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

arXiv: https://arxiv.org/abs/2508.17180 · HTML: https://mlsanity.com/paper/2508.17180/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 10 of 10)

- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Nightshade: Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models](https://mlsanity.com/paper/2310.13828/) (2023)
- [CountQA: How Well Do MLLMs Count in the Wild?](https://mlsanity.com/paper/2508.06585/) (2025)
- [GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning](https://mlsanity.com/paper/2506.00785/) (2025)
- [MAEA: Multimodal Attribution for Embodied AI](https://mlsanity.com/paper/2307.13850/) (2023)
- [HueManity: Probing Fine-Grained Visual Perception in MLLMs](https://mlsanity.com/paper/2506.03194/) (2025)
- [Semantic Augmentation in Images using Language](https://mlsanity.com/paper/2404.02353/) (2024)
- [Attribution Regularization for Multimodal Paradigms](https://mlsanity.com/paper/2404.02359/) (2024)
- [User Persona Identification and New Service Adaptation Recommendation](https://mlsanity.com/paper/2311.10773/) (2023)
- [Vehicular Cooperative Maneuvers -- Quo Vaditis?](https://mlsanity.com/paper/2311.18430/) (2023)

## Built on by (most cited first, 4 of 4)

- [GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models](https://mlsanity.com/paper/2511.11134/) (2025)
- [The Artificial Intelligence Cognitive Examination: A Survey on the Evolution of Multimodal Evaluation from Recognition to Reasoning](https://mlsanity.com/paper/2510.04141/) (2025)
- [Confidence Aware SSD Ensemble with Weighted Boxes Fusion for Weapon Detection](https://mlsanity.com/paper/2509.23697/) (2025)
- [Understanding Virality: A Rubric based Vision-Language Model Framework for Short-Form Edutainment Evaluation](https://mlsanity.com/paper/2512.21402/) (2025)

## Abstract (arXiv)

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.