Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution

In three linesMed-V1 is a family of 3-billion-parameter language models trained on synthetic data for biomedical evidence attribution. It outperforms base models by +27% to +71% on five benchmarks and matches frontier LLMs like GPT-5, while detecting hallucinations and misattributions in clinical guidelines.

Summary generated by Claude — human-verified