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JD's Oxygen AI Item Center manages tens of billions of SKUs via a 'semantic search then discrimination' architecture, where a separately updatable ontology knowledge base lets the model just check item-to-ontology matches, reducing task complexity and hallucination without needing model retraining as the ontology evolves.
JD describes its Oxygen AIIC system, which handles hundreds of millions of item updates daily on Huawei Ascend NPUs by externalizing ontology knowledge for retrieval and having the model only discriminate matches, avoiding costly retraining as categories evolve. ✦ AI generated
JD (Oxygen AIIC research paper) · Import AI · 2026-07-06 · original ↗
In the semantic search stage, the dynamically evolving ontology is externalized as a separate ontology knowledge base, enabling continuous ontology updates without model retraining. In the discrimination stage, the model only determines whether the item matches the retrieved ontology entries. This formulation substantially reduces task complexity, mitigates model hallucination, and enhances generalization to ontology evolution.
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supports → JD's Oxygen AI Item Center scales item understanding across tens of billions of SKUs by separating a continuously updatable ontology knowledge base from a self-evolving LLM/VLM that only needs to match items to retrieved ontology entries, avoiding costly retraining.JD researchers · Import AIextends → JD's Oxygen AI Item Center scales item understanding across tens of billions of SKUs by separating a continuously updatable ontology knowledge base from a self-evolving LLM/VLM that only needs to match items to retrieved ontology entries, avoiding costly retraining.JD researchers · Import AI