Moonshot AI 发布 PerceptionBench：多模态模型视觉感知能力诊断基准
Moonshot AI 发布 PerceptionBench，一个从 40 多个现有基准中模型实际失败案例归纳出的视觉感知基准，包含 10 项原子感知能力和 3000 道验证题。所有测试模型准确率均未超过 60%，且大量正确答案在重复提问时无法复现，表明模型更多是猜测而非真正感知。PerceptionBench 旨在精确诊断多模态 AI 的视觉感知断裂点，推动其实现忠实、一致的视觉理解。
这是一个把视觉感知拆成原子能力的基准，所有模型不及格，而且猜测多于感知的发现很扎心，做多模态的团队应该拿来测一下自家模型。
Introducing PerceptionBench
Evaluating Atomic Visual Perception in Multimodal Large Language Models
Authors Kimi Team
Overview
We are releasing PerceptionBench, a benchmark that isolates visual perception and evaluates it as a set of atomic capabilities—discovered from how today's models fail, not defined in advance. From frontier-model failures across 42 benchmarks, we derive 10 atomic perceptual capabilities and construct 3,000 verified questions, each isolating a single capability and answerable by looking, with no reasoning or external knowledge required.
Across sixteen frontier MLLMs, no model reaches 60% accuracy, and perception-related hallucination is the weakest capability on average. Models with nearly identical overall scores can diverge sharply in what they actually perceive. PerceptionBench is built to expose exactly where perception breaks, and to drive progress toward multimodal AI that sees faithfully and consistently.
The Dataset
Guided by the induced taxonomy, we select the most informative failures from the source benchmarks, decompose them into atomic sub-questions, and author additional questions on supplemented images. The retained and constructed samples together form an in-house pool of 17,000+ verified questions. The released benchmark subsamples 3,000 verified questions from the constructed portion—60% decomposed from attributed model failures and 40% newly authored—with category-level balancing and difficulty stratification to isolate atomic perceptual capabilities from confounding factors. The released benchmark distinguishes itself through three core design principles:
- Failure-Driven Taxonomy: Every category is discovered from real model failures, attributed to the earliest erroneous step across 42 existing benchmarks.
- Ten Atomic Perceptual Categories: Visual Relation, Counting, Attribute, Depth & 3D, Localization, Comparison, Fine-grained Recognition, Context Integration, OCR, and Hallucination.
- Perception, Not Reasoning or Knowledge: Samples are curated, decomposed, and difficulty-balanced so that difficulty stems from perception rather than reasoning or external knowledge.
Each source benchmark captures a narrow slice of perception errors, and these slices overlap only weakly (mean pairwise weighted Jaccard 0.20). No single benchmark—or small group of them—covers perception as a whole, which motivates a capability-centric benchmark that aggregates and rebalances these fragmented views.
Distribution of Tasks per Category
| Statistics | Number |
|---|---|
| Data | |
| Total | 3,000 |
| Atomic perceptual categories | 10 |
| Task Categories | |
| Depth 3D Perception Error | 330 (11.00%) |
| Visual Counting Error | 330 (11.00%) |
| Fine-Grained Recognition Error | 290 (9.67%) |
| Visual Relation Error | 330 (11.00%) |
| Visual Attribute Error | 330 (11.00%) |
| Visual Localization Error | 330 (11.00%) |
| Visual Comparison Error | 279 (9.30%) |
| Context Integration Error | 255 (8.50%) |
| Hallucination | 271 (9.03%) |
| OCR Error | 255 (8.50%) |
Using PerceptionBench to Compare Models
Conclusion
PerceptionBench is a simple but challenging benchmark for evaluating the atomic visual perception of frontier models. It provides a capability-level standard for measuring and diagnosing the visual perception boundaries of multimodal models. We are open-sourcing the PerceptionBench dataset and evaluation code to help the community address the visual perception gap.
- Download the Data: https://github.com/MoonshotAI/PerceptionBench
来源：Moonshot AI：Kimi Blog · kimi.com