most citedOpenOneRec Technical Report

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

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

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.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.IR2026

ALPBench: A Benchmark for Attribution-level Long-term Personal Behavior Understanding

Lu Ren, Junda She, Xinchen Luo +23

Recent advances in large language models have highlighted their potential for personalized recommendation, where accurately capturing user preferences remains a key challenge. Leve…

cs.IR2025★ 1 cited

OpenOneRec Technical Report

Guorui Zhou, Honghui Bao, Jiaming Huang +44

While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation sy…

cs.IR2025

OneRec-Think: In-Text Reasoning for Generative Recommendation

Zhanyu Liu, Shiyao Wang, Xingmei Wang +23

The powerful generative capacity of Large Language Models (LLMs) has instigated a paradigm shift in recommendation. However, existing generative models (e.g., OneRec) operate as im…

cs.IR2025

OneRec-V2 Technical Report

Guorui Zhou, Hengrui Hu, Hongtao Cheng +72

Recent breakthroughs in generative AI have transformed recommender systems through end-to-end generation. OneRec reformulates recommendation as an autoregressive generation task, a…