AI能考过博士题，却看不懂你一眼就懂的画面
人类扫一眼街景，就能判断两辆车之间挤不挤得过去、谁在房间里说了算、有什么潜规则没写出来。这种不假思索的直觉，是我们在真实世界生存的基础。但最新基准测试显示：人类在这类任务上准确率93.1%，最强AI模型拼尽全力也只有53.6%。AI在数学、学术等需要深思熟虑的领域已经很强，却在人类婴儿时期就具备的视觉直觉上大幅落后。研究者把这种能力命名为“第六感”，并建立了专门测试它的基准HSS，覆盖时间、空间、社交、抽象四类隐含信息。即使让AI能主动操作画面来辅助判断，差距缩小了，但依然没追上。这不是你明天能用上的技术，但它指出了一个关键方向：AI的智能增长一直沿着“考试”路线走，而人类真正的智能，更多来自那些不需要思考的瞬间。
📄 原文摘要(英文)
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.