MAPS：在多智能体认知对话中建模共存的主观视角和共享意义
MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue
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中文总结 AI 辅助
研究针对当前AI对话系统问题，提出MAPS框架，通过领域加权配置文件、动态GRU记忆和令牌级注意力，让智能体保持个性化推理并趋向共享意义，在多数据集评估中支持语义对齐且不损主观性，为可解释对话系统发展提供路径。
中文摘要 AI 辅助
人类对话不仅是信息交换，还表达信念、情感和主观认知风格。当前人工智能对话系统常强制语义统一，牺牲多样性和可解释性。我们提出MAPS（多智能体视角空间）框架，通过领域加权配置文件、基于动态GRU的记忆和可解释的令牌级注意力对不同认知的智能体间对话建模。MAPS能让智能体保持个性化推理并逐渐趋向共享意义。在多个数据集上的评估表明，MAPS支持语义对齐且不破坏主观性。我们的结果为平衡表现力和连贯性的基于认知基础、可解释的对话系统指明了道路。
英文摘要
Human dialogue involves more than exchanging information; it also expresses beliefs, emotions, and subjective cognitive styles. Yet current AI dialogue systems often enforce semantic uniformity, sacrificing diversity and interpretability. We present MAPS (Multi-Agent Perspective Spaces), a novel framework that models dialogue between cognitively distinct agents through domain-weighted profiles, dynamic GRU-based memory, and interpretable token-level attention. MAPS enables agents to maintain individualized reasoning while progressively converging on shared meaning. Evaluations on EmpatheticDialogues, TopicalChat, and MultiWOZ show that MAPS supports semantic alignment without collapsing subjectivity. Our results demonstrate a path toward cognitively grounded, interpretable dialogue systems that balance expressiveness and coherence.
