, and guide the development of MLLMs with more profound reasoning abilities.

discussion (0)

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Reference graph

Works this paper leans on

31 extracted references · 24 canonical work pages · 3 internal anchors

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    Introducing claude 4, 2025

    Anthropic . Introducing claude 4, 2025. Model release announcement

    work page 2025

  3. [3]

    Lawrence Zitnick, and Devi Parikh

    Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. VQA : Visual question answering. In Proceedings of the IEEE International Conference on Computer Vision, pages 2425--2433, 2015

    work page 2015

  4. [4]

    Qwen2.5-vl technical report, 2025

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report, 2025

    2025

  5. [5]

    Vehicular Cooperative Maneuvers -- Quo Vaditis?

    Jiahao Chen, Shuo Cheng, Linyong Hou, Chao Zhang, and Jing Li. UniGeo : A unified framework for solving geometry problems. arXiv preprint arXiv:2311.18430, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  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. In International Conference on Machine Learning, pages 2095--2108. PMLR, 2021

    work page 2095

  7. [7]

    Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025

    Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025

    2025

  8. [8]

    Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024

    Chaoyou Fu, Peixian Chen, Yunhang Shen, Yulei Qin, Mengdan Zhang, Xu Lin, Jinrui Yang, Xiawu Zheng, Ke Li, Xing Sun, Yunsheng Wu, and Rongrong Ji. Mme: A comprehensive evaluation benchmark for multimodal large language models, 2024

    2024

  9. [9]

    Making the V in VQA matter: Embodying bias in evaluation and reasoning aåbout visual content

    Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the V in VQA matter: Embodying bias in evaluation and reasoning aåbout visual content. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6904--6913, 2017

    work page 2017

  10. [10]

    Huemanity: Probing fine-grained visual perception in mllms, 2025

    Rynaa Grover, Jayant Sravan Tamarapalli, Sahiti Yerramilli, and Nilay Pande. Huemanity: Probing fine-grained visual perception in mllms, 2025

    work page 2025

  11. [11]

    Measuring mathematical problem solving with the MATH dataset

    Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the MATH dataset. arXiv preprint arXiv:2103.03874, 2021

    Pith/arXiv arXiv 2021

  12. [12]

    Lim, and Sameer Divvala

    Zeeshan Hossain, Zadie Wu, Jia-Bin Xie, Stanley H. Lim, and Sameer Divvala. SciCap : A new benchmark for captioning scientific figures. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2502--2510, 2022

    work page 2022

  13. [13]

    Lawrence Zitnick, and Ross Girshick

    Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross Girshick. CLEVR : A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2901--2910, 2017

    work page 2017

  14. [14]

    FigureQA : An annotated figure dataset for visual reasoning

    Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, Csaba K \"a ding, Ann-Kathrin Zipf, and Kerry Schipper. FigureQA : An annotated figure dataset for visual reasoning. In Workshop at the 32nd Conference on Neural Information Processing Systems, 2018

    work page 2018

  15. [15]

    BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models . In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023 a

    work page 2023

  16. [16]

    Evaluating object hallucination in large vision-language models, 2023 b

    Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models, 2023 b

    work page 2023

  17. [17]

    Resonances and residue operators for pseudo-Riemannian hyperbolic spaces

    Fangyu Liu, Julian Mintz, Shuo Cheng, Linyong Hou, Chao Zhang, Da Chen, and Jing Li. DePlot : One-shot visual language localization. arXiv preprint arXiv:2303.04780, 2023 a

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  18. [18]

    Visual Instruction Tuning

    Haotian Liu, Chunyuan Li, Qingyan Wu, and Yong Jae Lee. Visual Instruction Tuning . In Advances in Neural Information Processing Systems, 2023 b

    work page 2023

  19. [19]

    LLaVA-NeXT : Improved reasoning, ocr, and world knowledge, 2024

    Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. LLaVA-NeXT : Improved reasoning, ocr, and world knowledge, 2024

    work page 2024

  20. [20]

    User Persona Identification and New Service Adaptation Recommendation

    Zu-Yu Liu, Zhaida Wang, Matt Gwilliam, Jinyue Cong, Jiacheng Li, He Ji, Ting-Hao Chang, and Heng Yu. Evaluating compositional structures in text-to-image models. arXiv preprint arXiv:2311.10773, 2023 c

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  21. [21]

    Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts, 2024

    Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts, 2024

    2024

  22. [22]

    ChartQA : A benchmark for question answering about charts with a human-written rationales

    Ahmed Masry, Do Long, Jiaxin Tan, Shafiq Joty, and Enamul Hoque Mite-Baidal. ChartQA : A benchmark for question answering about charts with a human-written rationales. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5443--5463, 2022

    work page 2022

  23. [23]

    El-allaui, V

    Nitesh Methani, Pratik Gpt, M. El-allaui, V. Varma, and K. Sankaranarayanan. PlotQA : Reasoning over scientific plots. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 417--422, 2020

    work page 2020

  24. [24]

    Mistral Medium Large Language Model

    Mistral AI . Mistral Medium Large Language Model . https://docs.mistral.ai/, 2025 a

    work page 2025

  25. [25]

    Mistral Small 3.1

    Mistral AI . Mistral Small 3.1 . https://mistral.ai/news/mistral-small-3-1, 2025 b

    work page 2025

  26. [26]

    Pixtral Large : Frontier multimodal ai

    Mistral AI et al. Pixtral Large : Frontier multimodal ai. https://mistral.ai/news/pixtral-large, 2024. Available at Hugging Face: https://huggingface.co/mistralai/Pixtral-Large-Instruct-2411

    work page 2024

  27. [27]

    OpenAI o3 and o4-mini System Card , 2025

    OpenAI . OpenAI o3 and o4-mini System Card , 2025. Accessed: August 1, 2025

    work page 2025

  28. [28]

    Gpt-4 technical report, 2024

    OpenAI, 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, 2024

    work page 2024

  29. [29]

    Learning Transferable Visual Models From Natural Language Supervision

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning Transferable Visual Models From Natural Language Supervision . In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021

    work page 2021

  30. [30]

    Spatial reasoning in large language models: A survey

    Yuxiang Wu, Zheyuan Li, Zheyuan Liu, Bailin Li, Zefan Li, Mingshen Yuan, and He Ji. Spatial reasoning in large language models: A survey. arXiv preprint arXiv:2310.13828, 2023

    Pith/arXiv arXiv 2023

  31. [31]

    Learning to count objects in natural images for visual question answering, 2018

    Yan Zhang, Jonathon Hare, and Adam Prügel-Bennett. Learning to count objects in natural images for visual question answering, 2018

    work page 2018

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.