A Feasibility Framework for Temporal and Conditional Consistency Checking of Sequential Chain-of-Causation Annotations

Chain-of-Causation (CoC) explanations from vision-language driving models may omit when an obligation ends. We link obligations to release conditions and condition-specific evidence, distinguish confirmed contradictions from insufficient evidence, and report the first issue event. UPPAAL observers process authored symbolic frames. In 40 legacy pairs, a memoryless checker missed cross-event errors (20/40 versus 40/40). On 144 authored scenarios, a single-obligation checker matched 96/144 expected decisions; after failure analysis, the condition-linked model matched 144/144 on the same cases, tying a finite-state machine. In 81 authored boundary cases with supplied links, retaining state matched all expected decisions; resetting state judged 41 of 44 contradictions compatible. Bounded enumeration of 35,042,928 event sequences, used to reduce circularity from case selection, matched a separate formula evaluator. Four reviewers disagreed on 27 natural scenes (Fleiss’ κ = 0.027); without independent reference labels, natural accuracy is not reported. These results demonstrate feasibility on authored inputs, not vehicle safety.