Ream: Unfolding Mutual Awareness in Human-Agent Workspaces

Peiling Jiang; Sangho Suh; Varsha Kishore; Jonathan Bragg; Haijun Xia; Pao Siangliulue; Daniel S. Weld; Amy X. Zhang

cs.AIOct 7, 2026

Ream: Unfolding Mutual Awareness in Human-Agent Workspaces

Authors: Peiling Jiang, Sangho Suh, Varsha Kishore, Jonathan Bragg, Haijun Xia, Pao Siangliulue, Daniel S. Weld, Amy X. Zhang, +1 more

Organizations: University of California San Diego La Jolla, California, USA · Allen Institute for AI (Ai2) · Allen Institute for AI Seattle, WA, USA · University of Washington Seattle, Washington, USA

Abstract

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.

Figures & tables

Figure 1. Ream is a human-agent collaborative literature review workspace, tracking and visualizing both the user’s and agents’ actions to facilitate mutual awareness. The file tree (a) shows different amounts of activity on each file, with blue indicating user activity and orange indicating the agents’. The tabbed file viewer (b) shows a .report file with paper citation chips. The right chat panel (c) allows the user to create and instruct multiple agents across workspace folders at the same time. The Ream workspace has three panels: a file tree with blue user activity bars and orange agent activity bars, a tabbed report viewer with interactive citations and a paper preview card, and a chat panel for coordinating agents.

Element	Awareness question	Ream mechanisms
Who	Which party engaged with this artifact?	User/agent attribution in bars and histories
What	What work has occurred here?	Search, read, write, chat, and organize events
Where	Which sources and passages received attention?	Traces on files, references, and PDF passages
When	When was this artifact visited or changed?	Timestamped per-file histories
How	How was a source used to address a question?	Exploration questions, answers, and extracted quotes
Why	What goals or preferences help explain an action?	Notes, stars/downvotes, and agent questions

Table 1. Workspace-awareness elements adapted to human-agent artifacts, with the questions each mechanism helps collaborators address.

Figure 2. The .paper file view includes: (1) controls allowing users to Star or Downvote a paper to indicate their interests; (2) paper metadata and figures; (3) user notes (here, the user took notes about additional papers found while reading, which are in turn used by the agent to generate proactive suggestions); and (4) the Agent Explorations section with questions, answers, and quotes extracted from the paper (5). A paper document with Star and Downvote controls, paper metadata and figures, user notes, and an Agent Explorations section containing questions, answers, and evidence quotes from the paper.

Figure 3. When the chat input is focused, Ream proactively generates task suggestions based on user activities. Four task suggestions above the chat input propose reviewing a synthesis for unsupported claims, comparing interaction patterns across papers, adding a research question, and checking whether another paper adds a distinct form of user control.

Figure 4. Left: at the bottom of each file, Ream lists activities from the user and agents. Here, the agent first fetched this .paper file by searching. The user then mentioned it in chat, first asking a detailed question, then asking the agent to compare it with its citations. Right: the PDF view highlights passages matched to quotes returned by the agent’s paper-reading tool, connecting its questions and answers to source evidence for inspection. Three panels show a file's chronological signal history, the user's follow-up questions in chat, and passages in the PDF highlighted to match evidence quotes returned by the agent's paper-reading tool.

Figure 5. Two literature review workflows in Ream . P7 first worked with agents to gather papers and develop syntheses, then focused on reading. P9 interleaved searching, synthesis, organization, and reading throughout the session. Two stacked activity plots compare sessions P7 and P9, with user activity in blue and agent activity in orange. P7 concentrates agent work earlier and user activity later, while P9 interleaves user and agent activity.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Actor	Action	Strength
User	Hover over paper card	0.02
	Open file	0.05
	Active file focus (30 s)	0.80
	Highlight PDF passage	0.50
	Add / edit highlight note	0.30
	Mention in chat	0.50

Appendix

Table 2. Action signal weights.

Figure 6. Activity traces for users and agents, and for different action types in user study sessions. Twelve panels show activity traces for participants P1 through P12. Each panel compares user and agent activity over the session and separates recorded actions into chat, organize, read, search, and write categories.

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Abstract

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