arXiv cs.AI· Qi Xu, Zhigang Hua, Zijie Lei, Yan Xie, Jizhe Zhang, Lynn Lu, Delun Du, Sha Hua, Ren Mao, Ke Pan, Haomin Yu, Sandeep Pandey, Luke Simon·· 11 小时前AI 评分26
面向高效整页排序的 One-Pass 推理框架 Multi-Item Scoring
Towards Efficient Whole Page Ranking with One-Pass Inference
AI 导读
研究者提出 Multi-Item Scoring（MIS），一种前缀感知的 NAR 风格整页排序框架，通过每次请求只计算一次可复用表示、在受限 beam search 中做轻量前缀条件打分，兼顾序列依赖与推理效率。
正文 · 原文
Authors:Qi Xu, Zhigang Hua, Zijie Lei, Yan Xie, Jizhe Zhang, Lynn Lu, Delun Du, Sha Hua, Ren Mao, Ke Pan, Haomin Yu, Sandeep Pandey, Luke Simon
Abstract:Whole-page ranking jointly optimizes advertisements and organic content to balance business outcomes and user engagement under complex layout and business constraints. Existing non-autoregressive (NAR) approaches enable efficient page generation but typically score candidate items independently of previously selected items, limiting their ability to capture order-dependent interactions. Autoregressive (AR) methods address this limitation through sequential conditioning, but incur substantial inference overhead that challenges large-scale deployment. Bridging this quality-efficiency gap remains a challenge in industrial whole-page ranking.
We propose Multi-Item Scoring (MIS), a prefix-aware NAR-style framework that incorporates sequential context into efficient multi-item scoring. MIS integrates global candidate interactions, localized prefix conditioning, and position-aware scoring within a shared representation architecture. By computing reusable representations once per request and performing lightweight prefix-conditioned scoring during constrained beam search, MIS captures important sequential dependencies without repeated full-model inference.
Experiments on KuaiRand-1K and RL4RS demonstrate that MIS approaches the generation quality of an autoregressive CGA baseline while achieving 4.07x and 3.54x faster CPU decoding, respectively. In a randomized evaluation on a large-scale social-video platform, MIS improves the realization metric by 0.104% and watch time by 0.058%, while reducing ranking-service CPU consumption by 9.73%. These results demonstrate that prefix-aware multi-item scoring can narrow the quality gap between NAR and AR generation while retaining the efficiency required for industrial scalability.
| Comments: | 12 pages, 2 figures, 5 tables |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (stat.ML) |
| Cite as: | arXiv:2611.05542 [cs.AI] |
| (or arXiv:2611.05542v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05542 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qi Xu [view email]
[v1]
Tue, 3 Nov 2026 19:33:22 UTC (625 KB)
来源：arXiv cs.AI · arxiv.org