We demonstrate the importance of persona-based multi-agents brainstorming for both diverse topics and subject matter ideation. Prior work has shown that generalized multi-agent collaboration often provides better reasoning than a single agent alone. In this paper, we propose and develop a framework for persona-based agent selection, showing how persona domain curation can improve brainstorming outcomes. Using multiple experimental setups, we evaluate brainstorming outputs across different persona pairings (e.g., Doctor vs VR Engineer) and A2A (agent-to-agent) dynamics (separate, together, separate-then-together). Our results show that (1) persona choice shapes idea domains, (2) collaboration mode shifts diversity of idea generation, and (3) multi-agent persona-driven brainstorming produces idea depth and cross-domain coverage.
BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation
Department of Electrical Engineering, National Taiwan University（国立台湾大学电子工程系）；Department of Computer Science & Information Engineering, National Taiwan University（国立台湾大学计算机科学与信息工程系）；Graduate Institute of Communication Engineering, National Taiwan University（国立台湾大学通信工程研究所）；CSAIL, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）
