Datasets:

CauchyFan
/

UNREAL_Benchmark

dataset_info:
  features:
    - name: id
      dtype: string
    - name: index
      dtype: int32
    - name: qtype
      dtype: string
    - name: domain
      dtype: string
    - name: subdomain
      dtype: string
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: options
      dtype: string
    - name: option_map
      dtype: string
    - name: source_image
      dtype: string
    - name: image
      dtype: image
task_categories:
  - visual-question-answering
language:
  - en
pretty_name: UNREAL Benchmark
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train-*.parquet
tags:
  - visual-reasoning
  - benchmark
  - multimodal
  - genesis

UNREAL Benchmark

Visual reasoning across unfamiliar scenes
600 questions · 5 domains · Embedded images · English

UNREAL Benchmark is an evaluation dataset for visual question answering across mathematics, natural science, perception, physics and engineering, and reasoning and logic. Each example pairs an image with a question and a reference answer.

A look inside

Six examples from the released benchmark. Click any image to view it at full resolution. The gallery uses the original image bytes, with no cropping or retouching.

Physics & Engineering Reasoning about local gravity _{PHYS_0001}	Natural Science Anatomy and locomotion _{NATS_0001}
Mathematics Geometry in a visual scene _{MATH_0001}	Perception Spatial orientation and shadows _{PERC_0001}
Reasoning & Logic Recognizing anachronisms _{REAS_0001}	Reasoning & Logic Interpreting illumination _{REAS_0004}

Dataset at a glance

Domain	Examples
Mathematics	259
Natural Science	62
Perception	91
Physics & Engineering	96
Reasoning & Logic	92
Total	600

Question formats: 458 open-ended (oe) and 142 multiple-choice (mc).

The release contains three Parquet shards of 200 examples each. Images are embedded in the image column. The existing split name is train; the content is intended for evaluation.

Load the benchmark

from datasets import load_dataset

dataset = load_dataset("CauchyFan/UNREAL_Benchmark", split="train")
example = dataset[0]
print(example["id"], example["question"])
image = example["image"]  # PIL image

To iterate without downloading the entire release first:

stream = load_dataset(
    "CauchyFan/UNREAL_Benchmark", split="train", streaming=True
)
example = next(iter(stream))

Fields

Field	Description
`id`, `index`	Sample identifier and numeric index.
`domain`, `subdomain`	Topic labels.
`qtype`	`oe` for open-ended or `mc` for multiple-choice.
`question`, `answer`	Question text and reference answer.
`options`	JSON-encoded option mapping when present; parse with `json.loads`.
`option_map`	Human-readable labeled options when present.
`source_image`	Original image filename.
`image`	Embedded image decoded by Hugging Face Datasets.

For multiple-choice evaluation, present both the question and its labeled options. Keep reference answers separate from model inputs.

Genesis model family

Public checkpoints: Genesis-Qwen3vl-4B · Genesis-Qwen3vl-8B · Genesis-Qwen3vl-32B