The paper demonstrates that humans and AI systems achieve better performance when collaborating rather than working alone. It investigates how two design dimensions—autonomy and initiative—shape collaboration patterns, using a paradox perspective to uncover internal tensions and map underlying paradoxes. From this analysis, the authors derive four distinct human‑AI collaboration patterns: Instruction, Delegation, Assistance, and Co‑creation.
By Michael Weiss
arXiv:2607. 12180v1 Announce Type: cross Abstract: An AI teammate's design properties (personality, communication style, when it speaks) can shape a team's trust, coordination, and decisions.
By Mohammad Amin Samadi, Pedro Martins De Bastos, Jaeyoon Choi, Spencer JaQuay, Seehee Park, Nia Nixon
arXiv:2609.11737v2 Announce Type: replace-cross
Abstract: Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized. Yet artifici...
By Zhengran Ji, Jonathan Hyun, Boyuan Chen
arXiv:2607. 27179v1 Announce Type: cross Abstract: Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team.
By Nia Nixon, Jaeyoon Choi, Pedro Martins De Bastos, Mohammad Amin Samadi, Luise Mehner, Seehee Park, Spencer JaQuay
The paper introduces ORCH, a method that applies human organizational theory to create task‑specific hierarchical structures for large, heterogeneous embodied AI teams. Using ORCH, teams of up to 50 agents across 25 wildfire‑response missions outperformed four existing multi‑agent frameworks, achieving higher mission scores and greater execution efficiency. Both human‑designed and language‑model‑generated ORCH organizations improved performance, with hierarchical organization preserving concurrent activity while coordinating ordered transitions between mission phases.
By Zhengran Ji, Jonathan Hyun, Boyuan Chen
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.
By Nachiket Kotalwar, Rohini Das, Carolyn Rose
arXiv:2606. 09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work.
By Yijia Shao, Zora Zhiruo Wang, Neel Ahuja, Yicheng Wang, Bowen Liu, Diyi Yang
PersonaTeaming introduces a workflow that incorporates personas into adversarial prompt generation for generative AI, achieving higher attack success rates than the state‑of‑the‑art RainbowPlus while preserving prompt diversity. The system is extended into a user‑facing playground that lets red‑teamers create their own personas and collaborate with AI to refine prompts, fostering diverse strategies. A user study with 11 industry practitioners found the playground produced useful outputs and encouraged out‑of‑the‑box thinking, even when suggestions were not strictly followed.
By Wesley Hanwen Deng, Mingxi Yan, Sunnie S. Y. Kim, Akshita Jha, Lauren Wilcox, Kenneth Holstein, Motahhare Eslami, Leon A. Gatys
Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team. We examined sociocognitive communication dynamics in team decision-making using Group Communication Analysis (GCA), team surveys, and lexical analyses of team discourse.
The paper reports an in‑situ qualitative study of a persistent, proactive AI teammate deployed across multiple teams in a large technology company. It finds that the human‑agent workplace is in flux, with breakdowns and negotiations emerging around tacit workflow rules, the relational boundaries of the non‑human actor, and the redistribution of trust and human agency. These micro‑negotiations are used to propose a new research, design, and organizational agenda that seeks to preserve human agency when sharing workspaces with non‑human actors.
By Rida Qadri, Remi Denton, Michael Madaio, Mahima Pushkarna, Leslie Lai, Sherry Moore, Michelle Chen Huebscher, Andrew Butcher, Ritom Sen, Hsiao-Yu Tung, Shaan Mathur, Yimeng Liu, Shibl Mourad, Noah Fiedel, Edward Grefenstette, Michael Terry
arXiv:2609.22682v1 Announce Type: new
Abstract: Collective intelligence depends not only on what team members know, but also on how they organize their work. When the structure of a solution is unkno...
By Aneesh Pappu, Mirac Suzgun, Yongchan Kwon, Federico Bianchi, Batu El, Mykel J. Kochenderfer, Hancheng Cao, James Zou
arXiv:2609.38274v1 Announce Type: new
Abstract: The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictiv...
By Liangyu Teng, Hengsong Liu, Juncen Guo, Jingyu Zhang, Yang Liu, Jing Liu, Liang Song