Persona-based Multi-Agent Collaboration for Brainstorming
This addresses the need for more effective brainstorming in AI systems, but it is incremental as it builds on prior multi-agent collaboration work.
The paper tackled the problem of improving brainstorming outcomes by proposing a persona-based multi-agent collaboration framework, showing that persona choice shapes idea domains and collaboration mode shifts diversity, with multi-agent persona-driven brainstorming producing idea depth and cross-domain coverage.
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.