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

ArXi:2603.05308v2 Announce Type: replace Assessing whether an article s an assertion is essential for hallucination detection and claim verification. While large language models (LLMs) have the potential to automate this task, achieving strong performance requires frontier models such as GPT-5 that are prohibitively expensive to deploy at scale. To efficiently perform biomedical evidence attribution, we present Med-V1, a family of small language models with only three billion parameters.