GEM: A Generative Embedding Model Bridging Reasoning and Retrieval

• URL: http://arxiv.org/abs/2608.13200v2
• Date: Fri, 14 Aug 2026 09:58:29 GMT
• Title: GEM: A Generative Embedding Model Bridging Reasoning and Retrieval
• Abstract summary: We present GEM, a generative embedding model that augments retrieval through its own knowledge.<n> evaluated on reasoning-intensive and instruction-following retrieval tasks.<n> GEM's generative nature allows test-time compute scaling via prompting to further enhance retrieval performance.
• Score: 15.116615102031771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern LLMs excel at reasoning and instruction following, enabling users to express complex and diverse information needs. However, conventional retrievers largely rely on surface-level matching between queries and documents, resulting in a growing gap between how users express their needs and how retrievers interpret them. In this paper, we present GEM, a generative embedding model that augments retrieval through its own knowledge by explicitly reasoning about user intent and relevance criteria. GEM unifies generation and embedding within a single model: it first reasons over the query, then appends an embedding token to encode the enriched context for retrieval. Evaluated on reasoning-intensive and instruction-following retrieval tasks, GEM demonstrates the effectiveness of its reasoning-augmented retrieval, outperforming its non-reasoning variant and matching baselines using substantially larger models. Furthermore, GEM's generative nature allows test-time compute scaling via prompting to further enhance retrieval performance. Our code is available at: https://anonymous.4open.science/r/GEM.

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