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New DrIG framework enables universal multimodal retrieval with dual-role identifiers

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced DrIG, a novel Generative framework for universal multimodal retrieval that utilizes Dual-role Identifiers. This framework addresses challenges in generative information retrieval, such as constrained decoding and unimodal limitations, by enabling instruction-aware retrieval across text, images, and mixed formats. DrIG assigns each candidate a single identifier that functions both sequentially for autoregressive decoding and as a set for prefix-independent relevance priors, improving accuracy and efficiency compared to existing methods. AI

IMPACT This framework could advance multimodal search capabilities and improve the efficiency of information retrieval systems.

RANK_REASON The cluster contains a research paper detailing a new framework for multimodal retrieval.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New DrIG framework enables universal multimodal retrieval with dual-role identifiers

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The cluster contains a research paper detailing a new framework for multimodal retrieval.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Kaipeng Li, Haitao Yu, Xuanchen Zhou ·

    Generative Universal Multimodal Retrieval with Dual-role Identifiers

    arXiv:2608.12987v1 Announce Type: cross Abstract: Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce the identifiers of relevant items directly. Despite …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xuanchen Zhou ·

    Generative Universal Multimodal Retrieval with Dual-role Identifiers

    Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce the identifiers of relevant items directly. Despite its promise, a number of open challenges still rem…

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