GEM: A Generative Embedding Model Bridging Reasoning and Retrieval
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
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 55%Embedex →
- PossiblePossibly related (embedding) · 51%CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning - Apple Machine Learning Research →
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “GEM: A Generative Embedding Model Bridging Reasoning and Ret” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “GEM: A Generative Embedding Model Bridging Reasoning and Ret” ≈ “steven2358/awesome-generative-ai””
- LinkedLinked via arxiv author · 85%Zhili Shen →
“GEM: A Generative Embedding Model Bridging Reasoning and Retrieval”
- LinkedLinked via arxiv author · 85%Craig Macdonald →
“GEM: A Generative Embedding Model Bridging Reasoning and Retrieval”