发表机构
Taobao & Tmall Group of Alibaba（阿里巴巴淘宝及天猫集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电子商务多模态搜索问题，提出派利淘-多模态搜索基础模型，通过混合语义ID、两阶段持续预训练策略和混合推理后训练管道，在淘宝派利淘平台实现显著改进，提升了商品交易总额和交易量。
AI 中文摘要
电子商务的发展使产品搜索从简单文本关键词查询转变为复杂多模态交互。现有方法面临困境：单模态专家模型孤立运行无法处理跨模态查询，通用视觉语言模型缺乏领域特定知识。本文提出派利淘-多模态搜索基础模型，引入三项关键创新：混合语义ID、两阶段持续预训练策略和混合推理后训练管道。基于文生模型构建并部署在淘宝派利淘平台，在线A/B测试取得显著改进，证明了该模型的有效性。
英文摘要
The evolution of e-commerce has fundamentally transformed how users search for products, shifting from simple text-based keyword queries to complex multimodal interactions that seamlessly combine product images, natural language descriptions, and mixed-intent instructions. However, existing approaches face a critical dilemma: single-modal specialist models, deployed independently for text retrieval, visual search, and voice recognition, operate in isolation and cannot handle cross-modal queries, while general-purpose vision-language models lack the domain-specific knowledge necessary for fine-grained product understanding, user behavior modeling, and commercial intent reasoning. In this work, we present Pailitao-MMSearch, one native e-commerce multimodal search foundation model designed to bridge this gap. Our approach introduces three key innovations: (1)HybSID (Hybrid Semantic ID);(2)a two-stage continual pre-training strategy; and (3)a hybrid reasoning post-training pipeline. Built upon Qwen and deployed on Taobao's Pailitao multimodal search platform, Pailitao-MMSearch achieves substantial improvements in online A/B testing, including up to +13.61\% in Gross Merchandise Volume (GMV) and +8.21\% in transaction volume compared to traditional multi-modal search pipeline, demonstrating the effectiveness of our native e-commerce multimodal search large language models.
CommentsTechnical Report: Pailitao-MMSearch
