# Glance

Retrieval-augmented generation usually hands reranking and answering to two models, so candidate passages are processed twice. Glance adds a lightweight decision adapter and pointer head to the generator, prefills the candidates once, and reads one short probe per passage for a relevance score. After 39 minutes of MS MARCO fine-tuning it reaches nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, against 0.721 and 0.326 for bge-reranker-v2-m3; on 300 HotpotQA questions the same base generator answers with F1 0.045 higher from its selected context than from cross-encoder-selected context.

Retrieval-augmented generation usually hands reranking and answering to two models, so candidate passages are processed twice. Glance adds a lightweight decision adapter and pointer head to the generator, prefills the candidates once, and reads one short probe per passage for a relevance score. After 39 minutes of MS MARCO fine-tuning it reaches nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, against 0.721 and 0.326 for bge-reranker-v2-m3; on 300 HotpotQA questions the same base generator answers with F1 0.045 higher from its selected context than from cross-encoder-selected context.