Ad Hoc Teamwork

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Distributed Team Orchestration via Supervisor Networks: Convergence, Optimality, and Resilience
Aug 10, 2026Juntian Zhu, Guanpu Chen, Tongtian Zhu +3Multi-Agent Orchestration Zero-Sum Games
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China · School of Automation, Southeast University, Nanjing 210096, China · College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China +2
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
Jul 29, 2026Peter Tisnikar, Maja Swieczkowska, Benteng Ma +2Bayesian Inference Ad Hoc Teamwork
aDepartment of Informatics, King’s College London, 30 Aldwych, London, WC2B 4BG, England, United Kingdom · bDepartment of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, England, United Kingdom
What Types of Human-AI Teams Exist?
Jul 2, 2026Nathan Hughes, Ibrahim HabliAd Hoc Teamwork Human-AI Collaboration
Centre for Assuring Autonomy, University of York, Deramore Lane, York, North Yorkshire, YO10 5DD, UK.
CONCAT: Consensus- and Confidence-Driven Ad Hoc Teaming for Efficient LLM-Based Multi-Agent Systems
May 28, 2026Ziyang Ma, Dingyi Zhang, Sichu Liang +4Multi-Agent System Optimization Multi-Agent Orchestration
Southeast University · Huawei Technologies Ltd
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
May 23, 2026Yuheng Jing, Kai Li, Ziwen Zhang +8RL Benchmarks Multi-Agent Coordination
C2DL, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · University of Science and Technology of China +5
Partner-Aware Hierarchical Skill Discovery for Robust Human-AI Collaboration
May 23, 2026Adnan Ahmad, Bahareh Nakisa, Mohammad Naim RastgooHierarchical RL Multi-Agent Collaboration
aSchoold of Information Technology, Deakin University, 75 Pidgons Rd, Waurn Ponds, 3216, VIC, Australia · bFaculty of Information Technology, Monash University, Wellington Rd, Clayton, 3800, VIC, Australia
Adaptive Human-AI Coordination via Hierarchical Action Disentanglement
May 23, 2026Adnan Ahmad, Bahareh Nakisa, Mohammad Naim RastgooDisentangled Representation Learning Hierarchical RL
School of Information Technology Deakin University, Waurn Ponds, 3216, VIC, Australia · Faculty of Information Technology, Monash University, 3800, VIC, Australia
Too Many Specialists: Emergent Inefficiencies and Bottlenecks for Multi-agent Ad-hoc Collaboration
May 8, 2026Benjamin Panny, Shashank Mehrotra, Zahra Zahedi +2Multi-Agent Coordination Multi-Agent Collaboration
University of Pittsburgh Pittsburgh, PA, United States · Honda Research Institute USA, Inc. San Jose, CA, United States
ConventionPlay: Capability-Limited Training for Robust Ad-Hoc Collaboration
Apr 20, 2026Abhishek Sriraman, Eleni Vasilaki, Robert LoftinMulti-Agent Coordination Multi-Agent Collaboration
University of Sheffield · Sheffield, United Kingdom
PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork
Nov 10, 2025Hohei Chan, Xinzhi Zhang, Antao Xiang +2Multi-Agent Coordination Multi-Agent Collaboration
School of Software Engineering, South China University of Technology, Guangzhou, China · Shanghai Jiao Tong University, Shanghai, China
Unsupervised Partner Design Enables Robust Ad-hoc Teamwork
Aug 8, 2025Constantin Ruhdorfer, Matteo Bortoletto, Victor Oei +2Multi-Agent Collaboration Ad Hoc Teamwork
Collaborative Artificial Intelligence, University of Stuttgart, Stuttgart, Germany.
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork
May 29, 2025Caroline Wang, Arrasy Rahman, Benjamin Nativi +4Multi-Agent Collaboration Ad Hoc Teamwork
Department of Computer Science The University of Texas at Austin · Visa AI · College of Computing and Data Science Nanyang Technological University +1

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Distributed Team Orchestration via Supervisor Networks: Convergence, Optimality, and Resilience

Juntian Zhu, Guanpu Chen, Tongtian Zhu, Miguel de Carvalho+2

School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China · School of Automation, Southeast University, Nanjing 210096, China · College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China+2

Multi-Agent Orchestration Zero-Sum Games Multi-Agent Systems Ad Hoc Teamwork

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In this paper, we study zero-sum potential team games with a supervisor network, where agents rely on supervisor-provided belief information rather than accurate common beliefs. The main challenge is that such belief information can be inaccurate because of supervisors' belief-estimation errors and the misreporting of joint actions by Byzantine teams. We propose the distributed team-orchestrating algorithm (DTOA), which combines team fictitious play with supervisor-based distributed belief learning. We prove the convergence of supervisors' belief estimates and establish that the induced learning dynamics converge to a near team-Nash equilibrium (TNE) in terms of the team-Nash gap (TNG). In the Byzantine setting, we consider a misreporting attack model and develop a Byzantine-resilient DTOA. We further provide probabilistic guarantees for Byzantine-team identification and establish an asymptotic bound on the honest TNG. Numerical experiments illustrate the theoretical findings, compare DTOA with baseline learning methods, and evaluate its performance in a Markov decision process setting.

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