2026-07-29 04:00 UTCOriginal source2 min readUpdated: 2026-07-29 08:39 UTC

PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models

PerceptionBench is a benchmark designed to evaluate atomic visual perception in multimodal large language models (MLLMs). It uses a bottom-up approach, building an error taxonomy from 42 existing benchmarks to define ten atomic perceptual capabilities, and creates 3,000 verified questions. Tests on 16 frontier MLLMs show no model exceeds 60% accuracy, with perception-related hallucination being the weakest capability.

SourcearXiv Computer VisionAuthor: 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

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Key points

PerceptionBench adopts a bottom-up approach, constructing an error taxonomy from existing benchmarks to define ten atomic perceptual capabilities.
The benchmark includes 3,000 verified questions, each isolating a single capability, with difficulty stemming from perception rather than reasoning or knowledge.
Results across 16 frontier MLLMs reveal that no model reaches 60% accuracy, and perception-related hallucination is the weakest capability on average.

Why it matters

This matters because perceptionBench adopts a bottom-up approach, constructing an error taxonomy from existing benchmarks to define ten atomic perceptual capabilities.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

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[Submitted on 27 Jul 2026]

Title:PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models

View a PDF of the paper titled PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models, by Zichao Lin and 32 other authors

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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.

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2607.24957 [cs.CV]

(or arXiv:2607.24957v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2607.24957

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Bowen Qu [view email] [v1] Mon, 27 Jul 2026 18:04:54 UTC (10,386 KB)

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