MMSearch-Plus✨: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents

Official repository for the paper "MMSearch-Plus: Benchmarking Provenance-Aware Search for Multimodal Browsing Agents".

🌟 For more details, please refer to the project page with examples: https://mmsearch-plus.github.io/.

[🌐 Webpage] [📖 Paper] [🤗 Huggingface Dataset] [🏆 Leaderboard]

💥 News

• [2025.09.26] 🔥 We update the arXiv paper and release all MMSearch-Plus data samples in huggingface dataset.
• [2025.08.29] 🚀 We release the arXiv paper.

📌 ToDo

• Agentic rollout framework code
• Evaluation script
• Set-of-Mark annotations

Usage

⚠️ Important: This dataset is encrypted to prevent data contamination. However, decryption is handled transparently by the dataset loader.

Dataset Usage

Load the dataset with automatic decryption using your canary string:

import os
from huggingface_hub import hf_hub_download, snapshot_download
from datasets import load_dataset

# Set the canary string (hint: it's the name of this repo without username)
os.environ['MMSEARCH_PLUS'] = 'your_canary_string'

# Download the entire dataset repository (including all .arrow files)
snapshot_download(
    repo_id="Cie1/MMSearch-Plus",
    repo_type="dataset",
    revision="main"
)

# Download the custom data loader script
script_path = hf_hub_download(
    repo_id="Cie1/MMSearch-Plus",
    filename="mmsearch_plus.py",
    repo_type="dataset",
    revision="main"
)

# Load dataset with transparent decryption using the custom loader
dataset = load_dataset(script_path, trust_remote_code=True)

# Explore the dataset - everything is already decrypted!
print(f"Dataset size: {len(dataset['train'])}")
print(f"Features: {list(dataset['train'].features.keys())}")

# Access a sample
sample = dataset['train'][0]
print(f"Question: {sample['question']}")
print(f"Answer: {sample['answer']}")
print(f"Category: {sample['category']}")
print(f"Number of images: {sample['num_images']}")

# Access images (PIL Image objects)
sample['img_1'].show()  # Display the first image

👀 About MMSearch-Plus

MMSearch-Plus is a challenging benchmark designed to test multimodal browsing agents' ability to perform genuine visual reasoning. Unlike existing benchmarks where many tasks can be solved with text-only approaches, MMSearch-Plus requires models to extract and use fine-grained visual cues through iterative image-text retrieval.

Key Features

🔍 Genuine Multimodal Reasoning: 311 carefully curated tasks that cannot be solved without visual understanding

🎯 Fine-grained Visual Analysis: Questions require extracting spatial cues and temporal traces from images to find out-of-image facts like events, dates, and venues

🛠️ Agent Framework: Model-agnostic web agent with standard browsing tools (text search, image search, zoom-in)

📍 Set-of-Mark (SoM) Module: Enables provenance-aware cropping and targeted searches with human-verified bounding box annotations

Dataset Structure

Each sample contains:

• Quuestion text and images
• Ground truth answers and alternative valid responses
• Metadata including arXiv id (if an event is a paper), video URL (if an event is a video), area and subfield

Performance Results

Evaluation of closed- and open-source MLLMs shows:

• Best accuracy is achieved by o3 with full rollout: 36.0% (indicating significant room for improvement)
• SoM integration provides consistent gains up to +3.9 points
• Models struggle with multi-step visual reasoning and cross-modal information integration

The overview of three paradigms for multimodal browsing tasks that demand fine-grained visual reasoning.

The overview of an example trajectory for a task in MMSearch-Plus.

🏆 Leaderboard

Contributing to the Leaderboard

🚨 The Leaderboard is continuously being updated, welcoming the contribution of your excellent LMMs!

🔖 Citation

If you find MMSearch-Plus useful for your research and applications, please kindly cite using this BibTeX:

@article{tao2025mmsearch,
  title={MMSearch-Plus: A Simple Yet Challenging Benchmark for Multimodal Browsing Agents},
  author={Tao, Xijia and Teng, Yihua and Su, Xinxing and Fu, Xinyu and Wu, Jihao and Tao, Chaofan and Liu, Ziru and Bai, Haoli and Liu, Rui and Kong, Lingpeng},
  journal={arXiv preprint arXiv:2508.21475},
  year={2025}
}