InsightToast: Proactive Information Retrieval & Glanceable Visualization in the Side Channel of Data-Rich Meetings

2026-08-31

AI Summary

为解决会议中信息缺失问题，通过实时监测对话并利用基于多代理大语言模型的管道主动检索相关信息，以简洁文本和图表形式呈现。

Abstract

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.

Problem

• information retrieval
• contextual information
• task-switching
• meeting productivity
• decision-making

Innovation

• Proactive Information Retrieval
• Retrieval-Augmented Generation (RAG)
• Multi-Agent LLM-based Pipeline
• Glanceable Visualization