DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify

• URL: http://arxiv.org/abs/2607.08365v1
• Date: Thu, 09 Jul 2026 11:22:50 GMT
• Title: DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify
• Abstract summary: textbfDaV-Gen is a novel unified solution designed to fundamentally the paradigm for both search and recommendation.<n>During training, the model is concurrently optimized for both candidate drafting and fine-grained verification.<n>This is achieved by a composite loss function that jointly trains the model on two distinct but related objectives.
• Score: 4.546259713278263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mainstream industrial information retrieval systems (e.g., search and recommendation) are usually built upon Multi-Stage Cascade Architectures (MCAs), which balance effectiveness and efficiency through a coarse-to-fine ``retrieval-ranking'' pipeline. However, the optimization objectives across different stages are substantially inconsistent, propagating or even amplifying the early-stage errors that ultimately degrade the quality of final results. While emerging end-to-end generative models offer a potential solution by unifying the pipeline, their online serving performance is severely hindered by the auto-regressive process inherited from the standard decoder-only structure. To bridge this gap, we introduce \textbf{DaV-Gen}, a novel unified solution designed to fundamentally refactor the paradigm for both search and recommendation via a ``Draft-and-Verify'' mechanism. Inspired by the process used by speculative decoding, our framework redesigns the generation task into two synergistic operations within a single model. During training, the model is concurrently optimized for both candidate drafting and fine-grained verification. This is achieved by a composite loss function that jointly trains the model on two distinct but related objectives: 1) a contrastive loss that structures the embedding space for efficient drafting, and 2) a fusion loss that combines generative likelihood with vector similarity to produce a superior verification score. This integrated training strategy equips the model with dual capabilities. At inference time, it first performs highly efficient vector-based drafting to generate a candidate set, and then verifies these candidates using the more powerful fused scoring function, thereby achieving both the speed of sparse drafting and the precision of advanced generative models within a unified, end-to-end architecture.

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