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

Researchers have officially introduced InternAgent-1.5, a sophisticated unified agentic framework designed to automate the end-to-end process of scientific discovery, through a technical paper published on the arXiv preprint server on February 13, 2025. This technological breakthrough aims to bridge the gap between computational theory and empirical laboratory experimentation by providing an autonomous system capable of managing long-horizon research tasks that traditionally require human intervention. Developed to address the increasing complexity of modern science, the framework serves as a comprehensive tool for systematic innovation across diverse scientific domains. The core of InternAgent-1.5 is its unique tripartite architecture, which integrates three specialized subsystems dedicated to generation, verification, and evolution. This structure allows the AI to not only propose new scientific hypotheses or computational models but also to rigorously test them and refine its approach based on the resulting data. By coordinating these three functions, the system can self-correct and iterate on complex problems, effectively simulating the cycle of the scientific method without constant manual guidance. Beyond basic task execution, the framework is equipped with foundational capabilities for deep research and solution optimization. One of its most significant features is a long-horizon memory module, which enables the agent to retain and utilize information across extended periods and multiple stages of a project. This capability is crucial for long-term scientific endeavors where context from early experiments must inform later decision-making, ensuring that the discovery process remains coherent and goal-oriented. By unifying computational and empirical workflows, InternAgent-1.5 represents a significant step toward fully autonomous laboratories and digital research assistants. The system's ability to operate across different domains suggests that AI is moving past simple data analysis toward becoming a proactive partner in the discovery of new knowledge. This development could substantially accelerate the pace of research in fields ranging from materials science to drug discovery by reducing the time spent on routine experimental design and data synthesis.