FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics

In three linesFML-Bench is a benchmark of 18 ML tasks across 10 domains evaluating 6 AI research agents. Key findings: strategy complexity alone does not ensure performance (greedy hill-climber matches tree-search); effectiveness depends on improvement opportunity structure; an adaptive agent detecting stagnation outperforms others. Includes 12 process-level behavioral metrics.

Summary generated by Claude — human-verified