发表机构
University of California San Diego; Allen Institute for AI; University of Washington（加利福尼亚大学圣迭戈分校; 艾伦人工智能研究所; 华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Ream通过结构化工件、双向参与跟踪和局部可视化，在文献综述工作空间中实现人与智能体的相互感知，提升透明度和协作效率。
AI 中文摘要
随着AI智能体在共享工作空间中与人类并肩工作，一个相互感知的挑战随之出现：智能体的行动速度超过了人类的监控能力，而用户不断变化的兴趣并不总是通过聊天表达出来。这一挑战在文献综述中尤为紧迫，因为双方都需要检索、阅读并综合不断增长的论文集合。我们提出了Ream，一个文献综述工作空间，通过结构化工件、双向参与跟踪和局部可视化来支持相互感知。用户可以在这些文档中看到各方的活动，而智能体也可以检索相同的历史记录来指导其工作。在针对十八位研究者的研究中，参与者利用这些痕迹来检查证据、引导智能体、通过注释进行交流，并反思其研究重点。共享历史还帮助智能体基于先前的工作进行构建。这些发现揭示了共享文档中的参与痕迹如何支持人机知识工作中的透明度、个性化协助和协调。
英文摘要
As AI agents work alongside humans in shared workspaces, a mutual awareness challenge arises: agents act at speeds that outpace human monitoring, and users' evolving interests are not always expressed in chat. This challenge is especially pressing in literature review, where both parties retrieve, read, and synthesize a growing body of papers. We present Ream, a literature review workspace that supports mutual awareness through structured artifacts, bidirectional engagement tracking, and localized visualizations. Users can see each party's activity within these documents, and agents can retrieve the same history to guide their work. In studies with eighteen researchers, participants used these traces to inspect evidence, steer agents, communicate through annotations, and reflect on their research focus. Shared histories also helped agents build on earlier work. These findings inform how engagement traces within shared documents can support transparency, personalized assistance, and coordination in human-agent knowledge work.
