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

2026-07-09

AI Summary

This work addresses the limitations of traditional multi-stage retrieval systems, which suffer from error propagation due to misaligned stage-wise objectives, and end-to-end generative models, whose effectiveness is hindered by the inefficiency of autoregressive decoding. To bridge this gap, the authors propose DaV-Gen, a novel framework that introduces speculative decoding to information retrieval for the first time. DaV-Gen employs a unified “draft-and-verify” mechanism that jointly performs non-autoregressive candidate drafting and generative fine-grained verification. The model is trained with a combined objective integrating contrastive and fusion losses, effectively merging vector similarity and generative likelihood scores while leveraging a structured vector space for enhanced efficiency. This approach preserves the expressive power of generative models while significantly accelerating inference, achieving both the efficiency of sparse retrieval and the accuracy of generative ranking.

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.

Problem

• information retrieval
• multi-stage cascade architectures
• end-to-end generative models
• optimization inconsistency
• online serving performance

Innovation

• Draft-and-Verify
• end-to-end generative retrieval
• speculative decoding
• contrastive loss
• fusion scoring