InternAgent-1.5：面向长周期自主科学发现的一体化智能体框架

InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery

February 9, 2026

作者: Shiyang Feng, Runmin Ma, Xiangchao Yan, Yue Fan, Yusong Hu, Songtao Huang, Shuaiyu Zhang, Zongsheng Cao, Tianshuo Peng, Jiakang Yuan, Zijie Guo, Zhijie Zhong, Shangheng Du, Weida Wang, Jinxin Shi, Yuhao Zhou, Xiaohan He, Zhiyin Yu, Fangchen Yu, Qihao Zheng, Jiamin Wu, Mianxin Liu, Chi Zhang, Shaowei Hou, Shuya Li, Yankai Jiang, Wenjie Lou, Lilong Wang, Zifu Wang, Jiong Wang, Wanghan Xu, Yue Deng, Dongrui Liu, Yiheng Wang, Wenlong Zhang, Fenghua Ling, Shufei Zhang, Xiaosong Wang, Shuangjia Zheng, Xun Huang, Siqi Sun, Shuyue Hu, Peng Ye, Chunfeng Song, Bin Wang, Conghui He, Yihao Liu, Xin Li, Qibin Hou, Tao Chen, Xiangyu Yue, Bin Wang, Liang He, Dahua Lin, Bowen Zhou, Bo Zhang, Lei Bai

cs.AI

摘要

我们推出InternAgent-1.5——一个专为计算与实验科学领域端到端科学发现设计的统一系统。该系统采用由生成、验证和演进三个协同子系统构成的架构，并依托深度研究、方案优化与长周期记忆等基础能力支撑。该架构使InternAgent-1.5能在长周期发现过程中持续运行，同时保持行为一致性并实现性能提升，还能在统一系统内协调计算建模与实验室实验。我们在GAIA、HLE、GPQA和FrontierScience等科学推理基准测试中评估该系统，其领先表现展现了强大的基础能力。超越基准测试范畴，我们进一步评估了两类发现任务：在算法发现任务中，系统能自主设计针对核心机器学习问题的竞争性方法；在实验发现任务中，可执行完整计算或湿实验流程，并在地球科学、生命科学、生物及物理领域产出科学发现。总体而言，这些结果表明InternAgent-1.5为自主科学发现提供了通用且可扩展的框架。

English

We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution. These subsystems are supported by foundational capabilities for deep research, solution optimization, and long horizon memory. The architecture allows InternAgent-1.5 to operate continuously across extended discovery cycles while maintaining coherent and improving behavior. It also enables the system to coordinate computational modeling and laboratory experimentation within a single unified system. We evaluate InternAgent-1.5 on scientific reasoning benchmarks such as GAIA, HLE, GPQA, and FrontierScience, and the system achieves leading performance that demonstrates strong foundational capabilities. Beyond these benchmarks, we further assess two categories of discovery tasks. In algorithm discovery tasks, InternAgent-1.5 autonomously designs competitive methods for core machine learning problems. In empirical discovery tasks, it executes complete computational or wet lab experiments and produces scientific findings in earth, life, biological, and physical domains. Overall, these results show that InternAgent-1.5 provides a general and scalable framework for autonomous scientific discovery.