arXiv AI
"When to Hand Off, When to Work Together": Understanding Concurrent Human-Agent Interaction in Shared Co-Creative Workspaces
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arXiv AI
Jun 3
DeskCraft: Benchmarking Desktop Agents on Professional Workflows and Human-in-the-Loop Collaboration
arXiv:2606. 03103v1 Announce Type: new Abstract: Real-world professional desktop workflows in specialized creative and engineering software unfold over long horizons and often require human-in-the-loop coordination, where agents proactively seek necessary information and users provide additional instructions, clarifications, feedback, or corrections as the task progresses.
Hugging Face Trending Papers
Jul 4
ProACT: Towards Breakdown-Aware Proactive Agent in Multi-User Collaboration
Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than proactively recognizing moments when a team would benefit from timely intervention as human collaborators often do. This reactive design substantially limits the use of agents as active participants in multi-user collaboration, where disagreements, ambiguous goals, forgotten constraints, underspecified plans, discussion loops, and imbalanced participation can gradually undermine group progress.
arXiv AI
Sep 25
DocuTeam: Mixed-Initiative Multi-Agent Discussions around Evolving Documents
DocuTeam is a mixed‑initiative multi‑agent discussion system that allows both users and agents to start and steer conversations around evolving documents. Agents monitor changes to the document and proactively initiate or redirect discussions, while users can shape the dialogue or adopt agent suggestions. In a within‑subjects study with 20 participants, DocuTeam produced outcomes that were rated as more novel, relevant, and specific compared to a baseline, without increasing cognitive load.
arXiv AI
1d ago
Goal-Aware Dynamic Multi-Agent Collaboration for Long-Horizon Tasks
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.
arXiv Machine Learning
Oct 15
Scaling Human-AI Coding Collaboration Requires a Governable Consensus Layer
arXiv:2604.17883v2 Announce Type: replace-cross Abstract: Vibe coding turns natural-language intent into working software, but code generation alone does not give humans and agents a shared account o...
arXiv AI
Oct 16
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
Kolab is a new dataset comprising over 115 hours of screen recordings from 50 professionals across 40 occupations who collaborate with AI agents on realistic, long‑horizon tasks. The dataset includes participants’ judgments of collaboration quality and output, revealing varied coordination patterns and the need for agents to anticipate how experts will use or modify their contributions. Analysis shows current state‑of‑the‑art models overestimate collaboration success and struggle to differentiate high‑ from low‑quality interactions, indicating a gap in understanding long‑horizon human‑agent teamwork.