InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
AI 摘要
InternAgent-1.5是一个用于端到端自主科学发现的统一智能体框架。
主要贡献
- 提出一个用于科学发现的统一系统InternAgent-1.5
- 设计生成、验证和演化的三子系统架构
- 在多种科学推理基准和发现任务上验证了系统性能
方法论
构建三子系统架构，利用深度研究、优化和记忆能力，在计算和实验环境中进行持续自主科学发现。
原文摘要
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.