Coordinating heterogeneous large language models (LLMs) can improve instruction following, yet current multi-agent systems often depend on manual role choices or repeated calls to one backbone. This makes it difficult to separate complementary model behavior from model bias and verbosity. We introduce Role-Specialized Multi-Agent Collaboration (RS-MA), a prompting-only framework that assigns candidate LLMs to four interpretable roles: Judge, Summarizer, Ideator, and Reasoner. RS-MA estimates role fit with audition prompts, aggregates role-specific rankings with Borda scoring, and uses the selected assignment as an inspectable role map. A refinement phase records judgment gaps and targeted edits before synthesis. On AlpacaEval 2.0, audition-selected RS-MA reaches an 88.86% length-controlled win rate, and a validated role map reaches 90.72%. A full 805-example ablation shows that single-model reuse attains high raw preference through much longer outputs, whereas the validated map improves length-controlled quality with moderate length growth. MT-Bench and FLASK further indicate preserved dialogue quality and balanced fine-grained skills. The results support explicit role assignment as a practical mechanism for combining heterogeneous LLMs when quality, completeness, and inspectable evidence are jointly required.

Recommendation explanation systems have become increasingly vital with the widespread adoption of recommender systems. However, existing recommendation explanation evaluation benchmarks suffer from limited item diversity, impractical user profiling requirements, and unreliable and unscalable evaluation protocols. We present ALERT, a model-agnostic recommendation explanation evaluation benchmark. The benchmark comprises three main contributions: 1) a diverse dataset encompassing 15 Amazon e-commerce categories with 2,761 user-item interactions, incorporating implicit preferences through purchase histories;2) two novel LLM-powered automatic evaluators that enable scalable and human-preference aligned evaluation of explanations; and 3) a robust divide-and-aggregate approach that synthesizes multiple LLM judgments, achieving 70% concordance with expert human evaluation and substantially outperforming existing methods.ALERT facilitates comprehensive evaluation of recommendation explanations across diverse domains, advancing the development of more effective explanation systems.