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 describes Oxygen AIIC's architecture, which externalizes its evolving product ontology into a separate knowledge base so the LLM/VLM only has to judge item-to-ontology matches, reducing complexity and hallucination while enabling updates without retraining, run on Huawei Ascend NPUs. ✦ AI generated

JD researchers · 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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