By treating hypothesis generation as an iterative learning problem with explicit feedback loops, AI can progressively improve the quality and testability of scientific claims, achieving better alignment with real published research than existing models.
DiscoveryRSI is a framework that helps AI systems generate and improve scientific hypotheses through recursive learning. It uses multi-agent search to develop hypothesis trees, tests them against criteria, and learns from both successes and failures to refine future research workflows. The system shows measurable improvements in hypothesis quality across life-science domains.