arXiv:2606.18413v1 Announce Type: new 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 coll
The increasing capabilities of automated AI agents are pushing the boundaries of human-AI collaboration, making the study of their synergy crucial for future advancements.
Understanding the optimal integration of human judgment with AI capabilities is critical for maximizing productivity and effectiveness in complex scientific and professional tasks, especially as AI agents become more prevalent.
This research provides a framework for evaluating and optimizing human-AI team performance, shifting the focus from individual AI capability to collaborative system design.
- · AI-powered collaboration platforms
- · Organizations adopting human-AI teams
- · AI agent developers focused on interaction design
- · Tasks requiring only human or only AI input
- · Poorly designed human-AI collaboration systems
Improved performance in complex tasks through optimized human-AI team structures.
Accelerated development of AI agents capable of more sophisticated and adaptive human interaction.
Re-definition of professional roles and skill requirements, emphasizing human-AI coordination over individual task execution.
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Read at arXiv cs.AI