@inproceedings{liu-etal-2026-onerec,
    title = "{O}ne{R}ec-Think: In-Text Reasoning for Generative Recommendation",
    author = "Liu, Zhanyu  and
      Wang, Shiyao  and
      Wang, Xingmei  and
      Zhang, Rongzhou  and
      Deng, Jiaxin  and
      Bao, Honghui  and
      Zhang, Jinghao  and
      Li, Wuchao  and
      Zheng, PengFei  and
      Wu, Xiangyu  and
      Hu, Yifei  and
      Hu, Qigen  and
      Luo, Xinchen  and
      Ren, Lejian  and
      Zixing, Zhang  and
      Wang, Qianqian  and
      Cai, Kuo  and
      Wu, Yunfan  and
      Cheng, Hongtao  and
      Cheng, Zexuan  and
      Ren, Lu  and
      Wang, Huanjie  and
      Su, Yi  and
      Tang, Ruiming  and
      Gai, Kun  and
      Zhou, Guorui",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.123/",
    pages = "2664--2681",
    ISBN = "979-8-89176-390-6",
    abstract = "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 implicit predictors, critically lacking the capacity for explicit and controllable reasoning{---}a key advantage of LLMs. To bridge this gap, we propose OneRec-Think, a unified framework that seamlessly integrates dialogue, reasoning, and personalized recommendation. OneRec-Think incorporates: (1) Itemic Alignment: cross-modal Item-Textual Alignment for semantic grounding; (2) Reasoning Activation: Reasoning Scaffolding to activate LLM reasoning within the recommendation context; and (3) Reasoning Enhancement, where we design a recommendation-specific reward function that accounts for the multi-validity nature of user preferences. Experiments across public benchmarks show state-of-the-art performance. Moreover, our proposed ``Think-Ahead'' architecture enables effective industrial deployment, achieving a 0.159{\%} gain in APP Stay Time and validating the practical efficacy of the model{'}s explicit reasoning capability."
}