Large language model (LLM) correction is often evaluated by whether a model changes its answer after receiving new information, but answer change alone does not show that invalidated premises stop governing later reasoning. This paper defines warranted downstream correction (WDC) as selective, evidence-justified revision that propagates through materially dependent claims and actions, preserves independently supported claims, and remains active in subsequent reasoning while remaining open to later evidence. From a cognitive-systems perspective, WDC is treated as a behavioral property linking evidence integration, causal commitment, revision, and action in artificial systems. The empirical section synthesizes an investigation program originally designed to study premature causal closure. In a frozen three-case MAIB replication, two cases favored a minimal evidence-calibration prompt and one was inconclusive. In a later longitudinal analysis of one AAIB aviation investigation (G-WNSB), three of four ordered evidence slices favored the Minimal condition and one was a tie; the slices are not independent cases. Across six distinct usable investigation worlds, five directionally favored Minimal and one was inconclusive. The strongest recurring difference concerned causal commitment calibration rather than factual recall: interpretations were more often preserved at their warranted evidential strength instead of being promoted into stronger causal claims. A boundary case showed that better commitment discipline does not replace missing domain-specific computation. These findings motivate, but do not directly validate, the full WDC construct.