OnePiece: Bringing Context Engineering and Reasoning to Industrial
  Cascade Ranking System
Authors: name: Sunhao Dai; name: Jiakai Tang; name: Jiahua Wu; name: Kun Wang; name: Yuxuan Zhu; name: Bingjun Chen; name: Bangyang Hong; name: Yu Zhao; name: Cong Fu; name: Kangle Wu; name: Yabo Ni; name: Anxiang Zeng; name: Wenjie Wang; name: Xu Chen; name: Jun Xu; name: See-Kiong Ng
Published: 2025-09-22 17:59:07

Abstract: 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.