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Cura 1T addresses the critical challenge of capability degradation in specialized healthcare agents by employing human-gated recursive self-improvement to refine data mixtures based on observed failures rather than generic updates. This approach enables a single model to robustly handle diverse agentic tasks like clinical reasoning and EHR tool use while maintaining strong performance across out-of-domain benchmarks. The work highlights the necessity of targeted training strategies for deploying reliable, multi-functional AI systems in high-stakes medical environments.
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Cura 1T: Specialized Model for Agentic Healthcare

arXiv2 min read

arXiv:2607.15314v2 Announce Type: replace Abstract: Healthcare AI agents handle patient consultation, clinical reasoning over text and images, interactive diagnosis, and electronic health record (EHR) tool use, yet specialized agentic models that cover these use cases together remain limited. These capabilities fail in different ways, and a narrow update for one task can degrade another. We present Cura 1T, a healthcare-specialized LLM built on the open-weight Kimi-K2.6 and trained through a human-gated recursive self-improvement (RSI) loop. Specifically, in each round, the RSI harness plans a target capability, trains the model, evaluates benchmark trajectories, and refines the data mixture from observed failures with 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.

Abstract:Healthcare AI agents handle patient consultation, clinical reasoning over text and images, interactive diagnosis, and electronic health record (EHR) tool use, yet specialized agentic models that cover these use cases together remain limited. These capabilities fail in different ways, and a narrow update for one task can degrade another. We present Cura 1T, a healthcare-specialized LLM built on the open-weight Kimi-K2.6 and trained through a human-gated recursive self-improvement (RSI) loop. Specifically, in each round, the RSI harness plans a target capability, trains the model, evaluates benchmark trajectories, and refines the data mixture from observed failures with 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:2607.15314v2 [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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Full text under the publisher's open licence · 225 words

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