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New PUMA method sparsifies multimodal embeddings for efficient retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PUMA, a novel method for post-hoc sparsification of universal multimodal embeddings. This technique significantly reduces the memory and inference costs associated with multimodal retrieval by mapping dense embeddings to compact sparse codes without retraining the backbone model. PUMA has demonstrated effectiveness on benchmarks using the Qwen3-VL-Embedding-2B model, achieving comparable or improved retrieval performance while reducing storage by 8-16x and increasing speed by up to 25x. AI

IMPACT Enables more efficient and scalable multimodal retrieval systems by reducing computational costs.

RANK_REASON The cluster describes a new method presented in an arXiv paper for improving the efficiency of multimodal embeddings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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

New PUMA method sparsifies multimodal embeddings for efficient retrieval

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The cluster describes a new method presented in an arXiv paper for improving the efficiency of multimodal embeddings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Tommaso Di Noia ·

    PUMA: Post-Hoc Sparsification of Universal Multimodal Embeddings for Efficient Retrieval

    Universal multimodal embedders enable retrieval across text, image, and combined queries, but their dense representations incur high memory and inference costs. Post-hoc sparsification could reduce these costs but remains underexplored for multimodal retrieval. We introduce PUMA,…

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