QuarkMedSearch: A Long-Horizon Deep Search Agent for Exploring Medical Intelligence

2026-04-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

QuarkMedSearch is a new long-horizon deep search agent designed for the Chinese medical domain, built upon the Tongyi DeepResearch foundation model. It addresses the scarcity of medical deep search training data by combining a large-scale medical knowledge graph with real-time online exploration to synthesize long-horizon data. The model employs a two-stage Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) strategy to enhance its planning, tool invocation, and reflection capabilities for deep search, while maintaining search efficiency. For evaluation, a new benchmark, QuarkMedSearch Benchmark, was constructed with medical expert collaboration and rigorous manual verification. Experimental results indicate QuarkMedSearch achieves state-of-the-art performance among open-source models of comparable scale on its dedicated benchmark and remains competitive on general benchmarks.

Key takeaway

For AI scientists and machine learning engineers developing domain-specific agents, consider adopting a full-pipeline approach that integrates specialized data synthesis, multi-stage training, and expert-validated benchmarks. Your projects can benefit from combining knowledge graphs with real-time exploration for data generation, and using progressive SFT and RL to refine agent capabilities.

Key insights

QuarkMedSearch improves deep search in medical domains via specialized data, training, and evaluation.

Principles

• Combine knowledge graphs with online exploration for data synthesis.
• Use two-stage SFT and RL for progressive capability enhancement.

Method

A full-pipeline approach involving medical multi-hop data construction, a two-stage SFT and RL training strategy, and expert-verified evaluation benchmarks.

In practice

• Synthesize deep search data using knowledge graphs.
• Implement two-stage SFT/RL for agentic model training.
• Collaborate with experts for benchmark construction.

Topics

• QuarkMedSearch
• Deep Search Agents
• Medical Intelligence
• Agentic Foundation Models
• Medical Knowledge Graph

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.