A Boundary-Aware Synthesis of Llm Social Agents And Automated Program Repair: Robust Evaluation under Distribution Shift

Abstract

Progress in LLM social agents and automated program repair depends on more than accumulating favorable results. This critical synthesis connects a realistic benchmark centered on persistent LLM-based social-media agents with execution-grounded reinforcement learning with sequence- and line-level reward models and asks how measurement choices, boundary conditions, and decision costs shape the interpretation of both. 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 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.