LeakBench: Auditing Reward Hacking through Prediction-Time Leakage in Clinical Modeling Agents
Autonomous clinical-modeling agents can optimize predictive performance while violating conditions that make a model usable. We introduce LeakBench, an executable, guideline-derived audit suite that tests agent decisions for prediction-time leakage, calibration, site-level evaluation, support awareness, and decision-curve net benefit. LeakBench combines a synthetic task with known failure structure and two real-data settings: MIMIC-IV (430k admissions) and governed multi-hospital inpatient record front-page (MHFP) data. Our primary analysis uses reward hacking descriptively, without attributing intent: an agent selects prediction-time-invalid inputs because they improve its validation rew...
| Authors | Xiaoyuan Bao, Jingsi Jiang, Jiao Shan et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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