端到端联合分配与路径规划，实现高效多智能体任务调度。
ARMATA: Auto-Regressive Multi-Agent Task Assignment
- 采用自回归机制统一生成任务分配与路径顺序。
- 相比工业级求解器提升20%解质量，计算时间从小时缩短至秒级。
- 适合需要实时决策的多机器人协同场景。
在空间分布区域中协调多智能体系统需解决复杂分层问题：首先将区域分配给智能体（分配），随后确定最优访问顺序（路径规划）。现有方法通常将两阶段解耦，忽略阶段间依赖，或依赖缺乏全局视角的分布式启发式策略。本文提出一种集中式、完全端到端的自回归框架，联合生成分配决策与路径序列。核心贡献是多阶段解码机制，将高层分配与低层路径在同一自回归过程中统一建模，同时保持集中式全局状态。该设计使模型隐式平衡负载分布与路径效率，避免分布式方法常见的局部最优。大量实验表明，本方法显著优于多种基线，在解质量上相较Google OR-Tools、IBM CPLEX、LKH-3等工业求解器最高提升20%，计算时间由小时级降至秒级。
原文摘要 · Abstract (English)
Coordinating multi-agent systems over spatially distributed areas requires solving a complex hierarchical problem: first distributing areas among agents (allocation) and subsequently determining the optimal visitation order (routing). Existing methods typically decouple these stages ignoring inter-stage dependencies or rely on decentralized heuristics that lack global context. In this work, we propose a centralized, fully end-to-end auto-regressive framework that jointly generates allocation decisions and routing sequences. The core contribution of our approach is a multi-stage decoding mechanism that unifies high-level allocation and low-level routing in a single autoregressive pass while maintaining a centralized global state. This enables the model to implicitly balance workload distribution with routing efficiency, avoiding local optima common in decentralized methods. Extensive experiments demonstrate that our method significantly outperforms diverse baselines, achieving up to a 20\% improvement in solution quality over industrial solvers such as Google OR-Tools, IBM CPLEX, and LKH-3, while reducing computation time from hours to seconds.
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