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20242026
most citedLiveForesighter: Generating Future Information for Live-Streaming Recommendations at Kuaishou

1 citations · 2 across the 29 of their papers we have counts for

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14 papers · 1 filter

cs.LG2026

Kwai Summary Attention V2 Technical Report

OneRec Team, Chenglong Chu, Guohong Mu +30

Summary-token architectures such as Kwai Summary Attention (KSA) compress distant context into learnable Summary token per chunk, and hybrid variants interleave these layers with f…

cs.IR2026

From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents

Zijie Zhuang, Changxin Lao, Pengbo Xu +13

Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.…

cs.IR2026

OneReason Technical Report

OneRec Team, Biao Yang, Boyang Ding +81

Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. Howev…

cs.AI2026

UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence

Hongwei Zhang, Qiqiang Zhong, Jiangxia Cao +8

Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-specific search or item-agnostic…

cs.IR2026

Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling

Tianbao Ma, Ruochen Yang, Chengen Li +7

User retention is a key metric to measure long-term engagement in modern platforms. In real-time bidding (RTB) advertising system for user re-engagement, the retention model is req…

cs.IR2026

Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation

Yu Liu, Jiangxia Cao

Generative recommender systems have recently emerged as a promising paradigm by formulating next-item prediction as an auto-regressive semantic IDs generation, such as OneRec serie…

cs.CL2026

Kwai Summary Attention Technical Report

Chenglong Chu, Guorui Zhou, Guowang Zhang +35

Long-context ability, has become one of the most important iteration direction of next-generation Large Language Models, particularly in semantic understanding/reasoning, code agen…

cs.CV2026

Rethinking Token-Level Policy Optimization for Multimodal Chain-of-Thought

Yunheng Li, Hangyi Kuang, Hengrui Zhang +4

Multimodal Chain-of-Thought (CoT) reasoning requires large vision-language models to construct reasoning trajectories that interleave perceptual grounding with multi-step inference…

cs.CV2026

CREM: Compression-Driven Representation Enhancement for Multimodal Retrieval and Comprehension

Lihao Liu, Yan Wang, Biao Yang +10

Multimodal Large Language Models (MLLMs) have shown remarkable success in comprehension tasks such as visual description and visual question answering. However, their direct applic…

cs.CV2026

Kelix Technical Report

Boyang Ding, Chenglong Chu, Dunju Zang +28

Autoregressive large language models (LLMs) scale well by expressing diverse tasks as sequences of discrete natural-language tokens and training with next-token prediction, which u…

cs.IR2026

SARM: LLM-Augmented Semantic Anchor for End-to-End Live-Streaming Ranking

Ruochen Yang, Yueyang Liu, Zijie Zhuang +14

Large-scale live-streaming recommendation requires precise modeling of non-stationary content semantics under strict real-time serving constraints. In industrial deployment, two co…

cs.IR2026

OneLive: Dynamically Unified Generative Framework for Live-Streaming Recommendation

Shen Wang, Yusheng Huang, Ruochen Yang +15

Live-streaming recommender system serves as critical infrastructure that bridges the patterns of real-time interactions between users and authors. Similar to traditional industrial…

cs.IR2026

QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling

Tian Xia, Jiaqi Zhang, Yueyang Liu +25

With the evolution of large language models (LLMs), there is growing interest in leveraging their rich semantic understanding to enhance industrial recommendation systems (RecSys).…

cs.IR2026

OneMall: One Architecture, More Scenarios -- End-to-End Generative Recommender Family at Kuaishou E-Commerce

Kun Zhang, Jingming Zhang, Wei Cheng +29

In the wave of generative recommendation, we present OneMall, an end-to-end generative recommendation framework tailored for e-commerce services at Kuaishou. Our OneMall systematic…