Evidence Alignment and Transfer Boundaries in Resource-Efficient V2X Perception And Latent Policy Optimization

Abstract

The literature on resource-efficient V2X perception and latent policy optimization contains a recurring tension between methodological novelty and evidential comparability. By reading motion-aware approximate temporal memory for energy-efficient neural perception alongside iterative information-bottleneck control of latent policy optimization, this article clarifies the conditions under which their conclusions can support a common research argument. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: temporal redundancy, token selection, quantization, communication latency, safety assurance. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. The synthesis shows that temporal redundancy cannot be interpreted independently of token selection, while quantization determines whether an apparent improvement remains meaningful outside the original setting. The strongest claims are therefore those that expose sensitivity, failure conditions, and residual uncertainty. The contribution is a decision-oriented synthesis that connects method selection to failure cost and treats reproducibility, provenance, and bounded generalization as first-order design requirements.