AutoModel is an agent based architecture for the full lifecycle of industrial recommender systems. Instead of a fixed recall and ranking pipeline, AutoModel organizes recommendation as a set of interacting evolution agents with long term memory and self improvement capability. We instantiate three core agents along the axes of models, features, and resources: AutoTrain for model design and training, AutoFeature for data analysis and feature evolution, and AutoPerf for performance, deployment, and online experimentation. A shared coordination and knowledge layer connects these agents and records decisions, configurations, and outcomes. Through a case study of a module called paper autotrain, we show how AutoTrain automates paper driven model reproduction by closing the loop from method parsing to code generation, large scale training, and offline comparison, reducing manual effort for method transfer. AutoModel enables locally automated yet globally aligned evolution of large scale recommender systems and can be generalized to other AI systems such as search and advertising.

翻译：AutoModel是一种基于智能体的架构，用于工业推荐系统的完整生命周期。与固定的召回和排序流水线不同，AutoModel将推荐组织为一组具有长期记忆和自主改进能力的交互式演化智能体。我们沿着模型、特征和资源三个维度实例化三个核心智能体：用于模型设计与训练的AutoTrain、用于数据分析与特征演化的AutoFeature，以及用于性能优化、部署和在线实验的AutoPerf。一个共享的协调与知识层连接这些智能体，并记录决策、配置和结果。通过一个名为"论文自动训练"模块的案例研究，我们展示了AutoTrain如何通过闭环机制（从方法解析到代码生成、大规模训练及离线对比）自动化论文驱动的模型复现，从而减少方法迁移的人力成本。AutoModel实现了大规模推荐系统的局部自动化与全局协同演化，并可推广至搜索、广告等其他AI系统。