| | --- |
| | 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** |
| | [](https://huggingface.co/papers/2511.16205) |
| | [](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} |
| | } |
| | ``` |
