README.md

---
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- image-text-to-text
tags:
- chemistry
- agent
- olympaid
- benchmark
- llm-evaluation
- science
- multimodal
language:
- en
---
# 🧪 **ChemO Dataset**
[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper-blue)](https://huggingface.co/papers/2511.16205)
[![arXiv](https://img.shields.io/badge/arXiv-2511.16205-b31b1b.svg)](https://arxiv.org/abs/2511.16205)

📄 **Paper**: [ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025](https://huggingface.co/papers/2511.16205)

# ChemO Version 1.1

Now with CDXML Files! 🎉

The ChemO dataset has been officially released after meticulous proofreading and preparation. This benchmark is built from the **International Chemistry Olympiad (IChO) 2025** and represents a new frontier in automated chemical problem-solving.

## 🌟 Key Features

- **🏆 Olympic-Level Benchmark** - Challenging problems from IChO 2025 for advanced AI reasoning
- **🔬 Multimodal Symbolic Language** - Addresses chemistry's unique combination of text, formulas, and molecular structures
- **📊 Two Novel Assessment Methods**:
  - **AER (Assessment-Equivalent Reformulation)** - Converts visual output requirements (e.g., drawing molecules) into computationally tractable formats
  - **SVE (Structured Visual Enhancement)** - Diagnostic mechanism to separate visual perception from core chemical reasoning capabilities

## 📦 What's Included

The current release includes:

- ✅ **Original Problems** - Complete problem sets with additional chapter markers for Problems and Solutions sections (no other modifications to the original content)
- ✅ **Well-structured JSON Files** - Clean, organized data designed for:
  - 🤖 **MLLM Benchmarking** - Olympic-level chemistry reasoning evaluation
  - 🔗 **Multi-Agent System Testing** - Hierarchical agent collaboration assessment
  - 🎯 **Multimodal Reasoning** - Text, formula, and molecular structure understanding
- ✅ **CDXML Files** - Molecular structure files now available in `JSON/cdxml/`

## 📋 Dataset Structure

The ChemO dataset consists of **9 problems** from IChO 2025, with each problem provided as a structured JSON file (1.json ~ 9.json in `JSON/`). 
```
JSON/
├── 1.json ~ 9.json           # Problem and solution data in structured JSON format
├── images/                   # All referenced images indexed in JSON files
└── cdxml/                    # Molecular structure files in CDXML format
```

## 📚 Data Source

All problems are sourced from **ICHO 2025**: https://www.icho2025.ae/problems

## 🚀 State-of-the-Art Results

Our ChemLabs multi-agent system combined with SVE achieves **93.6/100** on ChemO, surpassing the estimated human gold medal threshold and establishing a new benchmark in automated chemical problem-solving.

## 🤝 Community

We appreciate your patience and look forward to your feedback as we continue to improve this resource for the community. Feel free to reach out to us at jerry.sy.bai@gmail.com.

## 📄 Citation

If you use ChemO in your research, please cite our paper:
```bibtex
@article{qiang2025chemlabs,
  title={ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025},
  author={Xu, Qiang and Bai, Shengyuan and Chen, Leqing and Liu, Zijing and Li, Yu},
  journal={arXiv preprint arXiv:2511.16205},
  year={2025}
}
```