🧠 AI⚪ NeutralImportance 6/10

OneReason Technical Report

arXiv – CS AI| OneRec Team, Biao Yang, Boyang Ding, Chenglong Chu, Dunju Zang, Fei Pan, Han Li, Hao Jiang, Honghui Bao, Huanjie Wang, Jian Liang, Jiangxia Cao, Jiao Ou, Jiaxin Deng, Jinghao Zhang, Kun Gai, Lu Ren, Peiru Du, Pengfei Zheng, Rongzhou Zhang, Ruiming Tang, Shiyao Wang, Siyang Mao, Siyuan Lou, Teng Shi, Wei Yuan, Wenlong Xu, Xingchen Liu, Xingmei Wang, Xinqi Jin, Yan Sun, Yan Wang, Yifei Hu, Yingzhi He, Yufei Ye, Yuhao Wang, Yunhao Zhou, Yuqin Dai, Zhao Liu, Zhipeng Wei, Zhixin Ling, Ziming Li, Zixing Zhang, Ziyuan Liu, An Zhang, Changxin Lao, Chaoyi Ma, Chengru Song, Defu Lian, Fan Yang, Guowang Zhang, Hao Peng, Jiayao Shen, Jie Chen, Jun Xu, Junmin Chen, Kun Zhang, Kuo Cai, Mingxing Wen, Minmao Wang, Minxuan Lv, Qi Zhang, Qiang Luo, Sheng Yu, Shijie Li, Shijie Yi, Shuang Yang, Shugui Liu, Shuni Chen, Tinghai Zhang, Tingting Gao, Xiang Wang, Xiangyu Wu, Xiangyu Zhao, Xiao Lv, Xiaoyou Zhou, Xuming Wang, Yong Du, Zejian Zhang, Zhaojie Liu, Zhiyang Zhang, Zhuang Zhuang, Ziqi Wang, Ziyi Zhao|June 5, 2026 at 04:00 AM

🤖AI Summary

OneReason introduces a novel framework for improving reasoning capabilities in generative recommendation models by addressing perception and cognition limitations. The approach combines semantic grounding of item tokens with multi-level chain-of-thought sequences, demonstrating that effective reasoning requires both language understanding and coherent interest modeling rather than scaling alone.

Analysis

OneReason addresses a critical limitation in deployed generative recommendation systems: while these models benefit from increased scale, they lack genuine reasoning capabilities. The research reveals that simply adopting chain-of-thought (CoT) techniques from large language models fails when applied to recommendation tasks using only item tokens. This gap between scaling benefits and reasoning activation represents a fundamental architectural constraint in current recommendation systems.

The technical contribution stems from analyzing why thinking-augmented models underperformed expectations. Rather than treating this as a scaling problem, the authors identify two essential components: perception—grounding item tokens in semantic meaning through pre-training—and cognition—reorganizing user behavior sequences into interpretable interest patterns. This framework acknowledges that recommendation reasoning differs structurally from language model reasoning, requiring specialized approaches.

The three-level cognition-enhanced CoT format represents a methodological advance for short-video, live-streaming, advertising, and e-commerce platforms. These high-velocity recommendation domains process massive user interactions daily; improved reasoning could enhance recommendation relevance while reducing computational overhead. The specialize-then-unify training recipe through reinforcement learning creates a training pathway balancing task-specific optimization with general capability transfer.

For stakeholders deploying generative recommendation systems, OneReason suggests that model transparency and user behavior coherence matter more than pure parameter scaling. The framework's applicability across multiple recommendation domains indicates broad implementation potential. Future research should validate whether these techniques meaningfully improve user satisfaction metrics and engagement in production environments.

Key Takeaways

→Chain-of-thought reasoning fails in item-token-only recommendation systems without semantic grounding and behavior coherence
→OneReason combines semantic perception during pre-training with multi-level cognition-enhanced reasoning for improved recommendations
→The three-factor approach (perception, cognition, specialized training) outperforms traditional scaling-based improvements
→Framework applies across e-commerce, short-video, live-streaming, and advertising platforms
→Results suggest recommendation reasoning requires domain-specific architecture distinct from language model reasoning