Title: RoleArena: A Multi-Agent Role-Playing Environment for Long Multi-Turn Dialogues with Autonomous Plot Progression
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Role-Playing;Multi-Agent;Multi-Turn

Abstract: Role-playing language agents (RPLAs) use large language models (LLMs) to simulate character dialogues, enabling immersive interactions with users.
Improving their plot progression ability plays a key role in enhancing user experience and the sense of immersion in the dialogue, with significant practical value.
However, research on plot progression in role-playing language agents is still limited.
Existing datasets often lack sufficient dialogue rounds and are missing sentences that include large-scale, clear plot twists, limiting the deep development and evaluation of related models.
To bridge this gap, we propose RoleArena, an innovative multi-agent role-playing environment designed to generate highly robust long multi-round dialogues with plot progression.
RoleArena contains several functionally independent large-model agents. To enable automatic plot progression, we introduce an environment agent that generates multiple plot storylines validated by self-reflection. After discretizing these, character agents complete plot points through dynamic dialogue, while the environment agent judges the plot completion state to smoothly drive the discrete plot forward.
To generate robust and controllable long multi-turn dialogues, we introduce a critic agent to calibrate the plot progression judgments made by the environment agent.
Using this environment, we constructed the PlotStream dataset, which includes 40.1k character configuration metadata files.
Numerous experiments have demonstrated the effectiveness of RoleArena.

TL;DR: We propose RoleArena, an innovative multi-agent role-playing environment designed to generate highly robust long multi-round dialogues with plot progression.