Agent-based models capture how local decisions give rise to collective phenomena, but are rarely learnable from real data. This study introduces a differentiable framework that reconstructs individual behavioral rules while preserving interaction structure and stochasticity through the integration of graph neural networks and diffusion models. The approach bridges mechanistic modeling and machine learning, enabling empirical testing of theories about decentralized and emergent social and ecological systems.