Searching for Synergy in Shared Workspace Human-AI Collaboration
Using Collaborative Gym and DiscoveryBench tasks, this study examines human-AI team coordination in shared workspaces. Results show that adding collaborators without structure lowers performance, while scaffolding with shared group memory and simulated human-in-the-loop gates improves performance, especially in three-person teams.
[2606.18413] Searching for Synergy in Shared Workspace Human-AI Collaboration
[Submitted on 16 Jun 2026]
Title:Searching for Synergy in Shared Workspace Human-AI Collaboration
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Abstract:Automated AI agents are increasingly capable, yet many scientific and professional tasks require human judgment and contextual expertise. We study shared-workspace human-AI teams, where AI agents and human collaborators must coordinate responsibilities before submitting a final answer. Using the Collaborative Gym environment with DiscoveryBench tasks, we examine when adding simulated human collaborators improves performance and when process loss turns additional collaborators into coordination overhead. Across 1,482 sessions, adding relevant collaborators can lower performance when teams lack structure to coordinate their contributions. We then evaluate scaffolding that combines shared group memory with simulated human-in-the-loop (HITL) gates, where selected actions require approval from a designated simulated participant. This scaffolding yields higher mean performance, most clearly in three-person teams, with clearer responsibility signals and stronger routing of expertise to team actions. Overall, how human-AI teams coordinate and integrate expertise matters as much as the capability available to them.
Comments: Accepted at ICML 2026 Workshop on Human-AI Co-Creativity. 13 pages, 5 figures, 3 tables
Subjects:
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2606.18413 [cs.AI]
(or arXiv:2606.18413v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.18413
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Nachiket Kotalwar [view email] [v1] Tue, 16 Jun 2026 19:08:43 UTC (608 KB)
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