Cura 1T: Specialized Model for Agentic Healthcare

Researchers have introduced Cura 1T, a healthcare-specialized large language model trained through a human-gated self-evolution loop that improves performance on patient consultation, clinical reasoning, interactive diagnosis, and EHR tool use without degrading other capabilities. The model ranks at or near the top among frontier baselines on healthcare evaluations while remaining competitive on out-of-domain reasoning and agentic benchmarks.

arXiv:2607.15314v1 Announce Type: new 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.