---
title: "DaV-Gen: End-to-End Generative Retrieval via Draft-and-Verify"
authors:
  - "Meng Zhao"
  - "Chunmei Liu"
  - "Qinyong Wang"
doi: "10.24963/ijcai.2026/363"
url: "https://ijcai.org/proceedings/2026/363"
pdf: "https://ijcai.org/proceedings/2026/0363.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3268-3275"
track: "Main Track"
keywords:
  - "Natural Language Processing: Language generation"
  - "Data Mining: Collaborative filtering"
  - "Data Mining: Recommender systems"
---

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

Meng Zhao, Chunmei Liu, Qinyong Wang

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3268-3275. DOI: [10.24963/ijcai.2026/363](https://doi.org/10.24963/ijcai.2026/363)

## 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 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.