Can the Agent Know? Certifying Answerability in Power-Grid Fault Diagnosis

Predictive confidence does not establish whether the available evidence uniquely determines an answer. In fault diagnosis, distinct physical events can remain indistinguishable under sparse sensing, making a confident prediction unjustified even when it matches the recorded label. We study this distinction through power-grid line-outage diagnosis and introduce ObsGuard, a training-free answerability layer based on physical feasibility. Given network topology, sparse phasor measurements, and bounded injection and measurement uncertainties, ObsGuard uses linear programming to construct the set of compatible contingencies. It returns a unique answer only when this set is a singleton, otherwise reporting feasible alternatives or model mismatch. Under the specified DC observation model and valid uncertainty bounds, we establish true-contingency inclusion, singleton correctness, and monotonic set refinement as consistent measurements are added. We also introduce GridID, a multimodal benchmark with matched sensor views that hold the underlying event fixed while varying information availability. On 3,072 simulated scenarios from held-out IEEE 57- and 118-bus networks, ObsGuard achieves 100% true-contingency coverage. Increasing sensor coverage from 10% to 100% raises singleton coverage from 24.7% to 93.8% and reduces mean feasible-set size from 40.3 to 1.15. A learned confidence rejector issues singleton predictions on 44.4–50.0% of physically ambiguous cases, whereas ObsGuard avoids such commitments by construction. These results establish a concrete distinction between statistical confidence and evidence-based identifiability, supporting diagnostic systems that determine whether an answer is warranted before committing to it.