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README.md

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+# KAMAC-Medical-MultiAgent Dataset
+
+## Dataset Summary
+
+KAMAC-Medical-MultiAgent is a curated dataset designed to support research on **knowledge-driven adaptive multi-agent collaboration** in medical decision-making. It is constructed to evaluate how large language models (LLMs) and multi-agent systems dynamically coordinate specialized expertise under complex clinical scenarios.
+
+This dataset is developed alongside the **KAMAC (Knowledge-driven Adaptive Multi-Agent Collaboration)** framework and is intended for benchmarking:
+
+* Multi-agent reasoning
+* Dynamic expert recruitment
+* Clinical question answering
+* Medical decision support
+
+The dataset includes structured medical questions, multimodal context (optional), and annotations suitable for simulating **multi-disciplinary team (MDT)** style reasoning.
+
+---
+
+## Supported Tasks
+
+* Multi-agent collaboration
+* Medical question answering (MedQA-style)
+* Clinical reasoning
+* Visual question answering (Prognostic / medical VQA)
+* Tool-augmented LLM evaluation
+* Adaptive agent planning
+
+---
+
+## Dataset Creation
+
+### Source Data
+
+The model is also tested under more datasets:
+
+* Public medical QA benchmarks (e.g., MedQA)
+* HEADNECK VQA datasets (e.g., Progn-VQA)
+
+---
+
+### Annotation Process
+
+Annotations include:
+
+* Ground-truth answers
+* Medical specialty tags
+
+---
+
+### Motivation
+
+Traditional multi-agent systems rely on **predefined expert roles**, which limits scalability and adaptability in complex domains such as medicine.
+
+This dataset is designed to evaluate:
+
+* Whether systems can **identify knowledge gaps**
+* Whether they can **dynamically recruit appropriate expertise**
+* Whether collaboration improves decision accuracy
+
+---
+
+## Evaluation
+
+Typical evaluation metrics include:
+
+* Accuracy
+* Multi-agent improvement over single-agent baseline
+* Reasoning quality (if traces are available)
+* Efficiency (number of agents invoked)
+
+---
+
+## Limitations
+
+* May inherit biases from source medical datasets
+* Limited coverage of rare diseases
+* Multimodal data availability may vary
+* Not a substitute for professional medical advice
+
+---
+
+## Ethical Considerations
+
+This dataset is intended **for research purposes only**.
+
+* Not for clinical deployment
+* Outputs should not be used for real medical decisions
+* Researchers should evaluate fairness and bias
+
+---
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@misc{kamac2025,
+  title={KAMAC: Knowledge-driven Adaptive Multi-Agent Collaboration for Medical Decision Making},
+  author={Wu, Xiao and Huang, Ting-Zhu and Deng, Liang-Jian and Qiao, Yanyuan and Razzak, Imran and Xie, Yutong},
+  year={2025},
+  note={Dataset and code available at https://github.com/XiaoXiao-Woo/KAMAC}
+}
+
+@inproceedings{wu-etal-2025-knowledge,
+    title = "A Knowledge-driven Adaptive Collaboration of {LLM}s for Enhancing Medical Decision-making",
+    author={Wu, Xiao and Huang, Ting-Zhu and Deng, Liang-Jian and Qiao, Yanyuan and Razzak, Imran and Xie, Yutong},
+    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
+    year = "2025",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.emnlp-main.1699/",
+    doi = "10.18653/v1/2025.emnlp-main.1699",
+    pages = "33495--33512",
+    ISBN = "979-8-89176-332-6",
+}
+
+```
+
+---
+
+## Acknowledgements
+
+This dataset is developed as part of research conducted on the **HANCOCK / NHR@FAU high-performance computing ecosystem**, which provides large-scale GPU infrastructure for AI and scientific computing.
+
+---
+
+## License
+
+Specify your license here (e.g., MIT, CC BY 4.0, etc.)
+
+---
+
+## Contact
+
+For questions, please open an issue on the GitHub repository:
+
+https://github.com/XiaoXiao-Woo/KAMAC