arXiv:2605.27076v1 Announce Type: cross Abstract: In many multi-agent applications, tasks yield rewards only when executed by a coalition meeting an unknown size threshold; otherwise, feedback is fully censored. This censorship creates an identifiability problem: agents cannot distinguish stochastic failure from insufficient coordination. We formalize this setting as the Threshold-Activated Cooperative Multi-Armed Bandit (TAC-MAB) and analyze it under both centralized and decentralized coordination. We show that a centralized algorithm (C-TAC) achieves cumulative regret O(log T), decomposed in
The proliferation of multi-agent systems and demand for robust coordination mechanisms across various AI applications necessitates advanced learning architectures capable of handling complex feedback loops.
This research offers a novel approach to optimizing multi-agent task execution under censored feedback, crucial for developing more resilient and efficient AI systems in real-world, uncertain environments.
The formalization and proposed algorithms for Threshold-Activated Cooperative Multi-Armed Bandits (TAC-MAB) provide a theoretical framework and practical tools for managing coordination in environments where success feedback is intermittent or ambiguous.
- · AI agents developers
- · Robotics companies
- · Multi-agent system researchers
- · Logistics and autonomous fleet operators
- · Systems relying on complete feedback for optimization
- · Inefficient multi-agent coordination strategies
Improved performance and reliability of AI agent swarms in distributed or partially observable tasks.
Accelerated development of autonomous systems in complex environments like defense, manufacturing, and exploration.
Reduced operational costs and increased efficiency across sectors adopting advanced multi-agent AI for critical tasks.
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