Treadstone：一个受社交媒体启发的多智能体协作数据分析平台
Treadstone: A Social-Media-Inspired Platform for Multi-Agent Collaborative Data Analysis
浏览论文内容
中文总结 AI 辅助
Treadstone提出受社交媒体启发的多智能体协作数据分析平台，通过共享协调流平衡机器自主与人类控制，定性研究显示其促进协作并保留人类分析能动性。
中文摘要 AI 辅助
协调人类分析师与自主AI智能体面临着与人际协作相同的挑战：共享中间结果、避免冲突以及保持群体意识。当前工具依赖非结构化消息或单线程聊天机器人交互，缺乏跟踪不断演变的假设或将主张与证据联系起来的结构。我们提出智能体社会数据分析，这是一种协作范式，通过模拟社交媒体服务中的内容时间线，将社会数据分析扩展为共享协调流。我们在TREADSTONE中实例化这一概念，这是一个平台，人类和AI智能体通过共享流中的线程化消息异步发布、链接和质疑分析主张。通过允许智能体主动广播假设，并让用户通过轻量级策展来引导分析，Treadstone旨在平衡机器自主性与人类分析控制。一项定性用户研究表明，Treadstone促进了协作，同时保留了人类分析能动性，这与传统聊天机器人交互的孤立体验形成对比。
英文摘要
Coordinating human analysts with autonomous AI agents faces the same challenges as human-to-human collaboration: sharing intermediate results, avoiding conflicts, and maintaining group awareness. Current tools rely on unstructured messaging or single-threaded chatbot interaction, which lack the structure to track evolving hypotheses or link claims to evidence. We propose agentic social data analysis, a collaboration paradigm extending social data analysis with a shared coordination feed modeled on the content timeline in social media services. We instantiate this concept in TREADSTONE, a platform where human and AI agents asynchronously post, link, and contest analytical claims via threaded messages within a shared feed. By allowing agents to proactively broadcast hypotheses and enabling users to steer the analysis through lightweight curation, Treadstone seeks to balance machine autonomy with human analytical control. A qualitative user study shows that Treadstone fosters collaboration while preserving human analytical agency, in contrast to the solitary experience of conventional chatbot interaction.
发表机构
- Soongsil University（崇实大学）
- Pohang University of Science and Technology（浦项科技大学）
- Sogang University（西江大学）
- Aarhus University（奥胡斯大学）
机构由 AI 辅助整理，请以论文原文为准。
