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Xiaohongshu unveils HELMSMAN for cost-effective billion-scale vector search

By PulseAugur Editorial · [1 sources] ·

Xiaohongshu's engine team has developed HELMSMAN, a new infrastructure for billion-scale vector search. Presented at OSDI 2026, HELMSMAN utilizes all-flash storage to replace a significant portion of traditional CPU and DRAM resources. This approach achieves a 90% cost reduction while maintaining performance for large-scale vector search operations. AI

IMPACT This development could significantly reduce the cost of deploying and scaling AI models that rely on large-scale vector search.

RANK_REASON Publication of a new infrastructure system at a research conference. [lever_c_demoted from research: ic=1 ai=0.7]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Xiaohongshu unveils HELMSMAN for cost-effective billion-scale vector search

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Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
Publication of a new infrastructure system at a research conference. [lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
infra, product
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
78 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

Full methodology in our editorial standards.

COVERAGE [1]

  1. Pandaily TIER_1 English(EN) · [email protected] (Pandaily) ·

    Xiaohongshu Engine Team Publishes HELMSMAN at OSDI 2026: Reinventing Billion-Scale Vector Search Infrastructure on All-Flash Storage

    Xiaohongshu engine team publishes HELMSMAN at OSDI 2026, replacing 35K CPU cores and 350TB DRAM with 40 all-flash servers for billion-scale vector search at 90% cost reduction.

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