Retrieval-augmented generation (RAG) systems often use a dedicated reranker to choose which retrieved candidates reach the language model. Reranking improves relevance but adds latency, so teams look...
arXiv:2610.32318v1 Announce Type: new
Abstract: System One models, or decision models, answer a question about an input with a calibrated probability instead of generated text. Within weeks of the fi...
By Michael Dinzinger, Kanishka Ghosh Dastidar, Michael Granitzer
Glance is a retrieval‑augmented generation approach that eliminates the need for separate reranker models by adding a lightweight decision adapter and pointer head to a single 9B backbone. It scores candidate passages directly from a shared prefill, requiring only about 0.1 GiB of extra memory per decision skill. In fine‑tuning on MS MARCO, Glance outperforms the bge‑reranker‑v2‑m3 on SciFact and NFCorpus, and improves answer F1 on HotpotQA by 0.045, while scaling to 200 candidates it surpasses cross‑encoder reranking by 0.058 nDCG@10.
arXiv:2609.40241v1 Announce Type: cross
Abstract: Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation q...
By Hanjia Lyu, Yinglong Xia
The study evaluates large language models (LLMs) as rerankers in conversational movie recommendation, comparing proprietary, open-weight, and fine-tuned LLMs against collaborative-filtering and sequential baselines on the ReDial benchmark. Results show that the best proprietary LLM achieves an NDCG@10 of 0.1497 with a shared semantic candidate pool, outperforming non-LLM baselines, while open-weight LLMs do not surpass a tuned shallow autoencoder under the same protocol. The analysis also highlights that reranker performance is highly sensitive to candidate generation, pool size, scoring policy, and decoding temperature, suggesting these factors should be reported as standard evaluation fields.
By Ante Kapetanovic, Tomislav Duricic, Andro Mercep, Emanuel Lacic
arXiv:2602.18613v2 Announce Type: replace
Abstract: Standard reranking evaluations study how a reranker orders candidates returned by an upstream retriever. This setup couples ranking behavior with r...
By Baris Arat, Emre Sefer