Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914371423174656 |
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| author | Chen, Lei Ju, Chen Chen, Xu Wang, Zhicheng Jiao, Yuheng Zhan, Hongfeng Li, Zhaoyang Xu, Shihao Zhao, Zhixiang Jia, Tong Li, Lin Gao, Yuan Song, Jun Lan, Jinsong Zhu, Xiaoyong Zheng, Bo |
| author_facet | Chen, Lei Ju, Chen Chen, Xu Wang, Zhicheng Jiao, Yuheng Zhan, Hongfeng Li, Zhaoyang Xu, Shihao Zhao, Zhixiang Jia, Tong Li, Lin Gao, Yuan Song, Jun Lan, Jinsong Zhu, Xiaoyong Zheng, Bo |
| contents | In this work, we presented Pailitao-VL, a comprehensive multi-modal retrieval system engineered for high-precision, real-time industrial search. We here address three critical challenges in the current SOTA solution: insufficient retrieval granularity, vulnerability to environmental noise, and prohibitive efficiency-performance gap. Our primary contribution lies in two fundamental paradigm shifts. First, we transitioned the embedding paradigm from traditional contrastive learning to an absolute ID-recognition task. Through anchoring instances to a globally consistent latent space defined by billions of semantic prototypes, we successfully overcome the stochasticity and granularity bottlenecks inherent in existing embedding solutions. Second, we evolved the generative reranker from isolated pointwise evaluation to the compare-and-calibrate listwise policy. By synergizing chunk-based comparative reasoning with calibrated absolute relevance scoring, the system achieves nuanced discriminative resolution while circumventing the prohibitive latency typically associated with conventional reranking methods. Extensive offline benchmarks and online A/B tests on Alibaba e-commerce platform confirm that Pailitao-VL achieves state-of-the-art performance and delivers substantial business impact. This work demonstrates a robust and scalable path for deploying advanced MLLM-based retrieval architectures in demanding, large-scale production environments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_13704 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search Chen, Lei Ju, Chen Chen, Xu Wang, Zhicheng Jiao, Yuheng Zhan, Hongfeng Li, Zhaoyang Xu, Shihao Zhao, Zhixiang Jia, Tong Li, Lin Gao, Yuan Song, Jun Lan, Jinsong Zhu, Xiaoyong Zheng, Bo Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition In this work, we presented Pailitao-VL, a comprehensive multi-modal retrieval system engineered for high-precision, real-time industrial search. We here address three critical challenges in the current SOTA solution: insufficient retrieval granularity, vulnerability to environmental noise, and prohibitive efficiency-performance gap. Our primary contribution lies in two fundamental paradigm shifts. First, we transitioned the embedding paradigm from traditional contrastive learning to an absolute ID-recognition task. Through anchoring instances to a globally consistent latent space defined by billions of semantic prototypes, we successfully overcome the stochasticity and granularity bottlenecks inherent in existing embedding solutions. Second, we evolved the generative reranker from isolated pointwise evaluation to the compare-and-calibrate listwise policy. By synergizing chunk-based comparative reasoning with calibrated absolute relevance scoring, the system achieves nuanced discriminative resolution while circumventing the prohibitive latency typically associated with conventional reranking methods. Extensive offline benchmarks and online A/B tests on Alibaba e-commerce platform confirm that Pailitao-VL achieves state-of-the-art performance and delivers substantial business impact. This work demonstrates a robust and scalable path for deploying advanced MLLM-based retrieval architectures in demanding, large-scale production environments. |
| title | Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search |
| topic | Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.13704 |