Evidence Alignment and Transfer Boundaries in Ai-Assisted Programming And Resource-Efficient V2X Perception

Abstract

Progress in AI-assisted programming and resource-efficient V2X perception depends on more than accumulating favorable results. This critical synthesis connects hierarchical debugging that closes the gap between generated code and executable correctness with motion-aware approximate temporal memory for energy-efficient neural perception and asks how measurement choices, boundary conditions, and decision costs shape the interpretation of both. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: programmer behavior, error localization, execution feedback, repair hierarchy, evaluation leakage. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. The combined literature indicates that methodological gains become actionable only when programmer behavior and error localization are evaluated together and when limits associated with evaluation leakage are explicit. This shifts the emphasis from isolated scores toward traceable chains of evidence and decision relevance. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.