arXiv:2611.09942v1 Announce Type: cross
Abstract: Clinical agents must retrieve patient-specific evidence and correctly update records despite unreliable tool responses. We introduce MedToolFault, a fault-injection benchmark linking six tool-failure classes to answer correctness, required state changes and forbidden mutations in public clinical-agent tasks. We also introduce MedGuard, a tool-layer method that verifies read evidence and attributes ambiguous writes through public interfaces. Across 127,861 formal episode evaluations, we assess fault recovery, mechanism controls, task expansion, constrained tool budgets and external question answering. Across nine model configurations, unguarded agents fail in 32.2-67.8% of fault-injected evaluations on tasks each configuration solves without faults. MedGuard improves paired safe success by 13.7-46.6 percentage points on these tasks.
Abstract: Clinical agents must retrieve patient-specific evidence and correctly update records despite unreliable tool responses. We introduce MedToolFault, a fault-injection benchmark linking six tool-failure classes to answer correctness, required state changes and forbidden mutations in public clinical-agent tasks. We also introduce MedGuard, a tool-layer method that verifies read evidence and attributes ambiguous writes through public interfaces. Across 127,861 formal episode evaluations, we assess fault recovery, mechanism controls, task expansion, constrained tool budgets and external question answering. Across nine model configurations, unguarded agents fail in 32.2-67.8% of fault-injected evaluations on tasks each configuration solves without faults. MedGuard improves paired safe success by 13.7-46.6 percentage points on these tasks.
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MedToolFault and MedGuard: evaluating and improving clinical agent reliability