titleJD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications
authorsOxygen AIIC, Chan Long, Chao Liu, Chaofan Chen, Chaohui Dong, Chunyuan Guo, Danping Liu, Debin Liu, Deping Xiang, Fulai Xu, Guangyue Liu, Hao Li, Huichun Hu, Jian Yang, Jianan Wang, Jianbo Zhao, Jiaoyang Li, Jiaxing Wang, Jinglong Li, Jinjin Guo, Jun Fang, Jun Liu, Kai Zhou, Li Wang, Lili Gao, Liying Chen, Luning Yang, Mengdi Zhou, Pengzhang Liu, Qi Lv, Qianyun Wang, Qixia Jiang, Ruyue Li, Shimu Liang, Shuxing Wang, Sijie Zhang, Siqi Li, Tianhao Gao, Wang Ke, Weihu Huang, Wencan Lai, Wenjie Zhang, Xiaohui Zhang, Xiaojing Dong, Ya Liu, Yifeng Zhang, Yixiang Wang, Yongtai Zhang, Yongyi Liao, Zhaoru Chen, Zhen Chen, Zhiyong Ma, Zhiyuan Liu, Zhongwei Liu, Ziyan Xing
date2026-06-29
venuearXiv (preprint, not peer reviewed)
idarXiv:2606.28070
upvotes13 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractJD.com, one of the world's largest e-commerce platforms, serves over 700 million active users and millions of merchants, with a catalog of tens of billions of SKUs. At this scale, high-quality, structured item knowledge underpins a better consumer experience, lower management costs, and higher operational efficiency-yet producing and serving it poses three industrial-scale challenges: fast-emerging concepts, high-quality knowledge production for massive SKUs, and diverse downstream requirements. To address these challenges, we present the JD Oxygen AI Item Center (Oxygen AIIC), an industrial-scale platform built on LLMs/VLMs for item-knowledge production and service. Oxygen AIIC is built around four core pillars: (i) ontology engineering driven by efficient human-AI collaboration, which supports the dynamic evolution and agile expansion of an ontology with millions of entries; (ii) a "Semantic Search then Discrimination"(S2D) knowledge identification architecture that, combined with throughput improvement strategies, enables scalable, extensible, and high-throughput AI Item Library production for tens of billions of SKUs; (iii) self-evolving item-understanding LLMs/VLMs that improve in a stable and controllable manner, enabling knowledge production with 94.2% precision and 82.8% recall; and (iv) a unified item tunnel that serves as the data and service hub. Oxygen AIIC now covers tens of thousands of JD categories and processes hundreds of millions of item updates per day on Huawei Ascend NPUs. It has accumulated hundreds of billions of item-knowledge assets. Deployed across core business scenarios-including search, recommendation, operations, category planning-Oxygen AIIC has delivered measurable gains at scale. Search-traffic coverage reaches 80.4%, item-information quality issues drop by 37%, the automated fill rate of core attributes during item listing exceeds 80%.

cite

@misc{aiic2026jd,
  title={JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications},
  author={Oxygen AIIC and Chan Long and Chao Liu and Chaofan Chen and Chaohui Dong and Chunyuan Guo and Danping Liu and Debin Liu and Deping Xiang and Fulai Xu and Guangyue Liu and Hao Li and Huichun Hu and Jian Yang and Jianan Wang and Jianbo Zhao and Jiaoyang Li and Jiaxing Wang and Jinglong Li and Jinjin Guo and Jun Fang and Jun Liu and Kai Zhou and Li Wang and Lili Gao and Liying Chen and Luning Yang and Mengdi Zhou and Pengzhang Liu and Qi Lv and Qianyun Wang and Qixia Jiang and Ruyue Li and Shimu Liang and Shuxing Wang and Sijie Zhang and Siqi Li and Tianhao Gao and Wang Ke and Weihu Huang and Wencan Lai and Wenjie Zhang and Xiaohui Zhang and Xiaojing Dong and Ya Liu and Yifeng Zhang and Yixiang Wang and Yongtai Zhang and Yongyi Liao and Zhaoru Chen and Zhen Chen and Zhiyong Ma and Zhiyuan Liu and Zhongwei Liu and Ziyan Xing},
  year={2026},
  eprint={2606.28070},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2606.28070}
}