Reframing Resource-Efficient V2X Perception And Latent Policy Optimization: Measurement Chains and Validation Design

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. Two target papers are triangulated against 12 locally validated publications. The comparison follows temporal redundancy, token selection, quantization, communication latency, safety assurance and deliberately separates mechanistic interpretation from performance ranking, because the latter can conceal incompatible experimental or operational conditions. Comparison reveals recurring trade-offs among temporal redundancy, token selection, and quantization. These trade-offs do not support a universal ranking; instead, they identify the operating envelope within which each method remains credible and the perturbations most likely to expose fragile conclusions. 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.