C4-Eval

C4-Eval is the evaluation set for C4 Bench, a Chengyu-based benchmark for measuring whether multimodal language models can understand cross-concept creativity. The release contains the original images, the corresponding idiom answers, and the complete task-specific questions used for evaluation.

• 221 base items: 37 human-designed seed figures and 184 bridge-controlled synthetic figures.
• 1,105 evaluation instances: five task formulations for every base item.
• Language: Chinese.
• Code and evaluation tools: github.com/sci-m-wang/C4

Tasks

Task	Evaluation form	Expected response
H0	Image-only idiom identification	One Chinese idiom
H1	Idiom identification with a generic cross-concept hint	One Chinese idiom
H4	Candidate-constrained idiom identification	One idiom from four candidates
E0	Free idiom identification and bridge explanation	Structured JSON
E1	Bridge explanation with the gold idiom provided	Structured JSON

The primary leaderboard score uses exact answer recovery over H0, H1, H4, and E0, for a total of 884 instances. E1 is retained for explanation analysis and is excluded from the primary score.

Load the evaluation set

from datasets import load_dataset

dataset = load_dataset("sci-m-wang/C4-Eval", split="test")
example = dataset[0]

print(example["question"])
print(example["answer"])
print(example["image"])

The image column contains a resolvable Hub URL. image_path gives the corresponding repository-relative path, which is useful when the dataset has been downloaded locally.

Evaluation fields

Field	Description
instance_id	Unique task-level identifier
item_id	Identifier shared by the five formulations of one image
origin	Human seed figure or synthetic bridge-controlled figure
image	URL of the original image on the Hub
image_path	Repository-relative image path
task	One of H0, H1, H4, E0, or E1
task_name	Descriptive task name
question	Complete model-facing prompt for this task instance
answer	Gold Chinese idiom
answer_aliases	Accepted answer aliases, when applicable
candidates	Four candidate idioms for H4; empty otherwise
level	Bridge-construction level for synthetic items
level_mode	Bridge configuration for synthetic items
explanation_reference	JSON-encoded reference bridge annotation

data/items.jsonl additionally provides one row per base item. data/task_templates.json records the five task definitions. The metadata/ directory contains the construction annotations, reviewed bridge networks, and manual scene specifications used to produce the release.

Repository layout

data/eval.jsonl                 # 1,105 model-facing task instances
data/items.jsonl                # 221 base items
data/task_templates.json        # task definitions
images/human/                   # 37 human-designed seed figures
images/synthetic/               # 184 bridge-controlled figures
metadata/                       # construction and provenance metadata
export_report.json              # release counts and schema version

Reproducibility

The public code repository can rebuild the task views, validate image coverage, score normalized exact answers, and export this Hugging Face layout. The published images retain the original files used by the benchmark.

Citation

If you use C4 Bench in your research, please cite the arXiv preprint:

@misc{wang2026mllmsdecodecreativeleap,
      title={Can MLLMs Decode the Creative Leap? Introducing C4 for Cross-Concept Understanding},
      author={Ming Wang and Yuqing Zhang and Tingna Xie and Xiangju Li and Xiaocui Yang and Daling Wang and Shi Feng and Yifei Zhang},
      year={2026},
      eprint={2608.06501},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2608.06501},
}