Large Language Models (LLMs) demonstrate remarkable potential in dynamic graph reasoning, but suffer from a scaling bottleneck: current models can only handle graphs with tens of nodes, constrained by exponential reasoning overhead and finite context windows. While multi-agent systems (MAS) offer collective reasoning and topology-aware orchestration, capabilities naturally suited for graph-structured tasks, their application to dynamic graphs remains unexplored. This paper presents Scaling LLM Reasoning on Dynamic Graphs via Adaptive Spatio-Temporal Multi-Agent Collaboration (AdaSTORM), a framework that reformulates large-scale dynamic graph reasoning into two stages: (i) Adaptive Partitioning, partitioning large-scale dynamic graphs into subregions that match the model's reasoning capacity while minimizing inference cost; and (ii) Collaborative Reasoning, aligning graph partition topologies with a spatio-temporal decoupled multi-agent architecture. AdaSTORM is the first multi-agent framework tailored for dynamic graph reasoning. Extensive experiments show that AdaSTORM successfully breaks through the scaling bottleneck, scaling reasoning to thousand-node graphs with over 90% accuracy across several large-scale dynamic graph settings without external tools, significantly outperforms seven competitive baselines. Furthermore, it achieves state-of-the-art accuracy on existing benchmarks and generalizes robustly to real-world datasets. The source code is available at: https://github.com/irisorchid107/AdaSTORM/.

翻译：大型语言模型在动态图推理中展现出显著潜力，但受限于指数级推理开销和有限上下文窗口，当前模型仅能处理包含数十个节点的图，存在扩展瓶颈。尽管多智能体系统具备集体推理与拓扑感知编排能力（这些能力天然适配图结构任务），但其在动态图上的应用尚未被探索。本文提出"通过自适应时空多智能体协作实现动态图上的大语言模型推理扩展"框架，将大规模动态图推理重构为两个阶段：（i）自适应划分——将大规模动态图分割为匹配模型推理能力且最小化推理成本的子区域；（ii）协作推理——将图划分拓扑与时空解耦的多智能体架构对齐。AdaSTORM是首个专为动态图推理设计的的多智能体框架。大量实验表明，AdaSTORM成功突破扩展瓶颈，在不借助外部工具的情况下，可将推理规模扩展至数千节点图，并在多个大规模动态图场景中达到超过90%的准确率，显著优于七个竞争基线。此外，它在现有基准上实现最先进准确率，并稳健泛化至真实世界数据集。源代码见：https://github.com/irisorchid107/AdaSTORM/。