Datasets:

anonymous-submission-RIG-bench
/

RIG-Bench

license: cc-by-nc-4.0
task_categories:
  - image-to-image
language:
  - en
tags:
  - benchmark
  - reasoning-driven-image-generation
pretty_name: RIG-Bench
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path: samples.jsonl

RIG-bench

Anonymous submission to the NeurIPS 2026 Evaluations & Datasets (E&D) Track.

A benchmark for reasoning-driven image generation: given visual context (images + instruction + optional demonstration pairs), the model must produce the answer as a single image.

2,000 samples
4 task families × 11 subtasks
~1.4 GB

Files

RIG-bench/
├── README.md
├── samples.jsonl              # 2,000 records
└── images/<sample_id>/
    ├── input_<order>.<ext>
    ├── demo_<j>_input_<k>.<ext>, demo_<j>_output_<k>.<ext>   (some samples)
    └── target.<ext>

Data Format

Each line in samples.jsonl corresponds to one benchmark sample. A sample includes the input context, the expected visual answer, task labels.

Field	Meaning
`input`	input images, and optional example images.
`output`	Target ground truth answer image.
`main_family`	Four cognitively demanding domains.
`subtask`	Eleven fine-grained subtasks

Loading

import json
from pathlib import Path
from PIL import Image
from huggingface_hub import snapshot_download

ROOT = Path(snapshot_download(repo_id="anonymous-submission-RIG-bench/RIG-Bench",repo_type="dataset"))
samples = [json.loads(l) for l in (ROOT / "samples.jsonl").open(encoding="utf-8")]
s = samples[0]
input_images    = [Image.open(ROOT / img["path"]) for img in s["input"]["images"]]
GT_target_image = Image.open(ROOT / s["output"]["target_image"])