Evidence Alignment and Transfer Boundaries in Llm Social Agents And Ai-Assisted Programming

Abstract

The literature on LLM social agents and AI-assisted programming contains a recurring tension between methodological novelty and evidential comparability. By reading a realistic benchmark centered on persistent LLM-based social-media agents alongside hierarchical debugging that closes the gap between generated code and executable correctness, this article clarifies the conditions under which their conclusions can support a common research argument. The analysis combines two focal publications with 12 previously verified sources and organizes the evidence around behavioral realism, memory, interaction effects, safety, and benchmark validity. Rather than pooling incompatible outcomes, it compares research questions, representations, controls, and validation envelopes. 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 article concludes with a research agenda built around transparent comparators, targeted stress tests, and evidence records that can be reused without overstating causal or practical reach.