用张量网络压缩多智能体协作，实现无需重训练的高效分布式控制。

Decentralized multi-agent swarm coordination remains fundamentally challenged by the combinatorial scaling of joint action spaces and high-overhead training or optimization loops when managing localized group behaviors. To address this problem from a different perspective, this paper introduces the Swarm Policy Interference Network (SPIN) framework, which models multi-agent communication topologies as compressed tensor networks. By factorizing the joint policy tensors of local multi-agent cliques into Open Boundary Condition Matrix Product State (MPS) chains, SPIN replaces explicit exponential joint-action enumeration with clique-level contractions that scale linearly in clique length for fixed local behavior and bond dimensions. To connect raw spatial geometry with this discrete algebraic backend without relying on online training loops, we introduce a decoupled framework combining a lightweight frozen neural mapper evaluated offline with a deterministic zero-shot importance-reweighting filter based on the Radon-Nikodým derivative. We evaluate an executable prototype of this framework within a simulation experiment across distinct task regimes: single-target tracking, decentralized area coverage, and structured multi-goal coordination. The results demonstrate that SPIN functions as a reusable decentralized coordination layer across tracking, dispersion / area coverage, and structured multi-goal coordination, with its strongest gains appearing in multi-goal coordination and dense local interaction regimes, without requiring scenario-specific online optimization or retraining.