How to Train a Real-World Silicon Concierge? Internalizing Complex Business Workflow to Only OneModel
Chang Liu, Chaoyang Ning, Dayi Jiang, Enrui Gu, Fang Ran, Hongyan Xue, Huaqing Li, Hui Cai, Jia Liu, Jiang-Ming Yang, Jianshe Li, Jiawei Luo, Jin Zhou, Leshen Zhu, Lihui Chen, Liying Ma, Lyuxin Xue, Mengjian Ji, Ruijia Xu, Wei Ren, Wei Wu, Xiaoling Qu, Xiaoyun Feng, Xin Zhang, Xixie Zhou, Xuanwei Hu, Yan Chen, Yichao Wang, Yongqi Tong, Yu Liu, Yuhong Zhou, Zemin Sun, Zhenwen Xu, Zhiling Liu, Zifan Wang
Abstract
Traditional industrial agents rely on modular pipelines, including Router, Retriever, Planner, Executor, Responder, Reviewer, and other components. These systems often fracture into a labyrinth of ad-hoc patches, leading to cascading errors and high latency. We propose OneModel, an applicable paradigm shift from external workflows to internalized knowledge representation. Unlike modular systems that slice fluid user intents into static steps, OneModel consolidates complex business logic and SOPs directly into the model parameters. Through Continual Pre-training (CPT) and logic-compilation SFT, we transform fragmented business rules into intuitive model reasoning within a unified attention space. Deployed in our global financial service system, OneModel effectively breaks the trade-off between latency, accuracy, and complexity. Online A/B testing demonstrates an end-to-end latency reduction of more than 50 percent, from 18.7 seconds to 8.0 seconds, while the Intelligent Resolution Rate (IRR) increases from 64.3 percent to 83.3 percent. The results show that OneModel can replace brittle engineering logic with internalized cognitive intuition, offering a scalable blueprint for transitioning industrial agents from complex, error-prone workflows to unified model architectures.
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Classified with taxonomy v2 on Sat, 5 Sept 2026.