arXiv 2511.217282025-12-20cs.CLcs.AIcs.LG

具有主动知识 grounding 的情感多模态代理用于情感对齐的营销对话

Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue

Hunan Police Academy, Department of Criminal Investigation（湖南警察学院犯罪侦查系）；Business College, California State University（加州州立大学长滩分校商学院）；Northwestern University（西北大学）；AI Agent Lab, Vokram Group（Vokram集团AI代理实验室）；Beijing University of Technology（北京理工大学）；Zheng Zhou University of Light Industry（郑州轻工业大学）

Lin Yu, Xiaofei Han, Yifei Kang, Chiung-Yi Tseng, Danyang Zhang, Ziqian Bi, Zhimo Han

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AI总结：

AffectMind通过主动知识 grounding 和情绪-意图对齐模型，提升营销对话中的情感一致性与说服效果。

摘要：

最近在大语言模型（LLMs）上的进展使流畅的对话系统成为可能，但大多数仍然具有反应性，并在情感丰富、目标导向的环境中如营销对话中挣扎。为了解决这一限制，我们提出了AffectMind，一个多模态情感对话代理，能够进行主动推理和动态知识 grounding 以维持情感对齐和说服性的互动。AffectMind结合了三个组件：一个主动知识 grounding 网络（PKGN），它从文本、视觉和语调中持续更新事实性和情感性上下文；一个情绪-意图对齐模型（EIAM），它联合建模用户情绪和购买意图以适应说服策略；以及一个强化 discourse 循环（RDL），通过用户响应的强化信号优化情感连贯性和参与度。在两个新创建的营销对话数据集MM-ConvMarket和AffectPromo上的实验表明，AffectMind在情感一致性（+26%）、说服成功率（+19%）和长期用户参与度（+23%）方面优于强大的LLM基线，突显了情感基础的主动性作为商业多模态代理的关键能力。

英文摘要：

Recent advances in large language models (LLMs) have enabled fluent dialogue systems, but most remain reactive and struggle in emotionally rich, goal-oriented settings such as marketing conversations. To address this limitation, we propose AffectMind, a multimodal affective dialogue agent that performs proactive reasoning and dynamic knowledge grounding to sustain emotionally aligned and persuasive interactions. AffectMind combines three components: a Proactive Knowledge Grounding Network (PKGN) that continuously updates factual and affective context from text, vision, and prosody; an Emotion--Intent Alignment Model (EIAM) that jointly models user emotion and purchase intent to adapt persuasion strategies; and a Reinforced Discourse Loop (RDL) that optimizes emotional coherence and engagement via reinforcement signals from user responses. Experiments on two newly curated marketing dialogue datasets, MM-ConvMarket and AffectPromo, show that AffectMind outperforms strong LLM-based baselines in emotional consistency (+26\%), persuasive success rate (+19\%), and long-term user engagement (+23\%), highlighting emotion-grounded proactivity as a key capability for commercial multimodal agents.

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。