通过角色视角：解决多模态角色扮演代理中的模态-角色干扰
Through the Lens of Character: Resolving Modality-Role Interference in Multimodal Role-Playing Agent
AI总结：
本文提出CAVI框架，通过角色视角减少多模态角色扮演代理中的模态-角色干扰，提升角色一致的多模态交互。
摘要：
多模态大语言模型（MLLMs）的发展使角色扮演代理（RPAs）扩展到视觉环境。然而，人类视觉具有主观性和身份驱动性，而现有MLLMs提取客观、角色无关的特征用于通用任务。在RPAs中，这种通用视觉噪声会压制脆弱的角色特征，导致模态-角色干扰（MRI），即代理难以整合视觉基础和角色一致性。为了解决这一问题，我们引入免训练的角色感知视觉干预（CAVI）框架，使代理能够通过角色视角感知世界。CAVI系统性地针对MRI：宏观上，角色引导的标记修剪（CTP）将视觉感受野限制在角色相关实体；微观上，正交特征调节（OFM）将标记投影到角色上下文子空间以提取一致的事实；在解码过程中，模态自适应角色引导（MARS）根据视觉依赖动态优化引导强度。大量实验表明，CAVI有效缓解了MRI，显著增强了角色一致的多模态交互。
英文摘要：
The advancement of Multimodal Large Language Models (MLLMs) has expanded Role-Playing Agents (RPAs) into visually grounded environments. However, human vision is inherently subjective and identity-driven, whereas existing MLLMs extract objective, character-agnostic features for general tasks. In RPAs, this generic visual noise overpowers fragile character traits, causing Modality-Role Interference (MRI), where agents struggle to integrate visual grounding and character consistency. To address this, we introduce the training-free Character-Aware Visual Intervention (CAVI) framework, enabling agents to perceive the world through the lens of character. CAVI systematically targets MRI: macroscopically, Character-Guided Token Pruning (CTP) restricts the visual receptive field to role-relevant entities; microscopically, Orthogonal Feature Modulation (OFM) projects tokens onto a character-context subspace to extract aligned facts; and during decoding, Modality-Adaptive Role Steering (MARS) dynamically optimizes steering intensity based on visual reliance. Extensive experiments show CAVI effectively alleviates MRI, significantly enhancing character-consistent multimodal interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
