Cura 1T：代理型醫療專用模型

摘要

醫療領域涵蓋高風險溝通、專家推理及工作流程執行，然而能夠同時涵蓋這些應用場景的專業大型語言模型仍屬少數。一個醫療模型必須能夠處理病患諮詢、基於文字與影像的臨床推理、互動式診斷，以及使用電子健康記錄工具。這些能力在不同面向會產生不同的失效模式，針對單一任務的狹義更新可能導致其他任務表現下降。我們提出 Cura 1T，這是一個透過人工門控自我演化迴圈訓練的醫療專業大型語言模型。在每個演化回合中，訓練代理會規劃一個目標能力、訓練模型、評估基準軌跡，並根據觀察到的失敗案例調整資料混合。這個以資料為中心的迴圈透過有針對性的合成與精選範例來改進模型，而非僅進行一次通用醫療資料更新。在醫療評測套件中，Cura 1T 在頂尖基準模型中排名居首或接近頂尖，同時在跨領域推理與代理型基準測試中仍保持競爭力。

English

Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized LLMs that cover these use cases together remain limited. A healthcare model must handle patient consultation, clinical reasoning over text and images, interactive diagnosis, and electronic health record (EHR) tool use. These capabilities fail in different ways, and a narrow update for one task can degrade another. We present Cura 1T, a healthcare-specialized LLM trained through a human-gated self-evolution loop. In each evolution round, a training agent plans a target capability, trains the model, evaluates benchmark trajectories, and refines the data mixture from observed failures. This data-centered loop improves the model through targeted synthetic and curated examples rather than a single generic medical-data update. Across the healthcare evaluation suite, Cura 1T ranks at or near the top among frontier baselines, while remaining competitive on out-of-domain reasoning and agentic benchmarks.

Cura 1T：代理型醫療專用模型

Cura 1T: Specialized Model for Agentic Healthcare

摘要

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