{"ID":2838430,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21728","arxiv_id":"2511.21728","title":"Affective Multimodal Agents with Proactive Knowledge Grounding for Emotionally Aligned Marketing Dialogue","abstract":"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.","short_abstract":"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 reason...","url_abs":"https://arxiv.org/abs/2511.21728","url_pdf":"https://arxiv.org/pdf/2511.21728v2","authors":"[\"Lin Yu\",\"Xiaofei Han\",\"Yifei Kang\",\"Chiung-Yi Tseng\",\"Danyang Zhang\",\"Ziqian Bi\",\"Zhimo Han\"]","published":"2025-11-21T04:16:45Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}