OnePiece: Bringing Context Engineering and Reasoning to Industrial
  Cascade Ranking System
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.
68d20b8a1ca7156988a8ece7
Sunhao Dai
claimed_verified
2025-09-23T10:06:54.519Z
64db88993725f8d9a908c077
https://cdn-avatars.huggingface.co/v1/production/uploads/64db88993725f8d9a908c077/JZEGk0kius6mrANlwOWw9.jpeg
Sunhao Dai
user
KID-22
68d20b8a1ca7156988a8ece8
Jiakai Tang
claimed_verified
2025-09-23T10:06:57.620Z
65acfb3a14e6582c30b4ce76
https://cdn-avatars.huggingface.co/v1/production/uploads/65acfb3a14e6582c30b4ce76/RhEhePggBtyM0RIIqXQen.jpeg
TangJiakai
user
TangJiakai5704
68d20b8a1ca7156988a8ece9
Jiahua Wu
68d20b8a1ca7156988a8ecea
Kun Wang
claimed_verified
2025-09-23T10:41:11.576Z
67e21ce15d02fcdf8d7abd39
/avatars/01f63e591d327f3d54f51460a599dc80.svg
kun
user
vicowang
68d20b8a1ca7156988a8eceb
Yuxuan Zhu
claimed_verified
2025-09-23T10:07:00.003Z
641f18a673cfc036ddbaeccf
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/RtOK_qV71NM87A67XFJCN.png
Yuxuan Zhu
user
Ethan7
68d20b8a1ca7156988a8ecec
Bingjun Chen
68d20b8a1ca7156988a8eced
Bangyang Hong
68d20b8a1ca7156988a8ecee
Yu Zhao
68d20b8a1ca7156988a8ecef
Cong Fu
claimed_verified
2025-09-26T12:23:22.649Z
665e8515045fcbf12b99558a
/avatars/e4ea26703d9dd2c8f19412ccd81d2cae.svg
Fu Cong
user
fcthebrave
68d20b8a1ca7156988a8ecf0
Kangle Wu
68d20b8a1ca7156988a8ecf1
Yabo Ni
68d20b8a1ca7156988a8ecf2
Anxiang Zeng
68d20b8a1ca7156988a8ecf3
Wenjie Wang
68d20b8a1ca7156988a8ecf4
Xu Chen
68d20b8a1ca7156988a8ecf5
Jun Xu
68d20b8a1ca7156988a8ecf6
See-Kiong Ng
OnePiece: Bringing Context Engineering and Reasoning to Industrial
  Cascade Ranking System
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.
OnePiece integrates LLM-style context engineering and reasoning into industrial search and recommendation systems, achieving significant improvements in key business metrics.
Transformer architectures Deep Learning Recommendation Models (DLRMs) context engineering multi-step reasoning structured context engineering block-wise latent reasoning progressive multi-task training