@inproceedings{chen-etal-2026-medverse,
    title = "{M}ed{V}erse: Efficient and Reliable Medical Reasoning via {DAG}-Structured Parallel Execution",
    author = "Chen, Jianwen  and
      Yang, Xinyu  and
      Xia, Peng  and
      Azarang, Arian  and
      Lee, Yueh Z  and
      Li, Gang  and
      Zhu, Hongtu  and
      Li, Yun  and
      Chen, Beidi  and
      Yao, Huaxiu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.699/",
    pages = "15320--15336",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks.However, their sequential autoregressive decoding forces inherently parallel clinical reasoning, such as differential diagnosis, into a single linear reasoning path, limiting both efficiency and reliability for complex medical problems.To address this, we propose MedVerse, a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph (DAG) process based on Petri Net theory.The framework adopts a full-stack design across data, model architecture, and system execution.For data creation, we introduce the MedVerse Curator, an automated pipeline that synthesizes knowledge-grounded medical reasoning path and transforms them into Petri Net{--}structured representations.At the architectural level, we propose a topology-aware attention mechanism with adaptive position indices that supports parallel reasoning while preserving logical consistency.Systematically, we develop a customized inference engine that supports parallel execution without additional overhead.Empirical evaluations show that MedVerse improves strong general-purpose LLMs by up to 8.9{\%}. Compared to specialized medical LLMs, MedVerse achieves comparable performance with improved clinical reliability, while delivering a 1.3$\times$ reduction in inference latency and a 1.7$\times$ increase in generation throughput, enabled by its parallel decoding capability."
}