Cura 1T: Specialized Model for Agentic Healthcare

Abstract: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.

Artificial Intelligence (cs.AI)

arXiv:2607.15314 [cs.AI]

(or arXiv:2607.15314v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.15314 Focus to learn more arXiv-issued DOI via DataCite

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