Sage-A1.5：35B MoE 智能体模型从长程执行走向科学探索
Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition
35B MoE 智能体模型 Sage-A1.5 发布，在 A1 基础上通过自适应能力组合提升长程问题求解。它新增智能体编排与验证能力：子智能体在隔离上下文中并行协作分解任务，并通过进度检查、证据验证和反思式策略修订识别无效路径。该模型还将能力扩展至开放式科学发现，支持探索性研究与可检查的研究产物。
Authors:Wenjie Lou, Tianshuo Peng, Shiyang Feng, Shengji Tang, Runmin Ma, Ruizhe Chen, Jinxin Shi, Haoyang Peng, Fangchen Yu, Zhijie Zhong, Yuhao Zhou, Yue Fan, Yixin Chen, Yiming Wu, Yiheng Wang, Yigeng Jiang, Yichen Jiang, Yi Xie, Xu Guo, Xiaohan He, Tao Hu, Songtao Huang, Siyu Zhou, Shuaiyu Zhang, Shangheng Du, Qinglong Cao, Kun Li, Jingqi Ye, Hongbin Zhou, Guohang Yan, Zongsheng Cao, Ziyi Wang, Zhuo Liu, Zhiyin Yu, Zhiwang Zhou, Zelin Tan, Yuqing Zhu, Yaoze Zhang, Yang Chen, Xuming He, Xiuliang Liang, Xingjian Guo, Xiang Zhuang, Weiping Shen, Weida Wang, Tianyuan Zhou, Tianlin Ye, Tengyuan Fan, Siyu Yan, Shuo Li, Shuchen Pu, Shengyuan Xu, Shao Zhang, Ran Wei, Qinyuan Qin, Qingyu Yang, Nianchen Deng, Meng Dai, Kuikun Liu, Kaitao Chen, Juncheng Xiao, Jiangning Liu, Jianbiao Mei, Jia Yu, Haonan He, Hangfan Zhang, Cheng Yang, Chen Zhang, Bihao Zhan, Benteng Cheng, Yuqiang Li, Yihao Liu, Yang Yang, Xujun Fu, Xiaosong Wang, Xiangyu Yue, Wenwei Zhang, Shuyue Hu, Pinlong Cai, Mianxin Liu, Lei Bai, Jie Zhou, Jie Hou, Jiamin Wu, Hui Wang, Haijun Li, Guangyu Wang, Dongzhan Zhou, Chunfeng Song, Chao Yang, Biqing Qi, Xing Xie, Dahua Lin, Bowen Zhou, Xiangchao Yan, Wenlong Zhang, Shufei Zhang, Peng Ye, Bo Zhang
Abstract:In this report, we introduce Sage-A1.5, a 35B Mixture-of-Experts agentic model that builds on A1 to advance long-horizon problem solving through adaptive capability composition. Compared with A1, Sage-A1.5 learns not only to solve individual tasks, but also to organize work and revise its approach during execution. Sage-A1.5 highlights three improvements: agentic orchestration, verification, and open scientific discovery. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently and supporting efficient execution while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing the agent to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We showcase these capabilities through complicated agentic tasks and multidisciplinary scientific applications. Besides, we hope Sage-A1.5 provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
| Comments: | Token-plan for Sage-A1.5: this https URL model checkpoints: this https URL |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.28578 [cs.AI] |
| (or arXiv:2610.28578v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28578 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bo Zhang [view email]
[v1]
Tue, 20 Oct 2026 17:32:48 UTC (11,369 KB)
来源：HuggingFace Daily Papers · arxiv.org