Large Language Model (LLM) agents deployed in complex real-world scenarios typically operate as spatially distributed entities. However, this physical dispersion constrains agents to limited local perception and finite temporal horizons. We characterize this bottleneck as spatiotemporal partial observability. Given such fragmented awareness, distributed agents struggle to coordinate efficiently. To bridge this gap, we introduce MACRO-LLM, LLM-empowered multi-agent collaborative reasoning under spatiotemporal partial observability. The architecture addresses spatiotemporal constraints via three modules: (1) the CoProposer mitigates temporal uncertainty by verifying candidate actions via predictive rollouts; (2) the Negotiator overcomes spatial myopia by resolving conflicts through mean-field statistical aggregation; and (3) the Introspector ensures continuous adaptation by analyzing historical experience to refine strategies via semantic gradient descent. Extensive evaluations on two complex long-horizon tasks, cooperative adaptive cruise control and pandemic control, demonstrate that our framework effectively mitigates spatiotemporal partial observability through spatial and temporal strategies, enabling robust coordination.

翻译：部署在复杂现实场景中的大语言模型智能体通常作为空间分布式实体运行。然而，这种物理分布将智能体限制在有限的局部感知和有限的时间视野内。我们将此瓶颈表征为时空部分可观测性。在这种碎片化的认知下，分布式智能体难以高效协调。为弥合这一差距，我们提出了MACRO-LLM，一种时空部分可观测性下基于大语言模型的多智能体协同推理框架。该架构通过三个模块应对时空约束：（1）CoProposer通过预测推演验证候选动作，以减轻时间不确定性；（2）Negotiator通过平均场统计聚合解决冲突，以克服空间短视；（3）Introspector通过分析历史经验，借助语义梯度下降优化策略，确保持续适应。在合作式自适应巡航控制和疫情控制这两个复杂长时程任务上的大量评估表明，我们的框架通过空间与时间策略有效缓解了时空部分可观测性，实现了鲁棒的协同。