| title | PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models |
|---|---|
| authors | Zichao Lin, Yifeng Xie, Bowen Qu, Haiming Wang, Jia Li, Haoning Wu, Yuhao Dong, Zuhao Yang, Jinguo Zhu, Haoyu Lu, Zijia Zhao, Tongtian Yue, Zhangyang Qi, Junwei Yang, Mengfan Dong, Peizhou Cao, Chenzhuang Du, Zaida Zhou, Haotian Yao, Hao Yang, Hongcheng Gao, Lin Sui, Weihong Li, Xinxing Zu, Jia Chen, Yao Wang, Xiaoxue Wu, Yalin Wang, Y. Charles, Yiping Bao, Yangyang Liu, Zhiqi Huang, Xinyu Zhou |
| date | 2026-07-27 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.24957 |
| affiliation | Moonshot AI |
| upvotes | 16 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | We introduce PerceptionBench, a benchmark specifically designed to evaluate the atomic visual perception capabilities of Multimodal Large Language Models (MLLMs). Existing benchmarks often fail to isolate perception: holistic evaluations conflate perceptual errors with failures in reasoning or domain knowledge, while application-driven benchmarks only cover narrow, fragmented domains shaped by heuristic designs. To address these limitations, PerceptionBench adopts a bottom-up approach: by diagnosing the earliest failure points in the responses of frontier MLLMs across 42 existing benchmarks, we construct an error taxonomy whose perception branch defines ten atomic perceptual capabilities. Guided by this taxonomy, we construct 3,000 verified questions with short, unambiguous answers, each isolating a single capability, with difficulty stemming from perception rather than reasoning or knowledge. Benchmark results across sixteen frontier MLLMs reveal that atomic perception remains largely unsolved---no model reaches 60\% accuracy, perception-related hallucination is the weakest capability on average, and similar overall scores conceal sharply divergent capability profiles. PerceptionBench thus provides a capability-level standard for measuring and diagnosing the visual perception boundaries of MLLMs. |
cite
@misc{lin2026perceptionbench,
title={PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models},
author={Zichao Lin and Yifeng Xie and Bowen Qu and Haiming Wang and Jia Li and Haoning Wu and Yuhao Dong and Zuhao Yang and Jinguo Zhu and Haoyu Lu and Zijia Zhao and Tongtian Yue and Zhangyang Qi and Junwei Yang and Mengfan Dong and Peizhou Cao and Chenzhuang Du and Zaida Zhou and Haotian Yao and Hao Yang and Hongcheng Gao and Lin Sui and Weihong Li and Xinxing Zu and Jia Chen and Yao Wang and Xiaoxue Wu and Yalin Wang and Y. Charles and Yiping Bao and Yangyang Liu and Zhiqi Huang and Xinyu Zhou},
year={2026},
eprint={2607.24957},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.24957}
}