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--- |
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license: apache-2.0 |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- question-answering |
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- image-text-to-text |
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tags: |
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- chemistry |
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- agent |
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- olympaid |
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- benchmark |
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- llm-evaluation |
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- science |
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- multimodal |
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language: |
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- en |
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--- |
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# 🧪 **ChemO Dataset** |
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[](https://huggingface.co/papers/2511.16205) |
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[](https://arxiv.org/abs/2511.16205) |
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📄 **Paper**: [ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025](https://huggingface.co/papers/2511.16205) |
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# ChemO Version 1.1 |
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Now with CDXML Files! 🎉 |
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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. |
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## 🌟 Key Features |
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- **🏆 Olympic-Level Benchmark** - Challenging problems from IChO 2025 for advanced AI reasoning |
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- **🔬 Multimodal Symbolic Language** - Addresses chemistry's unique combination of text, formulas, and molecular structures |
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- **📊 Two Novel Assessment Methods**: |
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- **AER (Assessment-Equivalent Reformulation)** - Converts visual output requirements (e.g., drawing molecules) into computationally tractable formats |
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- **SVE (Structured Visual Enhancement)** - Diagnostic mechanism to separate visual perception from core chemical reasoning capabilities |
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## 📦 What's Included |
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The current release includes: |
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- ✅ **Original Problems** - Complete problem sets with additional chapter markers for Problems and Solutions sections (no other modifications to the original content) |
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- ✅ **Well-structured JSON Files** - Clean, organized data designed for: |
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- 🤖 **MLLM Benchmarking** - Olympic-level chemistry reasoning evaluation |
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- 🔗 **Multi-Agent System Testing** - Hierarchical agent collaboration assessment |
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- 🎯 **Multimodal Reasoning** - Text, formula, and molecular structure understanding |
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- ✅ **CDXML Files** - Molecular structure files now available in `JSON/cdxml/` |
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## 📋 Dataset Structure |
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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/`). |
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``` |
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JSON/ |
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├── 1.json ~ 9.json # Problem and solution data in structured JSON format |
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├── images/ # All referenced images indexed in JSON files |
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└── cdxml/ # Molecular structure files in CDXML format |
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``` |
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## 📚 Data Source |
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All problems are sourced from **ICHO 2025**: https://www.icho2025.ae/problems |
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## 🚀 State-of-the-Art Results |
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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. |
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## 🤝 Community |
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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. |
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## 📄 Citation |
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If you use ChemO in your research, please cite our paper: |
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```bibtex |
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@article{qiang2025chemlabs, |
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title={ChemLabs on ChemO: A Multi-Agent System for Multimodal Reasoning on IChO 2025}, |
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author={Xu, Qiang and Bai, Shengyuan and Chen, Leqing and Liu, Zijing and Li, Yu}, |
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journal={arXiv preprint arXiv:2511.16205}, |
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year={2025} |
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} |
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``` |
