As agentic foundation models continue to evolve, how to further improve their performance in vertical domains has become an important challenge. To this end, building upon Tongyi DeepResearch, a powerful agentic foundation model, we focus on the Chinese medical deep search scenario and propose QuarkMedSearch, systematically exploring a full-pipeline approach spanning medical multi-hop data construction, training strategies, and evaluation benchmarks to further push and assess its performance upper bound in vertical domains. Specifically, for data synthesis, to address the scarcity of deep search training data in the medical domain, we combine a large-scale medical knowledge graph with real-time online exploration to construct long-horizon medical deep search training data; for post-training, we adopt a two-stage SFT and RL training strategy that progressively enhances the model's planning, tool invocation, and reflection capabilities required for deep search, while maintaining search efficiency; for evaluation, we collaborate with medical experts to construct the QuarkMedSearch Benchmark through rigorous manual verification. Experimental results demonstrate that QuarkMedSearch achieves state-of-the-art performance among open-source models of comparable scale on the QuarkMedSearch Benchmark, while also maintaining strong competitiveness on general benchmarks.
Figures & tables
Figure 1: Medical Deep Search Task Synthesis Pipeline.
Figure 2: Long-Horizon Deep Search QA Augmentation in Online Environments.
Figure 3: Comparison of the distribution of tool-call counts between QuarkMedSearch and BrowseComp.
Figure 4: Post-training stages for QuarkMedSearch.
Dimension
Scoring Criteria
Intent Planning
Accuracy of problem understanding and solution path planning; requires clear decomposition of complex problems.
Tool Invocation
Parameter accuracy, query construction quality, and fault tolerance; requires precise and efficient invocation without redundancy.
Information Integration
Extraction of key information, strategy adjustment upon failed retrieval, and (for medical QA) terminology standardization, source authority and timeliness.
Factual Rigor
Hard constraint: any factual error results in a score of 0.
Table 1: Multi-Dimensional Rubrics for Trajectory Evaluation
Figure 5: Distribution of Sample Categories in the QuarkMedSearch Benchmark.
Category
Key Entity Coverage
Biomedical Fundamentals
Anatomical structures, biochemical molecules, pathophysiological mechanisms, pathogenic microorganisms, etc.
Drugs and Medical Products
Chemical drugs, active ingredients of herbal medicine, biologics, vaccines, medical devices, etc.
Medical Research and Knowledge
Clinical trial findings, evidence-based medicine, medical statistics, diagnostic and treatment technical standards, etc.
Diseases and Clinical Manifestations
Disease diagnosis and classification, clinical syndromes, symptoms and signs, key differential diagnosis points, etc.
Clinical Procedures
Clinical laboratory tests, surgical procedures, treatment protocols, medication strategies and interventions, etc.
Medical Institutions
Hospitals, pharmaceutical companies, medical device companies, medical academic associations, health regulatory agencies, etc.
Table 2: Category Taxonomy of the QuarkMedSearch Benchmark
Backbone
Size
QuarkMedSearch Benchmark
Large-scale Models
Seed1.8
-
55.71
Kimi-K2.5
1T-A32B
56.74
GLM-5
744B-A40B
58.57
DeepSeek-V3.2
671B-A37B
57.14
Qwen3.5-Plus
397B-A17B
51.42
Table 3: Results on the QuarkMedSearch Benchmark.
Backbone
Size
BrowseComp-EN
BrowseComp-ZH
Xbench DeepSearch
Commercial Closed-source Models
Seed1.8
-
67.6
81.3
-
Gemini-3-Pro
-
59.2
66.8
-
Claude Opus 4.5
-
57.8
62.3
-
GPT-5.2 (xhigh)
-
65.8
76.1
-
Open-source Large-scale Models (w/o ctm)
Table 4: Evaluation results on general deep search benchmarks. For commercial closed-source models, we directly adopt the best results from official reports. For open-source models, we report only the results without context management (w/o ctm) to ensure fair comparison, and include only those models that explicitly provide such results in the comparison among models of similar parameter scale. † denotes results obtained under our unified evaluation environment. Bold indicates the best result among models of similar parameter scale.
Stage
QuarkMedSearch Benchmark
BrowseComp-EN
BrowseComp-ZH
Base(Tongyi DeepResearch)
40.71
42.67
45.40
+ 32K Stage I
49.99
43.23
49.40
+ 128K Stage II
52.21
45.47
55.36
Table 5: Ablation study results of the two-stage SFT training strategy.
Figure 6: RL training dynamics: response length and reward over training steps.
Metric
BrowseComp-ZH
BrowseComp-EN
w/o RL
w/ RL
w/o RL
w/ RL
Termination Rate ↑
0.676
0.725
0.580
0.635
Avg. Tool Calls (Correct) ↓
28.09
24.01
40.03
37.12
Table 6: Comparison of termination rate and average number of tool call rounds required to correctly answer questions before and after RL training. w/o RL and w/ RL correspond to the model states at the end of two-stage SFT and after RL convergence, respectively. ↑ means higher is better, ↓ means lower is better.
Figure 7: Training dynamics of correct score and format score under two reward strategies: correct score + format score (blue) and strict correct score (red).
Method
BrowseComp-EN
BrowseComp-ZH
Large-scale Open-source Models (w/ ctm)
Kimi-K2.5
74.9
-
GLM-5
75.9
72.7
Qwen3.5-Plus
78.6
70.3
DeepSeek-V3.2
67.6
65.0
Open-source Models of Comparable Scale (w/ ctm)
Table 7: Results with ctm on BrowseComp.
Figure 8: Win/Tie/Lose comparison between the fused model and the non-fused baseline, as evaluated by professional medical experts on medical Deep Research report generation.