JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications
Quick Answer
This paper shows that JD.com introduces the Oxygen AI Item Center (Oxygen AIIC), a large-scale LLM/VLM platform that enhances item knowledge production with 94.2% precision and 82.8% recall.
Quick Take
JD.com introduces the Oxygen AI Item Center (Oxygen AIIC), a large-scale LLM/ platform that enhances item knowledge production with 94.2% precision and 82.8% recall. It processes hundreds of millions of updates daily, achieving 80.4% search-traffic coverage and reducing item-information quality issues by 37%. This system supports over 700 million users and millions of merchants, optimizing operational efficiency and consumer experience.
Key Points
- Oxygen AIIC utilizes ontology engineering for dynamic knowledge evolution.
- The S2D architecture enables high-throughput AI Item Library production.
- Self-evolving LLMs/VLMs ensure stable knowledge production with high precision.
- Covers tens of thousands of categories, processing hundreds of millions of updates daily.
- Automated attribute fill rate exceeds 80%, enhancing item listing efficiency.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 28070v1 Announce Type: new Abstract: JD. 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/ 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%.
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