Beyond the Best Available Cause: Calibrated Verification and Triage under Hypothesis-Space Inadequacy

From clinical practice to LLM reasoning, causal diagnosis commits to the top-ranked candidate from a set of explanations. When the true cause is absent from that set, even the best candidate can be wrong. We call the absence of any task-compatible candidate hypothesis-space inadequacy, and ranking alone cannot detect it. Diagnosing it requires separating it from selection failure, where an adequate space still yields a wrong pick. We propose Causal Adequacy Verification with Evidence-guided Action and Triage (CAVEAT) with a dual-null test design, one test and one repair for each failure type. The core idea is to make inadequacy testable without knowing the true cause, by defining compatibility through the consequences the task depends on and comparing every candidate's predictions with the observed evidence. The first test assesses support for the space and requests revision when support is insufficient; the second checks the selected candidate and prompts distinguishing evidence when the choice remains unresolved. Among task-incompatible selections, calibration limits how often commitment occurs, assuming past and new failures are exchangeable under the same workflow. On a clinical benchmark, retrieved candidate lists omit the recorded diagnosis in 78% of cases. Across physical, biological, and clinical tasks, CAVEAT reduces wrong commitments, with retained usefulness depending on the evidence. In controlled LLM reasoning, test-guided repair yields more correct commitments than either fixed repair, even with imperfect candidate supply.