Researchers from Princeton, Ant Group and Stanford introduced AQuA, a two‑part agentic framework for autonomous factor discovery and model development in quantitative finance. The framework separates symbolic factor discovery from trainable model development into distinct systems that share no agents, memory, candidate spaces, or research state, each with sealed data splits and feature‑label definitions. This design counters the failure of self‑improving agents that rely on a reviewer to validate code.