Evidence Alignment and Transfer Boundaries in Llm Social Agents And Automated Program Repair

Abstract

The literature on LLM social agents and automated program repair contains a recurring tension between methodological novelty and evidential comparability. By reading a realistic benchmark centered on persistent LLM-based social-media agents alongside execution-grounded reinforcement learning with sequence- and line-level reward models, this article clarifies the conditions under which their conclusions can support a common research argument. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: behavioral realism, memory, interaction effects, safety, benchmark validity. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. The synthesis shows that behavioral realism cannot be interpreted independently of memory, while interaction effects 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 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.