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.

翻译：我们介绍了InternAgent-1.5，这是一个为计算与实证领域端到端科学发现而设计的统一系统。该系统建立在一个结构化架构之上，该架构由三个协调的子系统构成，分别负责生成、验证与演化。这些子系统由深度研究、方案优化和长周期记忆等基础能力提供支持。该架构使得InternAgent-1.5能够在延长的发现周期中持续运行，同时保持行为的一致性与改进性。它还能使系统在单一的统一框架内协调计算建模与实验室实验。我们在GAIA、HLE、GPQA和FrontierScience等科学推理基准上评估了InternAgent-1.5，该系统取得了领先的性能，展现了强大的基础能力。除了这些基准测试，我们进一步评估了两类发现任务。在算法发现任务中，InternAgent-1.5能自主设计针对核心机器学习问题的有竞争力的方法。在实证发现任务中，它执行完整的计算或湿实验室实验，并在地球、生命、生物和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一个通用且可扩展的框架。