mlsanityData to 7 Oct 2026

Baichuan-M1: Pushing the Medical Capability of Large Language Models

arXiv 2502.12671 ↗First posted 18 Feb 2025cs.CLcited by 41

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Cited by 22 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like medicine, remain relatively scarce. In particular, the development of highly efficient and practical LLMs for the medical domain is challenging due to the complexity of medical knowledge and the limited availability of high-quality data. To bridge this gap, we introduce Baichuan-M1, a series of large language models specifically optimized for medical applications. Unlike traditional approaches that simply continue pretraining on existing models or apply post-training to a general base model, Baichuan-M1 is trained from scratch with a dedicated focus on enhancing medical capabilities. Our model is trained on 20 trillion tokens and incorporates a range of effective training methods that strike a balance between general capabilities and medical expertise. As a result, Baichuan-M1 not only performs strongly across general domains such as mathematics and coding but also excels in specialized medical fields. We have open-sourced Baichuan-M1-14B, a mini version of our model, which can be accessed through the following links.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 59 of its 74 references that are in the map. First posted on arXiv 18 Feb 2025.

Builds on

59 of 74 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 29; 2026: 12.

41 papers.

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  1. 2025A Survey of Reinforcement Learning for Large Reasoning Modelscited by 175
  2. 2025From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discoverycited by 74
  3. 2025Baichuan-M2: Scaling Medical Capability with Large Verifier Systemcited by 55
  4. 2025An Agentic System for Rare Disease Diagnosis with Traceable Reasoningcited by 50
  5. 2025A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontierscited by 34
  6. 2025Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Casescited by 28
  7. 2025Jenga: Effective Memory Management for Serving LLM with Heterogeneitycited by 24
  8. 2025MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Sciencecited by 22
  9. 2025Automating Expert-Level Medical Reasoning Evaluation of Large Language Modelscited by 21
  10. 2025DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Modelscited by 19
  11. 2025EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysiscited by 17
  12. 2025Citrus: Leveraging Expert Cognitive Pathways in a Medical Language Model for Advanced Medical Decision Supportcited by 16
29 more
  1. 2025Chiron-o1: Igniting Multimodal Large Language Models towards Generalizable Medical Reasoning via Mentor-Intern Collaborative Searchcited by 16
  2. 2025L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Modelscited by 14
  3. 2025Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoningcited by 14
  4. 2025BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Textcited by 11
  5. 2025Surrogate Signals from Format and Length: Reinforcement Learning for Solving Mathematical Problems without Ground Truth Answerscited by 11
  6. 2025End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoningcited by 10
  7. 2026Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Makingcited by 10
  8. 2025The Multi-Round Diagnostic RAG Framework for Emulating Clinical Reasoningcited by 8
  9. 2025Evolving Interactive Diagnostic Agents in a Virtual Clinical Environmentcited by 7
  10. 2025MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicinecited by 4
  11. 2026Medical Reasoning with Large Language Models: A Survey and MR-Benchcited by 4
  12. 2025Grounded in Reality: Learning and Deploying Proactive LLM from Offline Logscited by 3
  13. 2025Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Makingcited by 2
  14. 2025CX-Mind: A Pioneering Multimodal Large Language Model for Interleaved Reasoning in Chest X-ray via Curriculum-Guided Reinforcement Learningcited by 2
  15. 2025Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigmcited by 2
  16. 2026MedAction: Towards Active Multi-turn Clinical Diagnostic LLMscited by 2
  17. 2025Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyondcited by 1
  18. 2025MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicinecited by 1
  19. 2025FEAT: A Multi-Agent Forensic AI System with Domain-Adapted Large Language Model for Automated Cause-of-Death Analysiscited by 1
  20. 2026LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuningcited by 1
  21. 2026ReMedi: Reasoner for Medical Clinical Predictioncited by 1
  22. 2026Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Supportcited by 1
  23. 2026Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Carecited by 1
  24. 2026Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoningcited by 1
  25. 2025MedCalc-Eval and MedCalc-Env: Advancing Medical Calculation Capabilities of Large Language Modelscited by 0
  26. 2026DoAtlas-1: A Causal Compilation Paradigm for Clinical AIcited by 0
  27. 2026RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotationcited by 0
  28. 2026Teaching LLMs How ICU Physicians Approach Clinical Reasoning Through OMOP-Aligned Retrieval Improves Reasoning Across Clinical Domainscited by 0
  29. 2026CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicinecited by 0