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 paths 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 while delivering a 1.3x reduction in inference latency and a 1.7x increase in generation throughput, enabled by its parallel decoding capability. Code is available at this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.07529 [cs.LG] |
| (or arXiv:2602.07529v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.07529 arXiv-issued DOI via DataCite |
From: Jianwen Chen [view email]
[v1]
Sat, 7 Feb 2026 12:54:01 UTC (5,218 KB)
[v2]
Tue, 10 Feb 2026 03:03:52 UTC (5,218 KB)
[v3]
Wed, 15 Apr 2026 23:53:20 UTC (5,219 KB)
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