medddc-eval-2a7521da·1 events·first seen Aliases: MedDDC-Eval
Researchers introduce MedDDC-Eval, a benchmark framework that decouples history-elicitation quality from diagnosis generation in multi-turn medical consultation agents, using a shared frozen reader to hold the history-to-diagnosis mapping constant. The paper demonstrates that varying only the diagnostic reader shifts diagnosis F1 by 2.2–19.0 points and reverses 18–36% of pairwise policy orderings, exposing confounds in coupled evaluation. The authors also apply Group Relative Policy Optimization (GRPO) to post-train Qwen3-32B using diagnosis-result and trajectory feedback, achieving 9.7 and 4.6 total-score point improvements on two evaluation splits. The work addresses a methodological gap in evaluating medical dialogue agents by enabling controlled attribution of policy quality.