Meta-Learning for Scientific Hypothesis Generation and Experimental Design

Abstract

Generating novel scientific hypotheses and designing experiments often requires deep domain expertise and substantial time investment. This paper proposes a meta-learning framework to accelerate hypothesis generation and experimental design using agentic AI systems. The approach trains AI agents across diverse scientific domains (e.g., materials science, drug discovery, physics simulations), enabling rapid adaptation to new research problems with minimal labeled data. Specifically, a few-shot learning mechanism facilitates domain transfer, while a reinforcement learning (RL) engine autonomously refines experimental parameters under resource constraints. Experimental results demonstrate a 40% reduction in design iterations and 25% faster convergence on valid hypotheses, statistically validated with p < 0.05. These findings highlight the potential of meta-learning and RL to expedite scientific discovery, reduce trial-and-error, and improve research efficiency. Future work will explore formal theoretical guarantees, benchmarking against SOTA approaches, and real-world validation in laboratory settings.