The paper introduces a new scorer for generative answer engines that ranks candidate pages by the likelihood of being cited. It evaluates pretrained rerankers, a hosted typed decision model (Jev), eight open decision models, and a distilled 4B student on GEO‑Bench queries answered by ten engines. The distilled student, fine‑tuned with low‑rank adapters, achieves comparable performance to larger models and the authors release the model, benchmark, predictions, and code.
arXiv:2611.03179v1 Announce Type: cross Abstract: People often attach instructions to their search queries, asking for pages written for beginners, documents in a certain language or official sources...
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: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...
DecisionRank introduces an on-policy distillation (OPD) method that trains decision models to replicate a generative LLM’s ranking knowledge by converting the student’s document scores into a ranked sequence and using a teacher to provide conditional feedback. The approach combines item‑space OPD with offline ranking supervision, achieving state‑of‑the‑art results on retrieval benchmarks such as TREC DL, BEIR, and BRIGHT. Even with a 1.7B parameter model, DecisionRank outperforms its 27B generative teacher on TREC DL19–23, and at 8B it surpasses the generative listwise counterpart while running 24.5× faster on TREC DL and 16.7× faster on BRIGHT.
arXiv:2605. 04495v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance.
The paper investigates whether System‑1‑style decision models can replace dedicated rerankers in retrieval‑augmented generation (RAG) systems. After screening 21 open‑weight models on 11 benchmarks, the authors compare a promising decision model with a dedicated reranker on identical candidate lists. While limiting candidate text length improves reliability and makes some configurations competitive, a tuned reranker remains faster and uses less GPU memory, indicating that replacing a reranker requires balancing quality, latency, and memory.
ZooWork-ShopRanker is a family of open e‑commerce rerankers (0.6B, 4B, and 8B) that align with human shopping preferences by using large language models as preference oracles to generate training pairs. The flagship 8B model serves as a teacher for the smaller 4B and 0.6B models, which are further refined on judged pairs. A new benchmark, ShopRank‑Bench, contains ~10,000 private‑traffic preference pairs and shows that all ZooWork models outperform the strongest open reranker baseline and their own un‑aligned versions.
arXiv:2609.37647v1 Announce Type: cross Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed...
Re$^3$Rank is a listwise reranker that uses reasoning-conditioned retrieval heads (RRHeads). It generates a reasoning trace for each candidate, then scores candidates by using the trace tokens as attention queries for selected RRHeads, producing continuous relevance scores. A straight‑through estimator allows joint training of the generator and scorer, and on nine benchmarks the 4B model outperforms 11 baseline rerankers by an average of 3.11 NDCG@10 points, while the 0.6B model beats rerankers with up to 32B parameters.
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...
The paper introduces Iris-mini and Iris-pro, two search agents trained at 35B and 397B parameter scales. They use a novel data pipeline that constructs reverse‑engineered multi‑hop queries from web hyperlinks, filters trajectories, and alternates supervised fine‑tuning with reinforcement learning in a process called SFT‑RL climbing. Evaluations on several benchmarks show that, with inference‑time context management, the agents achieve the best open‑source results in their parameter ranges.
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...