Bridging Scientific Goals and AI Execution: A Multi-Agent Framework with Hierarchical Self-Evolution

Scientific discovery underpins progress in chemistry, biomedicine, and materials research. AI increasingly supports this work through scientific modeling, candidate design, and experiment selection. However, applying AI to a scientific task still requires domain scientists and AI specialists to jointly design the investigation, choose suitable methods, and revise them as results emerge. This repeated coordination is demanding, and experience from one investigation is not readily available to the next. We propose a multi-agent framework that addresses these difficulties through hierarchical self-evolution. The framework first generates a research workflow from the scientist's task description, then formulates an AI problem and execution plan for each scientific subproblem. Scientific and AI-method reviews guide intra-task evolution of the workflow, methods, and execution strategies. After the investigation, the framework extracts workflow playbooks, policy priors, executable skills, and scientific intuitions for reuse in subsequent tasks through inter-task evolution. Experiments across chemistry, biomedicine, and materials science show that our hierarchical self-evolving multi-agent framework supports scientific discovery by translating scientific goals into executable AI plans and improving task outcomes through intra-task and inter-task evolution. In comparisons with expert-team baselines on the evaluated tasks, the framework also achieves outcomes comparable to or better than those of collaborating domain scientists and AI experts.