# The Virtual Roundtable: Multi-Agent Personas Simulating the Dynamics of Human Brainstorming

arXiv: https://arxiv.org/abs/2606.05178 · HTML: https://mlsanity.com/paper/2606.05178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https://mlsanity.com/paper/2307.05300/) (2023)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [Persona is a Double-edged Sword: Mitigating the Negative Impact of Role-playing Prompts in Zero-shot Reasoning Tasks](https://mlsanity.com/paper/2408.08631/) (2024)
- [DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers](https://mlsanity.com/paper/2502.18460/) (2025)

## Abstract (arXiv)

As AI-driven product development accelerates, the bottleneck is shifting from how we build to what we build. Traditional human brainstorming faces challenges including groupthink, echo chambers, and limited diversity. To address this, we present a multi-agentic architecture that simulates roundtable brainstorming through two phases: divergent thinking to generate diverse ideas, and convergent thinking to evaluate and rank the most promising ones. The system employs diverse AI personas that engage in roundtable discussions, guided by an agentic facilitator that steers the discussion toward productive outcomes. Personas maintain private thoughts while commenting publicly, with ideas emerging organically throughout the discussion. Per-persona quotas on idea submissions and votes promote balanced participation while producing natural rankings. Throughout the session, the system tracks each idea's lineage, capturing how concepts originate and cross-pollinate over time. We demonstrate this approach through a case study generating consumer ideas for AI smart glasses, showing (i) it produces diverse, relevant ideas with insights into their evolution; (ii) the cumulative exchange of perspectives across personas cultivates a shared context that progressively deepens the quality of discussion and the ideas produced.