Datasets:

Crysun
/

DailyClue

license: mit
task_categories:
  - visual-question-answering
  - image-to-text
language:
  - en
tags:
  - benchmark
  - multimodal
  - reasoning
  - visual-grounding
  - mllm-evaluation
pretty_name: DailyClue
size_categories:
  - n<1K

DailyClue Dataset

Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios

Dataset Summary

DailyClue is a benchmark for evaluating visual clue-driven reasoning in Multimodal Large Language Models (MLLMs). Unlike benchmarks that test pre-existing knowledge, DailyClue requires models to actively identify decisive visual clues from images before producing answers.

The dataset spans 4 major domains and 16 distinct subtasks, with 666 total questions.

Dataset Structure

DailyClue/
├── daily_life/          # Images for Daily Commonsense Reasoning
├── location/            # Images for Location Identification
├── science/             # Images for Scientific Commonsense
├── spatial/             # Images for Spatial Reasoning
├── daily_life.json
├── location.json
├── science.json
└── spatial.json

Statistics

Category	# Questions	Formats
Daily Commonsense Reasoning	180	Multiple Choice, Yes/No, Open-ended
Location Identification	200	Open-ended
Spatial Reasoning	163	Multiple Choice, Yes/No
Scientific Commonsense	123	Multiple Choice, Yes/No, Open-ended
Total	666

Data Fields

Each JSON entry contains:

Field	Type	Description
`image`	`list[str]`	Image filename(s) within the category subfolder
`question`	`str`	The question posed to the model
`clues`	`str`	Human-annotated ground-truth visual clues (see note below)
`ground_truth`	`str`	The correct answer
`format`	`str`	`"Multiple choice"`, `"Yes or no"`, or `"Open-ended"`
`category_1`	`str`	Primary domain (one of the four above)
`category_2`	`str`	Subtask within the primary domain
`language`	`str`	`"English"`

Note on clues: This field contains human-annotated ground-truth visual clues. It is used in ablation experiments (injecting GT clues to probe the impact on model accuracy) and in the Rigorous Evaluation Protocol (checking whether model-predicted clues semantically align with GT clues). It is not fed to the model during standard inference.

Usage

Download

# via git
git clone https://huggingface.co/datasets/Crysun/DailyClue

# via huggingface_hub
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Crysun/DailyClue", repo_type="dataset", local_dir="./dataset")

Run Inference

After downloading, point the inference script to the local directory:

python infer/inference.py \
  --dataset ./dataset \
  --model_names "gpt-4o" \
  --prompt_mode "b"

See the GitHub repository for the full evaluation pipeline.

Citation

@article{dailyclue2026,
  title={Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios},
  author={Li, Xiaomin and Wang, Tala and Zhong, Zichen and Zhang, Ying and Zheng, Zirui and Isobe, Takashi and Li, Dezhuang and Lu, Huchuan and He, You and Jia, Xu},
  journal={arXiv preprint arXiv:2604.14041},
  year={2026}
}