The Generator Reuses Its Prefill to Rank Two Hundred Passages

Retrieval systems often keep separate reranking and answer models resident, duplicating memory and passage processing. Glance adds a small decision adapter and pointer head to a 9-billion-parameter generator, then branches short relevance probes from a shared prefill. After 39 minutes of MS MARCO fine-tuning, it exceeded the reported bge reranker scores on SciFact and NFCorpus. On 300 HotpotQA questions, selected context raised answer F1 by 0.045, and ranking quality continued to improve through 200 candidates—about 43,000 tokens—where it beat the cross-encoder by 0.058 nDCG@10. Evaluation covers one backbone family and three datasets.