mlsanityData to 14 Oct 2026

QuarkMed Medical Foundation Model Technical Report

arXiv 2508.11894 ↗First posted 16 Aug 2025cs.AIcited by 5

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Abstract (arXiv)

Recent advancements in large language models have significantly accelerated their adoption in healthcare applications, including AI-powered medical consultations, diagnostic report assistance, and medical search tools. However, medical tasks often demand highly specialized knowledge, professional accuracy, and customization capabilities, necessitating a robust and reliable foundation model. QuarkMed addresses these needs by leveraging curated medical data processing, medical-content Retrieval-Augmented Generation (RAG), and a large-scale, verifiable reinforcement learning pipeline to develop a high-performance medical foundation model. The model achieved 70% accuracy on the Chinese Medical Licensing Examination, demonstrating strong generalization across diverse medical benchmarks. QuarkMed offers a powerful yet versatile personal medical AI solution, already serving over millions of users at ai.quark.cn.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 46 references that are in the map. First posted on arXiv 16 Aug 2025.

Builds on

35 of 46 references.

References · 35

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23 more
  1. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialoguecited by 8
  2. 2026MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMscited by 6
  3. 2026Medical Reasoning with Large Language Models: A Survey and MR-Benchcited by 4
  4. 2026Quark Medical Alignment: A Holistic Multi-Dimensional Alignment and Collaborative Optimization Paradigmcited by 2
  5. 2026QuarkMedBench: A Real-World Scenario Driven Benchmark for Evaluating Large Language Modelscited by 1

Similar, not cited

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  2. 2024Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models
  3. 2025Retrieval Augmented Generation Evaluation for Health Documents
  4. 2025Accurate and Energy Efficient: Local Retrieval-Augmented Generation Models Outperform Commercial Large Language Models in Medical Tasks
  5. 2024oRetrieval Augmented Generation for 10 Large Language Models and its Generalizability in Assessing Medical Fitness
  6. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
  7. 2025Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond
  8. 2025Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription