让对话模型同时保持个性与共识，更像真人交流。
MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue
- 用加权特征和动态记忆建模不同认知风格的对话者
- 在三个数据集上实现主题一致且不丢失个性
- 适合研究可解释对话系统或具身智能的开发者
人类对话不仅传递信息，还表达信念、情绪和主观认知方式。然而现有AI对话系统常强制语义统一，牺牲多样性与可解释性。我们提出MAPS（多智能体视角空间）框架，通过领域加权特征、基于GRU的动态记忆和可解释的词级注意力，建模具有认知差异的智能体之间的对话。该框架使智能体在保持个性化推理的同时逐步达成共享理解。在EmpatheticDialogues、TopicalChat和MultiWOZ三个数据集上的评估表明，MAPS能在不消解主观性的情况下实现语义对齐。结果展示了迈向具认知基础、可解释对话系统的可行路径，兼顾表达力与连贯性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。