Evidence Alignment and Transfer Boundaries in Resource-Efficient V2X Perception And Reinforcement Learning For Software Reasoning

Abstract

Research spanning resource-efficient V2X perception and reinforcement learning for software reasoning increasingly joins methods that were developed for different objects and decisions. Here, motion-aware approximate temporal memory for energy-efficient neural perception is compared with curriculum-aware reinforcement learning over code-lineage graphs to determine which claims can travel across those boundaries and which remain context dependent. 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. 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 resulting framework supports reproducible comparison while preserving differences between study designs, and it identifies concrete points at which transfer claims should be narrowed or retested.