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 of evaluation from an exponential $O(n^m)$ wall to a strictly linear $O(m \cdot n \cdot χ^2)$ constraint. To bridge local continuous spatial geometry with this discrete algebraic backend without requiring power-intensive online training loops, we introduce a decoupled, hybrid neuro-symbolic control pipeline. Local multi-layered neural networks operate as structural coordination encoders, pre-trained offline to nonlinearly map hand-engineered geometric descriptors into abstract environmental target measures. At runtime, edge agents execute instantaneous behavioral adaptations by applying the Radon-Nikodým derivative directly as a zero-shot importance-reweighting filter. We validate the framework within a discrete-time multi-agent simulation sandbox spanning tracking, decentralized dispersion/area coverage, and multi-goal coordination regimes. Qualitative telemetry demonstrates that the integrated pipeline achieves stable target-directed motion, anti-collapse spatial spreading under decentralized constraints, and structured subgroup formation across multiple targets, providing a mathematically grounded route to tractable, low-power edge swarm intelligence.

翻译：在资源受限的边缘平台上实现去中心化多智能体集群协调，其根本瓶颈在于联合动作空间的指数级扩展以及高延迟的通信开销。本文提出集群策略干扰网络（SPIN）框架，该架构范式通过将集群拓扑结构建模为压缩张量网络，绕过了这些限制。我们将局部多智能体团簇的联合策略张量分解为矩阵乘积态（MPS）链，从而将评估的计算复杂度从指数级 $O(n^m)$ 上限严格降低为线性约束 $O(m \cdot n \cdot χ^2)$。为了桥接局部连续空间几何结构与这一离散代数后端之间的鸿沟，且无需依赖高能耗的在线训练循环，我们引入了一种解耦的混合神经符号控制流水线。局部多层神经网络作为结构协调编码器，通过离线预训练，将手工设计的几何描述符非线性映射为抽象的环境目标度量。在运行时，边缘智能体通过直接应用Radon-Nikodým导数作为零样本重要性重加权滤波器，执行即时行为适应。我们在一个离散时间多智能体仿真沙盒中验证了该框架，涵盖追踪、去中心化扩散/区域覆盖以及多目标协调等场景。定性遥测数据表明，整合后的流水线实现了稳定的目标导向运动、去中心化约束下的抗塌缩空间扩散，以及跨多目标的结构化子群形成，为可实施的、低功耗的边缘集群智能提供了一条具有数学基础的路径。