🧪 ChemO Dataset

📄 Paper: ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025

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.

📄 Citation

If you use ChemO in your research, please cite our paper:

@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}
}