Evidence Alignment and Transfer Boundaries in Llm Social Agents And Ai-Assisted Programming: SoMe Realistic Benchmark LLM-based and Between lines code Unraveling

Abstract

Two distinct lines of inquiry—a realistic benchmark centered on persistent LLM-based social-media agents and comparative analysis of observable coding patterns produced by people and machines—converge on a practical question for LLM social agents and AI-assisted programming: what evidence is needed before a reported advantage becomes a defensible basis for explanation, comparison, or deployment? 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. The combined literature indicates that methodological gains become actionable only when behavioral realism and memory are evaluated together and when limits associated with benchmark validity 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.