Collabpersona: A Framework for Collaborative Decision Analysis in Persona Driven LLM-Based Multi-Agent Systems

Abstract

Large Language Model (LLM) agents have recently demonstrated impressive capabilities in single agent and adversarial settings, but their ability to collaborate effectively with minimal communication remains uncertain. We introduce CollabPersona, a simulation framework that combines persona-grounded memory with one-shot feedback to study team-based reasoning among LLM agents. In a multi-round variant of the Guess 0.8 of the Average game, agents reason entirely through structured prompts without fine-tuning. Our results show that minimal feedback significantly improves intra-team coordination and stabilizes strategic behavior, while cognitive style remains a primary driver of competitive outcomes. These findings suggest that lightweight scaffolding can elicit emergent collaboration in LLM agents and provide a flexible platform for studying cooperative intelligence. © 2025 IEEE.