CWBench

A dual-world VQA benchmark and training set for cross-world aligned distillation (CWAD) of vision-language models.

Each item is a pair: world A is an original image, world B is a counterfactual edit of that same image that flips the answer to the question. A model is scored on both members; a pair counts as correct only when both worlds are answered correctly (cross-world pair accuracy, CWPA).

Paper and Code

• 📄 Paper: Distill the Visual Evidence, Not Just the Answer: Cross-World On-Policy Distillation for Vision-Language Models
• 💻 Code: https://github.com/baokou-fw2/CWAD

@misc{sun2026distillvisualevidencejust,
      title={Distill the Visual Evidence, Not Just the Answer: Cross-World On-Policy Distillation for Vision-Language Models},
      author={Yuanhao Sun and Huawei Ji and Jiaxin Ding and Luoyi Fu and Xinbing Wang},
      year={2026},
      eprint={2609.38777},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2609.38777},
}

Files

train.jsonl    2365 rows   training set (dual-world)
test.jsonl     1188 rows   test set = 594 pairs × {A, B}
train/         4730 images referenced by train.jsonl
test/          1188 images referenced by test.jsonl

Image paths inside the jsonl are relative to this directory, so the tree is self-contained.

Schemas

train.jsonl — one object per training row

field	meaning
prompt	chat messages, <image> marks where the image goes
images	world A — the original image at teacher resolution, matching test.jsonl member A
teacher_images	world B — the counterfactual edit at 1024×1024
reward_model.ground_truth	letter answer for the row
ability, data_source, extra_info	provenance metadata carried over from the source release

test.jsonl — one object per member (two rows per pair, same pair_id)

field	meaning
pair_id	join key; A and B rows share it
member	"A" (original) or "B" (edited)
images	single-element list with the image path
query	the question, with options
response	ground-truth letter
category	edit category tag
world_a_resolution	resolution mode used for world A

Loading

import json

rows = [json.loads(l) for l in open("train.jsonl", encoding="utf-8")]
row = rows[0]
print(row["prompt"][0]["content"])
print(row["images"][0]["image"], row["teacher_images"][0]["image"])

# score a submission pair-wise
test = [json.loads(l) for l in open("test.jsonl", encoding="utf-8")]
pairs = {}
for e in test:
    pairs.setdefault(e["pair_id"], {})[e["member"]] = e

test.jsonl is not in a format datasets.load_dataset can auto-convert (the images are plain files, not embedded bytes). Read the jsonl and open the images yourself.

Provenance and licence

This release is derived from RP-OPSD Dataset4.0, whose questions originate from public VQA sources (e.g. A-OKVQA). The images are redistributed as part of that derived release. Check that the upstream licences permit redistribution before publishing or reusing this dataset. The license field above is set to other because the upstream terms, not an SPDX identifier, govern it.

Splits

train.jsonl and test.jsonl are disjoint:

• 0 image files in common (checked across all four A/B combinations)
• 0 question strings in common

The extra_info.dataset4_source_index in train.jsonl and source_idx in test.jsonl come from different index namespaces and must not be joined against each other.