Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models
Researchers introduce HSS, where humans score 93.1% and GPT-6-astra only 53.6% on intuitive visual reasoning.
The paper presents Humanity's Sixth Sense (HSS), a benchmark of intuitive visual reasoning over images and video. Items use human-written prompts covering implicit temporal, spatial, social, and abstract structure. Human participants reach 93.1% accuracy, while the strongest model, GPT-6-astra, scores 53.6% even at maximum reasoning effort. Agentic setups that dynamically manipulate visuals narrow the gap but do not close it.
- HSS tests zero-shot intuitive visual reasoning, not expert analysis or low-level perception.
- Humans score 93.1%; GPT-6-astra reaches 53.6% at maximum reasoning effort.
- Prompts cover temporal, spatial, social, and abstract inferences from images and video.
- Agentic visual manipulation narrows the human-model gap but does not close it.
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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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2610.08966