cfDNA-Scientist: Multi-Agent Biomedical Feature Discovery
A self-evolving multi-agent system that proposes, implements, and evaluates biomedical feature-extraction code. Reaches a cross-validated AUC of 0.916 on breast cancer detection.
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A self-evolving multi-agent system that proposes, implements, and evaluates biomedical feature-extraction code. Reaches a cross-validated AUC of 0.916 on breast cancer detection.
A reinforcement learning framework for fine-tuning discrete flow-matching models. Outperforms prior state-of-the-art by 10.6% on DNA design and 15.4% on protein design.