pretty_name: Humanity's Sixth Sense (HSS)
language:
- en
license: mit
task_categories:
- visual-question-answering
- video-text-to-text
tags:
- multimodal
- visual-reasoning
- video
- benchmark
- rubric-graded
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: test
path: data/test.jsonl
Humanity's Sixth Sense (HSS)
HSS is a benchmark for intuitive visual reasoning: the implicit temporal, spatial, social, and abstract structure that people infer from an image or a short clip at a glance. Each task pairs one image or video with a human-written question, a reference answer, and a rubric of independently checkable criteria.
People reach 93.1% on HSS. The strongest of the 24 models we evaluated, GPT-6-astra at maximum reasoning effort, reaches 53.6%.
At a glance
| Tasks | 522 (288 image, 234 video) |
| Rubric criteria | 723 (1–7 per task) |
| Domains / subdomains | 4 / 11 |
| Split | test only |
Every task was written by a trained annotator. It was admitted only after three independent review rounds approved it unanimously. Of 3,466 authored tasks, 522 survived (15.1%).
| Domain | Subdomains | Tasks |
|---|---|---|
| Physical & Spatial Logic | Hidden & Invisible · Spatial "Alien Viewpoint" · Affordance & Feasibility · Spatial Reachability | 176 |
| Temporal & Causal Dynamics | Retrodiction · Mechanistic Causality · Extrapolation | 134 |
| Social Understanding | Social Role, Norm & Power Dynamics · Theory of Mind | 110 |
| Abstract & Contextual Inference | Change & Consequence · Patterns & Pareidolia | 102 |
Fields
| Field | Type | Description |
|---|---|---|
task_id |
string | Unique task id |
domain |
string | One of the 4 domains |
subdomain |
string | One of the 11 subdomains |
media_type |
string | image or video |
media_path |
string | Path of the media file inside this repo, e.g. media/images/<task_id>.png |
prompt |
string | The question shown to the model |
golden_response |
string | Human-written reference answer |
rubric_criteria |
list of {id, title} |
Criteria a correct answer must satisfy |
num_criteria |
int | len(rubric_criteria) |
evidence_start, evidence_end |
string or null | Video tasks: the part of the clip that holds the evidence for the answer, as m:ss timestamps. Null for image tasks |
evidence_start_sec, evidence_end_sec |
float or null | The same window in seconds |
Models are evaluated on the whole video; the evidence window is metadata for analysis (for example, how much of a clip a model needs to see), not part of the input.
Loading
from datasets import load_dataset
from huggingface_hub import snapshot_download
repo = "ScaleAI/HSS"
ds = load_dataset(repo, split="test")
root = snapshot_download(repo, repo_type="dataset") # media files
task = ds[0]
media_file = f"{root}/{task['media_path']}"
Scoring
An LLM judge reads the question, the reference answer, and the model's answer, then marks each rubric criterion as met or unmet. Because only essential criteria survived curation, a task counts as correct only if every one of its criteria is met. The headline metric is accuracy over the 522 tasks. In the paper the judge is Claude Opus 5.
Updates
- 2026-10-08: added the video evidence window (
evidence_start,evidence_endand their_secversions) for all 234 video tasks. Revised the prompt of task6a7f82ad5cfeb63114fa869b(a minor issue found during our internal audit).
Notes and limitations
- Contamination. The media comes from the public web and may appear in pretraining data. The questions were written for this benchmark and are new.
- Judge dependence. Scores go through an LLM judge. The paper reports agreement across judges and a human-oracle check.
- This is a test set. Please do not train on it.
License and media takedown
The annotations in this dataset (prompts, reference answers, rubrics, and taxonomy labels) are released under the MIT License.
The images and videos under media/ were collected from publicly available
sources. They remain the property of their original creators and rights
holders, and the MIT License does not apply to them. They are included only so
that the benchmark can be used for non-commercial research and evaluation.
Users are responsible for complying with any terms that apply to the original
media.
If you own the rights to any media in this dataset and would like it removed,
please open a discussion on this repository with the task_id or file path.
We will remove it promptly.
Authors
Xingang Guo¹*, Jing Gu²*, Brian Jang¹*, Renxiong Wang¹, Utkarsh Tyagi¹, Daniel Quigley¹, Steven Li¹, David Yan², Daniel Yue Zhang¹, Darvin Yi¹, Forrest Huang², HiJae Kim¹, Tianyi Zhang², Jared Lichtarge², Jihua Huang², Le Xue², Manan Tomar², Qiuyi Richard Zhang², Ruofei Yu², Seth Neel², Yaning Hu², Marcella Valentine², Daniel Evans¹, Chenguang Wang¹·³, Dustin Tran², Tong Zhao¹, Yinfei Yang², Yunzhong He¹
¹Scale AI, ²Elorian, ³University of California, Santa Cruz · *Equal contribution
Citation
@misc{guo2026hss,
title = {Humanity's Sixth Sense: Benchmarking Intuitive Visual Reasoning in Multimodal Models},
author = {Xingang Guo and Jing Gu and Brian Jang and Renxiong Wang and Utkarsh Tyagi and Daniel Quigley and Steven Li and David Yan and Daniel Yue Zhang and Darvin Yi and Forrest Huang and HiJae Kim and Tianyi Zhang and Jared Lichtarge and Jihua Huang and Le Xue and Manan Tomar and Qiuyi Richard Zhang and Ruofei Yu and Seth Neel and Yaning Hu and Marcella Valentine and Daniel Evans and Chenguang Wang and Dustin Tran and Tong Zhao and Yinfei Yang and Yunzhong He},
year = {2026}
}