Selected work

Representative Papers

MedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation

Oct 06, 2026

This study addresses the limitations of large language models in medical reasoning, specifically their reliance on static knowledge and the prohibitive costs of expert supervision. To overcome these challenges, we propose a self-evolving framework tailored for open-domain medical applications, wherein question-generation and problem-solving agents collaborate to achieve continuous optimization. Furthermore, this work introduces a novel controlled knowledge accumulation strategy that integrates multi-turn evidence-guided reasoning, external tool invocation, and both exploratory and persistent knowledge management techniques to ensure iterative reliability. This mechanism effectively transcends the static knowledge bottleneck inherent in existing approaches. Extensive evaluations across five benchmarks demonstrate that the proposed framework significantly outperforms current baselines, achieving an average accuracy improvement of up to 13.7 percentage points.

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Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation

Sep 29, 2026

This study addresses the limitations of medical generalist models, specifically their restricted training data scale and insufficient depth of domain knowledge, by proposing a multi-teacher knowledge distillation framework for constructing vision foundation models. The method compresses multi-source expert knowledge into a unified model via a modality-aligned distillation stream, incorporating a balanced loss mechanism and a data reorganization strategy to effectively integrate complementary cross-modal expertise. Experimental results demonstrate that the proposed model significantly outperforms existing strong generalist models across benchmarks spanning five medical imaging modalities, achieving performance levels approaching the upper bounds of individual single-modality experts. This work establishes a novel paradigm for developing high-performance, universal medical AI systems.

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Recent publications

Latest Papers

MedZERO: Self-Evolving Agents for Open-Ended Medical Reasoning Through Controlled Knowledge Accumulation

Oct 06, 2026

This study addresses the limitations of large language models in medical reasoning, specifically their reliance on static knowledge and the prohibitive costs of expert supervision. To overcome these challenges, we propose a self-evolving framework tailored for open-domain medical applications, wherein question-generation and problem-solving agents collaborate to achieve continuous optimization. Furthermore, this work introduces a novel controlled knowledge accumulation strategy that integrates multi-turn evidence-guided reasoning, external tool invocation, and both exploratory and persistent knowledge management techniques to ensure iterative reliability. This mechanism effectively transcends the static knowledge bottleneck inherent in existing approaches. Extensive evaluations across five benchmarks demonstrate that the proposed framework significantly outperforms current baselines, achieving an average accuracy improvement of up to 13.7 percentage points.

0 citationsRead paper

Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation

Sep 29, 2026

This study addresses the limitations of medical generalist models, specifically their restricted training data scale and insufficient depth of domain knowledge, by proposing a multi-teacher knowledge distillation framework for constructing vision foundation models. The method compresses multi-source expert knowledge into a unified model via a modality-aligned distillation stream, incorporating a balanced loss mechanism and a data reorganization strategy to effectively integrate complementary cross-modal expertise. Experimental results demonstrate that the proposed model significantly outperforms existing strong generalist models across benchmarks spanning five medical imaging modalities, achieving performance levels approaching the upper bounds of individual single-modality experts. This work establishes a novel paradigm for developing high-performance, universal medical AI systems.

0 citationsRead paper