# DFM-Bench

Whether research agents can keep improving from their own research experience depends on turning a hypothesis into an investigation that can actually run and on knowing which contribution to reinforce. DiscoveryRSI builds research trees of hypotheses through multi-agent evolutionary search, keeps the evidence and decisions behind every proposal and revision, and uses those records to train the model and refine the workflow. After two rounds of self-improvement, mean hypothesis quality rises from 4.86 to 5.39, with gains concentrated in testability: falsifiability from 4.00 to 6.20 and data resolvability from 3.73 to 4.99.

Whether research agents can keep improving from their own research experience depends on turning a hypothesis into an investigation that can actually run and on knowing which contribution to reinforce. DiscoveryRSI builds research trees of hypotheses through multi-agent evolutionary search, keeps the evidence and decisions behind every proposal and revision, and uses those records to train the model and refine the workflow. After two rounds of self-improvement, mean hypothesis quality rises from 4.86 to 5.39, with gains concentrated in testability: falsifiability from 4.00 to 6.20 and data resolvability from 3.73 to 4.99.