OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
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| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911168899055616 |
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| author | Dai, Sunhao Tang, Jiakai Wu, Jiahua Wang, Kun Zhu, Yuxuan Chen, Bingjun Hong, Bangyang Zhao, Yu Fu, Cong Wu, Kangle Ni, Yabo Zeng, Anxiang Wang, Wenjie Chen, Xu Xu, Jun Ng, See-Kiong |
| author_facet | Dai, Sunhao Tang, Jiakai Wu, Jiahua Wang, Kun Zhu, Yuxuan Chen, Bingjun Hong, Bangyang Zhao, Yu Fu, Cong Wu, Kangle Ni, Yabo Zeng, Anxiang Wang, Wenjie Chen, Xu Xu, Jun Ng, See-Kiong |
| contents | Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, which enriches raw input queries with contextual cues to better elicit model capabilities, and multi-step reasoning, which iteratively refines model outputs through intermediate reasoning paths. However, these two mechanisms and their potential to unlock substantial improvements remain largely underexplored in industrial ranking systems.
In this paper, we propose OnePiece, a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models of industrial cascaded pipelines. OnePiece is built on a pure Transformer backbone and further introduces three key innovations: (1) structured context engineering, which augments interaction history with preference and scenario signals and unifies them into a structured tokenized input sequence for both retrieval and ranking; (2) block-wise latent reasoning, which equips the model with multi-step refinement of representations and scales reasoning bandwidth via block size; (3) progressive multi-task training, which leverages user feedback chains to effectively supervise reasoning steps during training. OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics, including over $+2\%$ GMV/UU and a $+2.90\%$ increase in advertising revenue. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System Dai, Sunhao Tang, Jiakai Wu, Jiahua Wang, Kun Zhu, Yuxuan Chen, Bingjun Hong, Bangyang Zhao, Yu Fu, Cong Wu, Kangle Ni, Yabo Zeng, Anxiang Wang, Wenjie Chen, Xu Xu, Jun Ng, See-Kiong Information Retrieval Artificial Intelligence Computation and Language Despite the growing interest in replicating the scaled success of large language models (LLMs) in industrial search and recommender systems, most existing industrial efforts remain limited to transplanting Transformer architectures, which bring only incremental improvements over strong Deep Learning Recommendation Models (DLRMs). From a first principle perspective, the breakthroughs of LLMs stem not only from their architectures but also from two complementary mechanisms: context engineering, which enriches raw input queries with contextual cues to better elicit model capabilities, and multi-step reasoning, which iteratively refines model outputs through intermediate reasoning paths. However, these two mechanisms and their potential to unlock substantial improvements remain largely underexplored in industrial ranking systems. In this paper, we propose OnePiece, a unified framework that seamlessly integrates LLM-style context engineering and reasoning into both retrieval and ranking models of industrial cascaded pipelines. OnePiece is built on a pure Transformer backbone and further introduces three key innovations: (1) structured context engineering, which augments interaction history with preference and scenario signals and unifies them into a structured tokenized input sequence for both retrieval and ranking; (2) block-wise latent reasoning, which equips the model with multi-step refinement of representations and scales reasoning bandwidth via block size; (3) progressive multi-task training, which leverages user feedback chains to effectively supervise reasoning steps during training. OnePiece has been deployed in the main personalized search scenario of Shopee and achieves consistent online gains across different key business metrics, including over $+2\%$ GMV/UU and a $+2.90\%$ increase in advertising revenue. |
| title | OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System |
| topic | Information Retrieval Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2509.18091 |