QuarkMedSearch: A Long-Horizon Deep Search Agent for Exploring Medical Intelligence
Summary: arXiv:2604.12867v1 Announce Type: new
Abstract
As agentic foundation models continue to evolve, the quest to enhance their performance in specialized vertical domains has become a critical challenge. Building upon Tongyi DeepResearch, an advanced agentic foundation model, we introduce QuarkMedSearch. This model systematically explores a comprehensive pipeline approach that encompasses medical multi-hop data construction, innovative training strategies, and thorough evaluation benchmarks aimed at pushing and assessing its performance upper bound within vertical domains.
Introduction
With the rapid advancements in artificial intelligence, particularly in the realm of medical intelligence, the need for robust search agents that can navigate complex data landscapes is paramount. QuarkMedSearch addresses this need by focusing on the Chinese medical deep search scenario, which presents unique challenges due to the intricacies of medical data.
Key Features of QuarkMedSearch
- Data Synthesis: To tackle the scarcity of training data specifically for deep search in the medical domain, QuarkMedSearch synthesizes long-horizon training data by integrating a large-scale medical knowledge graph with real-time online exploration.
- Post-Training Strategy: The model employs a two-stage Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) training strategy. This approach progressively enhances the model’s capabilities in planning, tool invocation, and reflection—key aspects necessary for efficient deep search—while ensuring search efficiency is maintained.
- Evaluation Benchmarks: In collaboration with medical experts, we have created the QuarkMedSearch Benchmark. This benchmark undergoes rigorous manual verification to ensure its reliability and relevance in evaluating the model’s performance.
Experimental Results
Comprehensive experiments demonstrate that QuarkMedSearch achieves state-of-the-art performance when compared to other open-source models of similar scale on the QuarkMedSearch Benchmark. Furthermore, it maintains strong competitiveness on general benchmarks, indicating its versatility and robustness across various applications.
Conclusion
QuarkMedSearch represents a significant advancement in the field of medical intelligence, offering a powerful tool for deep search applications. By addressing critical challenges such as data scarcity and model efficiency, this innovative approach not only enhances the capabilities of agentic foundation models but also sets a new standard for performance in the medical domain. As we continue to refine QuarkMedSearch, we anticipate its potential to revolutionize medical search applications and improve outcomes in healthcare.
Future Work
Looking forward, we aim to explore additional strategies to further enhance the model’s capabilities, including but not limited to:
- Expanding the medical knowledge graph to include more diverse datasets.
- Integrating user feedback for continuous learning and improvement.
- Enhancing collaboration with healthcare professionals for more targeted applications.
In conclusion, as the landscape of medical intelligence evolves, QuarkMedSearch stands as a beacon of innovation, paving the way for more intelligent, efficient, and effective medical search solutions.