@inproceedings{huang-etal-2026-sema,
    title = "{SEMA}-{RAG}: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning",
    author = "Huang, Yongfeng  and
      Chen, Ruiying  and
      Cheng, James",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.917/",
    pages = "18423--18442",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-Augmented Generation (RAG) is widely employed to mitigate risks such as hallucinations and knowledge obsolescence in medical question answering, yet its predominantly single-round, static retrieval paradigm misaligns with the multi-stage process of clinical reasoning. This compressed workflow induces two structural deficiencies: question-to-query translation often lacks clinically grounded semantic interpretation, and retrieval lacks iterative sufficiency feedback, making it difficult to form reliable evidence chains. We argue that both issues stem from a deeper cause{---}overloading a single reasoning chain with heterogeneous tasks of interpretation, exploration, and adjudication{---}and that the remedy is to reconstruct the workflow via task decoupling and dynamic multi-round exploration. To this end, we propose the Self-Evolving Multi-Agent framework **SEMA-RAG**, which assigns these roles to three specialist agents: **Interpreter Agent** for clinical schema interpretation, **Explorer Agent** for sufficiency-driven self-evolving retrieval, and **Arbiter Agent** for evidence adjudication and answer selection. Across five benchmarks and five LLM backbones, SEMA-RAG improves the strongest baseline by **+6.46** accuracy points on average, measured per backbone."
}