Humanity's Sixth Sense：多模态模型直觉视觉推理基准测试
Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models
研究者推出 Humanity's Sixth Sense（HSS）基准，用于评测多模态大模型（MLLMs）的直觉视觉推理能力，涵盖图像与视频输入，按时间、空间、社交和抽象结构分类。人类参与者准确率达 93.1%，而最强模型 GPT-6-astra 即使开启最大推理努力也仅达 53.6%。引入动态视觉操作的智能体设置可缩小差距但未能弥合。
Authors:Xingang Guo, Jing Gu, Brian Jang, Renxiong Wang, Utkarsh Tyagi, Daniel Quigley, Steven Li, David Yan, Daniel Yue Zhang, Darvin Yi, Forrest Huang, HiJae Kim, Tianyi Zhang, Jared Lichtarge, Jihua Huang, Le Xue, Manan Tomar, Qiuyi Richard Zhang, Ruofei Yu, Seth Neel, Yaning Hu, Marcella Valentine, Xinzhe Jiang, Daniel Evans, Chenguang Wang, Dustin Tran, Tong Zhao, Yinfei Yang, Yunzhong He
Abstract:Humans perceive far more in a scene than what is explicitly depicted: a single glance captures past causes and future trajectories; a quick peek determines if a vehicle can fit between two parked cars; a few seconds of video reveals who holds authority in a room; and a fleeting clip highlights subtle abstract patterns like unwritten rules or hidden labels. This capacity reflects a form of humanity's sixth sense: an intuitive reasoning mechanism that recovers implicit information beyond raw sensory perception. Crucially, this rapid, zero-shot visual intuition underpins everyday navigation and social interaction, making it a vital capability for Multimodal Large Language Models (MLLMs) deployed alongside people. Existing visual benchmarks, however, target either deliberate expert-level analysis in academic and mathematical domains or low-level perception, leaving the intuitive reasoning that people perform largely untested. To bridge this gap, we introduce Humanity's Sixth Sense (HSS), a benchmark for intuitive visual reasoning. HSS spans diverse image and video inputs, organizes items under a structured taxonomy, and pairs each with human-written prompts probing the implicit temporal, spatial, social, and abstract structure that people infer at a glance. Frontier MLLMs fall short of human performance: participants reach 93.1% accuracy, while the strongest model, GPT-6-astra, reaches only 53.6% even at maximum reasoning effort. Despite excelling in many complex tasks that require advanced perception and knowledge, current models still struggle significantly on these visual tasks that are intuitive for humans. We further explore agentic setup that apply dynamic visual manipulation to HSS, which narrows but does not close the gap. HSS establishes intuitive visual reasoning as a measurable axis and directs attention to a capability that scaling on current benchmarks has so far left behind.
| Subjects: | Artificial Intelligence (cs.AI); Multimedia (cs.MM) |
| Cite as: | arXiv:2610.08966 [cs.AI] |
| (or arXiv:2610.08966v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08966 arXiv-issued DOI via DataCite |
Submission history
From: Xingang Guo [view email]
[v1]
Tue, 6 Oct 2026 18:31:35 UTC (3,547 KB)
[v2]
Thu, 8 Oct 2026 17:39:44 UTC (9,308 KB)
来源：arXiv cs.AI · arxiv.org