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• Med-V1, a family of small language models with only three billion parameters, substantially outperforms its base models by +27.0% to +71.3% on five biomedical benchmarks unified into a verification format.result 95%
• Med-V1 performs comparably to frontier LLMs such as GPT-5 in biomedical evidence attribution tasks, despite its smaller size.result 90%
• Med-V1 was trained on high-quality synthetic data newly developed in this study, named MedFact-Synth.method 90%
• The format instruction for citations strongly affects citation validity and hallucination in LLM-generated answers, with GPT-5 generating more claims but exhibiting hallucination rates similar to GPT-4o.result 85%
• Med-V1 can automatically identify high-stakes evidence misattributions in clinical practice guidelines, revealing potentially negative public health impacts.technical 85%
• Scaling the Med-V1 solution outside biomedical verification could require new datasets and adaptations, and its current reliance on synthetic data might miss nuances captured in naturally occurring datasets.limitation 80%
• Med-V1 provides an efficient and accurate lightweight alternative to frontier LLMs for practical and real-world applications in biomedical evidence attribution and verification tasks.market 85%

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