Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence
Abstract
As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmented across evaluation, data management, and agent evolution, making it difficult to discover risks systematically and improve models in a continuous closed loop. In this report, we present \textbf{Safactory}, a scalable agent factory for trustworthy autonomous intelligence. Safactory integrates three tightly coupled platforms: a \textbf{Parallel Simulation Platform} for trajectory generation, a \textbf{Trustworthy Data Platform} for trajectory storage and experience extraction, and an \textbf{Autonomous Evolution Platform} for asynchronous reinforcement learning and on-policy distillation. As far as we know, Safactory is the first framework to propose a unified evolutionary pipeline for next-generation trustworthy autonomous intelligence.
Cite
@article{arxiv.2605.06230,
title = {Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence},
author = {Xinquan Chen and Zhenyun Yin and Shan He and Bin Huang and Shanzhe Lei and Pengcheng Shi and Kun Cai and Bei Chen and Bangwei Liu and Zeyu Kang and Chao Huang and Yang Zhang and Wenjie Li and Ruijun Ge and Yajie Wang and Tianshun Fang and Tianyang Xu and Yiwen Cong and Meng Jin and Gaolei Li and Xuansheng Wu and Linhan Liu and Zijing He and An Li and Yan Teng and Xin Tan and Dongrui Liu and Jing Shao and ChaoChao Lu and Ji He and Jie Li and Chunfeng Song and Jinya Xu and Fan Song and Shujie Wang and Jianmin Qian and Jie Hou and Xuhong Wang and Yingchun Wang and Hui Wang and Xia Hu},
journal= {arXiv preprint arXiv:2605.06230},
year = {2026}
}
Comments
50 pages, 21 figures