Xiaoyang Wang

The work introduces ProbeGuard, a certified abstention framework that decides from how multi-round consensus formed rather than from terminal agreement: it derives trajectory features from the system's execution log, checks unanimous first-round votes with rationale semantic entropy and an active counter-evidence probe, and converts these scores into a distribution-free selective-risk bound via stratified Learn-then-Test calibration; on MedQA terminal agreement scores 0.512 AUROC while the process score reaches 0.696, and 13.4% of unanimous votes are wrong, with the certified rule answering 55.3% of that layer at 9.0% observed risk.

The work introduces ProbeGuard, a certified abstention framework that decides from how multi-round consensus formed rather than from terminal agreement: it derives trajectory features from the system's execution log, checks unanimous first-round votes with rationale semantic entropy and an active counter-evidence probe, and converts these scores into a distribution-free selective-risk bound via stratified Learn-then-Test calibration; on MedQA terminal agreement scores 0.512 AUROC while the process score reaches 0.696, and 13.4% of unanimous votes are wrong, with the certified rule answering 55.3% of that layer at 9.0% observed risk.