Cura 1T: Healthcare Foundation Model via Recursive Self-Improvement
cs.AI
Submitted: 2026-07-15
Updated: 2026-10-05
Comments: Model: https://actava.ai/cura; Docs: https://actava.ai/cura/docs; Github: https://github.com/actava-ai/Cura
Code: https://github.com/actava-ai/Cura
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized language models that cover these use cases together remain limited.
Terminology
Abstract
Healthcare spans high-stakes communication, expert reasoning, and workflow execution, yet specialized language models 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 foundation model trained through recursive self-improvement (RSI). In each RSI round, the RSI harness runs the current model on healthcare benchmarks, evaluates the trajectories to locate capability gaps, and refines the training mixture by synthesizing training data. On 6 healthcare benchmarks, Cura 1T scores highest on MedAgentBench, HealthBench Professional, HealthBench Hard, MedXpertQA text, and AgentClinic, and second on MedXpertQA multimodal. It preserves performances on out-of-domain reasoning and agentic benchmarks including AIME, GPQA-Diamond, and τ squared-Bench.
Sources
- HealthBench: Evaluating Large Language Models Towards Improved Human Health
- Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care
- $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- ResearchGym: Evaluating Language Model Agents on Real-World AI Research
- MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents
- What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
- DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
- HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents
- PhysicianBench: Evaluating LLM Agents in Real-World EHR Environments
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- ClinAlign: Scaling Healthcare Alignment from Clinician Preference
- Self-Refine: Iterative Refinement with Self-Feedback
- AlphaEvolve: A coding agent for scientific and algorithmic discovery
- HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats
- MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
- GPQA: A Graduate-Level Google-Proof Q&A Benchmark
- AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
- Self-Distillation Enables Continual Learning
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