BeliefOPT: Recursive Self-Evolving Agents for Autonomous Research

Research is the disciplined conversion of experience into belief. Autonomous research agents accumulate experience through iterative experimentation, creating an opportunity to continually improve their capacity for discovery. Realizing this potential requires turning experimental outcomes into reliable knowledge and revising that knowledge as new evidence emerges. Yet lessons inferred from past success may reflect contingent advantages or unreliable measurements, allowing plausible but unsupported claims to shape subsequent research. We introduce BeliefOPT, a framework for autonomous research that organizes experience into falsifiable, revisable beliefs about when and why one choice should outperform an alternative. A research scientist distills experimental evidence into beliefs, while an engineering squad uses them to explore candidate solutions and returns evidence that informs their subsequent selection and revision. This interaction couples the evolution of the belief store with the research process it guides, supported by an evaluator that expands test coverage as experimentation proceeds. We use GPU kernel optimization as a representative setting for studying autonomous research. On 26 GPU kernels from SOL-ExecBench on NVIDIA B200, BeliefOPT operates from an empty belief store without human intervention, achieving a geometric-mean speedup of 23.9x, exceeding OpenEvolve by 24% and CodeEvolve by 6%. Exploratory ablations suggest that organizing relationships among beliefs improves search at larger budgets, while accumulating additional experience or seeding expert advice does not consistently help. These findings support an approach to autonomous research in which accumulated knowledge remains open to testing and revision, allowing experimental evidence to continually reshape both what an agent believes and how it investigates.