# The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems?

arXiv: https://arxiv.org/abs/2508.21143 · HTML: https://mlsanity.com/paper/2508.21143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 29 of 29)

- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://mlsanity.com/paper/2310.02255/) (2023)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://mlsanity.com/paper/2402.14008/) (2024)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://mlsanity.com/paper/2406.16860/) (2024)
- [BLINK: Multimodal Large Language Models Can See but Not Perceive](https://mlsanity.com/paper/2404.12390/) (2024)
- [Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset](https://mlsanity.com/paper/2402.14804/) (2024)
- [The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://mlsanity.com/paper/2506.06941/) (2025)
- [SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models](https://mlsanity.com/paper/2307.10635/) (2023)
- [Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers](https://mlsanity.com/paper/2506.23918/) (2025)
- [Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models](https://mlsanity.com/paper/2403.00231/) (2024)
- [Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark](https://mlsanity.com/paper/2501.05444/) (2025)
- [Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models](https://mlsanity.com/paper/2305.15074/) (2023)
- [CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark](https://mlsanity.com/paper/2401.11944/) (2024)
- [A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3](https://mlsanity.com/paper/2302.05729/) (2023)
- [VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information](https://mlsanity.com/paper/2412.00947/) (2024)
- [What is the Visual Cognition Gap between Humans and Multimodal LLMs?](https://mlsanity.com/paper/2406.10424/) (2024)
- [ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness](https://mlsanity.com/paper/2504.10514/) (2025)
- [Exploring Perceptual Limitation of Multimodal Large Language Models](https://mlsanity.com/paper/2402.07384/) (2024)
- [FCoReBench: Can Large Language Models Solve Challenging First-Order Combinatorial Reasoning Problems?](https://mlsanity.com/paper/2402.02611/) (2024)
- [MMRo: Are Multimodal LLMs Eligible as the Brain for In-Home Robotics?](https://mlsanity.com/paper/2406.19693/) (2024)
- [Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs](https://mlsanity.com/paper/2502.16435/) (2025)
- [Do You See Me : A Multidimensional Benchmark for Evaluating Visual Perception in Multimodal LLMs](https://mlsanity.com/paper/2506.02022/) (2025)
- [NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models](https://mlsanity.com/paper/2407.10380/) (2024)
- [VisuRiddles: Fine-grained Perception is a Primary Bottleneck for Multimodal Large Language Models in Abstract Visual Reasoning](https://mlsanity.com/paper/2506.02537/) (2025)
- [IQBench: How "Smart'' Are Vision-Language Models? A Study with Human IQ Tests](https://mlsanity.com/paper/2505.12000/) (2025)
- [VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning](https://mlsanity.com/paper/2503.00043/) (2025)
- [Exploring Multimodal Perception in Large Language Models Through Perceptual Strength Ratings](https://mlsanity.com/paper/2503.06980/) (2025)
- [MANBench: Is Your Multimodal Model Smarter than Human?](https://mlsanity.com/paper/2506.11080/) (2025)

## Abstract (arXiv)

Cognitive science research treats visual perception, the ability to understand and make sense of a visual input, as one of the early developmental signs of intelligence. Its TVPS-4 framework categorizes and tests human perception into seven skills such as visual discrimination, and form constancy. Do Multimodal Large Language Models (MLLMs) match up to humans in basic perception? Even though many benchmarks evaluate MLLMs on advanced reasoning and knowledge skills, there is limited research that focuses evaluation on simple perception. In response, we introduce Percept-V, a dataset containing 6000 program-generated uncontaminated images divided into 30 domains, where each domain tests one or more TVPS-4 skills. Our focus is on perception, so we make our domains quite simple and the reasoning and knowledge required for solving them are minimal. Since modern-day MLLMs can solve much more complex tasks, our a-priori expectation is that they will solve these domains very easily. Contrary to our belief, our experiments show a weak performance of SoTA proprietary and open-source MLLMs compared to very high human performance on Percept-V. We find that as the number of objects in the image increases, performance goes down rather fast. Our experiments also identify the perception skills that are considerably harder for all models. Fine-tuning an open-source MLLM shows considerable gains in performance, though the gains only marginally carry over to other related datasets, pointing to limitation in generalization abilities of the learned representations.