Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
Authors: name: Lei Bai, name: Zongsheng Cao, name: Yang Chen, name: Zhiyao Cui, name: Shangheng Du, name: Yue Fan, name: Shiyang Feng, name: Zijie Guo, name: Haonan He, name: Liang He, name: Xiaohan He, name: Shuyue Hu, name: Yusong Hu, name: Songtao Huang, name: Yichen Jiang, name: Hao Li, name: Xin Li, name: Dahua Lin, name: Weihao Lin, name: Fenghua Ling, name: Dongrui Liu, name: Zhuo Liu, name: Runmin Ma, name: Chunjiang Mu, name: Haoyang Peng, name: Tianshuo Peng, name: Jinxin Shi, name: Luohe Shi, name: Boyuan Sun, name: Zelin Tan, name: Shengji Tang, name: Qianyi Wang, name: Yiming Wu, name: Yi Xie, name: Xiangchao Yan, name: Jingqi Ye, name: Peng Ye, name: Fangchen Yu, name: Jiakang Yuan, name: Bihao Zhan, name: Bo Zhang, name: Chen Zhang, name: Shufei Zhang, name: Shuaiyu Zhang, name: Wenlong Zhang, name: Yiqun Zhang, name: Junpeng Zhao, name: Zhijie Zhong, name: Bowen Zhou, name: Yuhao Zhou
Organization: Intern Science

We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon. We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities. To support this goal, we build a long-horizon knowledge-action infrastructure that connects external knowledge, actions, observations, and verifier outcomes, producing agentic trajectories with an average length of 45K tokens. Based on this, we train Agents-A1 with a three-stage recipe. First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors. Second, we train domain-level teacher models to capture specialized expertise in each domain. Third, we propose a multi-teacher domain-routed on-policy distillation with salient vocabulary alignment to improve knowledge transfer efficiency across different domains, unifying six heterogeneous domains into one deployable student model. Agents-A1 achieves strong and broad performance for long-horizon agent benchmarks. Compared with 1T-parameter model such as Kimi-K2.6 and DeepSeek-V4-pro, Agents-A1 achieves leading results on SEAL-0 (56.4), IFBench (80.6), HiPhO (46.4), FrontierScience-Olympiad (79.0), and MolBench-Bind (56.8), and remains highly competitive on SciCode (44.3), HLE (47.6) and BrowseComp (75.5). We hope this work provides the community with a practical path for scaling the horizon using a 35B agent that can reach or match the performance of 1T models on long-horizon tasks.