会议中实时抓取信息需求，自动推送简明洞察与图表。
InsightToast: Proactive Information Retrieval & Glanceable Visualization in the Side Channel of Data-Rich Meetings
- 用多智能体LLM+RAG动态识别话题并检索信息。
- 16人实验表明决策更准且对话自然不中断。
- 适合需快速决策的会议场景，如政策制定。
会议中缺乏机构背景信息会阻碍有效参与。获取相关资讯常需在异构内部与外部来源间切换，导致任务频繁转换，破坏个人专注与集体对话流，尤其在决策等高认知负荷任务中尤为不利。我们提出InsightToast，一种混合主动式应用，可实时监测口语对话，识别随发言浮现的话题与信息需求，并通过基于多智能体大语言模型（LLM）的管道，结合检索增强生成（RAG），生成有源依据的简洁文本与可快速浏览的交互式图表，经侧边通道以短暂提示形式呈现。为展示其产生意外洞见的潜力，我们以立法文件知识库作为会议上下文进行示范。随后报告一项对比研究（N=16），参与者在保持自然对话流的同时作出了更知情的政策决策。
原文摘要 · Abstract (English)
Missing institutional context during meetings can impede effective participation. Retrieving relevant information, often scattered across heterogeneous internal and external sources, requires costly task-switching that disrupts both individual focus and collective conversational flow, particularly detrimental during cognitively demanding tasks such as decision-making. We introduce InsightToast, a mixed-initiative application that monitors verbal discourse in real time, identifies topics and informational needs as they emerge, and proactively retrieves relevant information through a multi-agent large language model (LLM)-based pipeline integrating retrieval-augmented generation (RAG) to produce source-grounded insights as succinct text and glanceable interactive charts, delivered through a peripheral interface as ephemeral toasts in the conversation's side channel. To demonstrate the potential for yielding serendipitous insights, we showcase a usage scenario involving a knowledge base of legislative documents as the meeting's context. We then report on a comparative study (N=16), in which participants arrived at informed policy decisions while maintaining natural conversation flow.
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