arXiv:2606.07557v1 Announce Type: new Abstract: Decentralized multi-agent swarm coordination on resource-constrained edge platforms remains fundamentally bottlenecked by the exponential scaling of joint action spaces and high-latency communication overhead. This paper introduces the Swarm Policy Interference Network (SPIN) framework, an architectural paradigm that bypasses these limitations by modeling swarm topologies as a compressed tensor network. We factorize the joint policy tensors of local multi-agent cliques into Matrix Product State (MPS) chains, reducing the computational complexity
The proliferation of distributed AI systems and resource-constrained edge devices necessitates new control paradigms to optimize performance and reduce communication overhead. This research addresses fundamental limitations that become more acute with increased decentralization.
Researchers and strategists interested in multi-agent systems, robotics, and distributed AI will find this important as it presents a novel theoretical and architectural approach to managing swarm complexity. This could unlock new capabilities for autonomous systems in environments with limited resources.
The proposed SPIN framework offers a way to overcome the exponential scaling of joint action spaces in decentralized swarms through tensor network compression. This fundamentally changes how complex multi-agent policies can be coordinated and deployed on edge platforms.
- · Robotics companies
- · AI hardware manufacturers (edge devices)
- · Defence contractors (swarm intelligence)
- · Logistics and autonomous vehicle developers
- · Traditional centralized control system developers
- · Solutions heavily reliant on high-bandwidth, low-latency communication networks
More efficient and scalable decentralized multi-agent systems become viable for real-world applications.
This efficiency enables more widespread deployment of autonomous swarms in logistics, exploration, and defense, reducing operational costs and increasing mission capabilities.
The underlying mathematical framework of tensor networks could find broader application in optimizing other complex AI systems, leading to a new class of AI architectures.
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Read at arXiv cs.LG