AdaMARP: An Adaptive Multi-Agent Interaction Framework for General Immersive Role-Playing
Zhenhua Xu, Dongsheng Chen, Shuo Wang, Jian Li, Chengjie Wang, Meng Han, Yabiao Wang

TL;DR
AdaMARP introduces an adaptive multi-agent framework for immersive role-playing with improved character consistency, scene management, and narrative coherence, leveraging new training sets and evaluation benchmarks.
Contribution
The paper presents AdaMARP, a novel framework with an immersive message format and explicit scene management, enhancing multi-character orchestration and adaptability in role-playing systems.
Findings
AdaRPSet improves character consistency and narrative coherence.
AdaSMSet enables smoother scene transitions and natural role introductions.
An 8B model outperforms several commercial LLMs in role-playing tasks.
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), <Environment>, 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…
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Taxonomy
TopicsArtificial Intelligence in Games · Multimodal Machine Learning Applications · Human Motion and Animation
