Year: 2026
Type: conference-paper
Abstract: 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.
Institutions: Sichuan University, Great Bay University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
Award: G6978877745
OpenAlex ID: W7224677558