# Fine-mapping

V2G splits variant-to-gene assignment into recorded retrieval steps, then has a language model rerank the assembled evidence and cite what it used. On 135 held-out loci where candidate retrieval succeeded and Open Targets predicts, reranking raises Precision@1 from 0.711 for the evidence ordering to 0.793, with no difference from Open Targets L2G detected. Given only the trait and shuffled gene symbols, the same model scores 0.864, so the gain is recall of known associations rather than integration of retrieved evidence.

V2G splits variant-to-gene assignment into recorded retrieval steps, then has a language model rerank the assembled evidence and cite what it used. On 135 held-out loci where candidate retrieval succeeded and Open Targets predicts, reranking raises Precision@1 from 0.711 for the evidence ordering to 0.793, with no difference from Open Targets L2G detected. Given only the trait and shuffled gene symbols, the same model scores 0.864, so the gain is recall of known associations rather than integration of retrieved evidence.