InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
This addresses the challenge of automating long-horizon scientific research for scientists and researchers, though it appears incremental as an extension of existing agentic frameworks.
The paper tackles the problem of autonomous scientific discovery by introducing InternAgent-1.5, a unified system that achieves leading performance on benchmarks like GAIA and GPQA, and autonomously designs competitive algorithms and executes experiments across computational and empirical domains.
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.