Hugging Face
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NVIDIA Nemotron 3.5 Embed, Rerank, and Parse: Advancing Agents with World-Class Retrieval Models
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What was said about it
NVIDIA's new retrieval models for agents 🤖 NVIDIA released new Nemotron 3.5 models for embedding and reranking, reducing AI agent token costs by up to 14%. Turns out, good retrieval means cheaper AI. Image: Hugging Face Blog
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Three kinds of line, three kinds of source. Every measurement is computed from primary artifacts we read ourselves — Hugging Face model cards and file listings, GitHub release feeds, OpenRouter's model catalogue. Every headline comes from the publisher's own feed, linked and attributed; no aggregator sits in between, and nothing is rewritten. Where a publisher syndicates a short summary in that same feed, it is shown under the headline the way a headline is — translated on the Korean page with the published original kept underneath it. Only the summary field is ever shown. The field that carries the article is read and never shown: it screens out 'summaries' that are really the article's opening lines, and — for a publisher's own announcements only — it is the evidence the event pages' What-happened notes are written and checked against. One exception, since 2026-09-03: the page a discussion thread links to is fetched and read once, to write a single sentence saying what it claims, labelled as the post's own claim; nothing from it is quoted. Every discussion is a public thread, linked, with its top comments in the order that thread ranked them and in the words they were written — selected by it, not by us.
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