Preprint Open access
RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking Models
Auto-research agents, LLM systems that propose, implement, train, and evaluate model changes across iterations, promise to automate applied ML's experimental loop. Over long horizons, execution accuracy is a binding constraint: a change can silently leak held-out data, omit normalization, disconnect a gradient, or leav …