GEM: A Generative Embedding Model Bridging Reasoning and Retrieval

2026-08-13

AI Summary

Traditional retrievers rely on surface-level matching and struggle to capture complex user intent, resulting in a semantic gap between queries and documents. This work proposes the Generative Embedding Model (GEM), which uniquely integrates explicit reasoning into the embedding architecture: it first employs a large language model to perform intent and relevance reasoning over the query, then appends embedding tokens that encode this enhanced contextual representation for retrieval. The unified framework enables test-time computational scaling through prompting and significantly outperforms non-reasoning baselines on both reasoning-intensive and instruction-following retrieval tasks. Notably, GEM achieves performance comparable to that of substantially larger models while operating at a smaller scale.

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. \zhili{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.

Problem

• retrieval
• reasoning
• user intent
• embedding
• information need

Innovation

• generative embedding
• reasoning-augmented retrieval
• intent reasoning
• unified generation-embedding
• test-time compute scaling