AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing

2026-01-16

AI Summary

This work addresses the limited immersion and dynamic adaptability of current large language models in role-playing scenarios, particularly their inability to support multi-agent coordination, scene transitions, and real-time character introduction. To overcome these limitations, the authors propose an adaptive multi-agent role-playing framework featuring a novel structured message format—[Thought]/(Action)/<Environment>/Speech—that integrates cognition, action, environment, and dialogue. An explicit scene manager is introduced to orchestrate narrative flow through discrete control actions and reasoning mechanisms. The study also contributes two specialized training datasets, AdaRPSet and AdaSMSet, along with AdaptiveBench, a trajectory-level evaluation benchmark. Experimental results demonstrate that an 8B-parameter role-playing model achieves superior character consistency and narrative coherence compared to several commercial large language models, while a 14B-parameter scene manager outperforms Claude Sonnet 4.5 in tasks involving dynamic character introduction and scene switching.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

LLM role-playing aims to portray arbitrary characters in interactive narratives, yet existing systems often suffer from limited immersion and adaptability. They typically under-model dynamic environmental information and assume largely static scenes and casts, offering insufficient support for multi-character orchestration, scene transitions, and on-the-fly character introduction. We propose an adaptive multi-agent role-playing framework, AdaMARP, featuring an immersive message format that interleaves [Thought], (Action),, and Speech, together with an explicit Scene Manager that governs role-playing through discrete actions (init_scene, pick_speaker, switch_scene, add_role, end) accompanied by rationales. To train these capabilities, we construct AdaRPSet for the Actor Model and AdaSMSet for supervising orchestration decisions, and introduce AdaptiveBench for trajectory-level evaluation. Experiments across multiple backbones and model scales demonstrate consistent improvements: AdaRPSet enhances character consistency, environment grounding, and narrative coherence, with an 8B actor outperforming several commercial LLMs, while AdaSMSet enables smoother scene transitions and more natural role introductions, surpassing Claude Sonnet 4.5 using only a 14B LLM.

Problem

• immersive role-playing
• multi-agent interaction
• dynamic environment modeling
• scene transition
• character consistency

Innovation

• adaptive multi-agent role-playing
• immersive message format
• Scene Manager
• AdaRPSet
• AdaptiveBench