Evidence Alignment and Transfer Boundaries in Llm Social Agents And Resource-Efficient V2X Perception

Abstract

The literature on LLM social agents and resource-efficient V2X perception contains a recurring tension between methodological novelty and evidential comparability. By reading a realistic benchmark centered on persistent LLM-based social-media agents alongside motion-aware approximate temporal memory for energy-efficient neural perception, 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 behavioral realism, memory, interaction effects, safety, benchmark validity and deliberately separates mechanistic interpretation from performance ranking, because the latter can conceal incompatible experimental or operational conditions. Comparison reveals recurring trade-offs among behavioral realism, memory, and interaction effects. 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 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.