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 remain. First, constrained left-to-right decoding is vulnerable to prefix-level errors and local optima. Second, most prior GIR research remains largely unimodal, leaving instruction-aware retrieval across text, image, and mixed image-text items underexplored. Third, although discrete identifier-based GIR offers higher efficiency, its retrieval accuracy still lags behind that of the cutting-edge dense-vector-based re...
| Authors | Kaipeng Li, Haitao Yu, Xuanchen Zhou |
|---|---|
| Primary category | cs.IR |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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