Cura 1T: Specialized Model for Agentic Healthcare
Cura 1T is a healthcare-specialized LLM trained through a human-gated self-evolution loop. It handles patient consultation, clinical reasoning over text and images, interactive diagnosis, and EHR tool use. It ranks top among frontier baselines on healthcare benchmarks while remaining competitive on out-of-domain reasoning and agentic tasks.
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[Submitted on 15 Jul 2026]
Title:Cura 1T: Specialized Model for Agentic Healthcare
View a PDF of the paper titled Cura 1T: Specialized Model for Agentic Healthcare, by actAVA AI: Haolin Chen and 9 other authors
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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.
Comments: Model: this https URL Docs: this https URL Github: this https URL
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.15314 [cs.AI]
(or arXiv:2607.15314v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.15314
arXiv-issued DOI via DataCite
Submission history
From: Haolin Chen [view email] [v1] Wed, 15 Jul 2026 22:05:23 UTC (1,388 KB)
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