Cura 1T: Specialized Model for Agentic Healthcare

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.