# Cross-Fitted Proposer Feedback

The work diagnoses a failure mode it calls co-cheating in self-evolving search agents, where a proposer and solver increasingly agree on shared errors so internal reward improves without matching external correctness, and introduces multi-sample verification (MSV) plus the main method CrossFit (splitting the proposer's source documents into folds and scoring each fold's questions with an auxiliary solver trained only on the other fold), reducing false-agreement mass from 6.1%/8.8% to 3.0%/3.7% on Qwen3.5-4B/9B and improving seven-benchmark average Cover-EM over standard coupled self-evolution by 8.8/8.4 points.

The work diagnoses a failure mode it calls co-cheating in self-evolving search agents, where a proposer and solver increasingly agree on shared errors so internal reward improves without matching external correctness, and introduces multi-sample verification (MSV) plus the main method CrossFit (splitting the proposer's source documents into folds and scoring each fold's questions with an auxiliary solver trained only on the other fold), reducing false-agreement mass from 6.1%/8.8% to 3.0%/3.7% on Qwen3.5-4B/9B and improving seven-benchmark average Cover-EM over standard coupled self-evolution by 8.8/8.4 points.