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 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.

翻译：大语言模型角色扮演旨在交互式叙事中刻画任意角色，然而现有系统常受限于沉浸感不足与适应性有限。这些系统通常对环境动态信息建模不足，并假设场景与角色阵容基本静态，对多角色编排、场景转换及实时角色引入的支持不足。我们提出一种自适应多智能体角色扮演框架AdaMARP，其采用沉浸式消息格式——交织[思考]、(动作)、<环境>与对话，并配备显式的场景管理器，通过离散动作（初始化场景、选择发言者、切换场景、添加角色、结束）及相应原理来调控角色扮演过程。为训练这些能力，我们构建了用于演员模型的AdaRPSet数据集和用于编排决策监督的AdaSMSet数据集，并引入AdaptiveBench进行轨迹级评估。跨多种骨干网络与模型规模的实验显示出一致的改进：AdaRPSet提升了角色一致性、环境关联性与叙事连贯性，其8B参数量演员模型性能超越多个商用大语言模型；而AdaSMSet则实现了更流畅的场景转换与更自然的角色引入，仅用14B参数量大语言模型即超越Claude Sonnet 4.5。