Large language models (LLMs) show promising performance on small-scale graph reasoning tasks but fail when handling real-world graphs with complex queries. This phenomenon arises from LLMs' working memory constraints, which result in their inability to retain long-range graph topology over extended contexts while sustaining coherent multi-step reasoning. However, real-world graphs are often structurally complex, such as Web, Transportation, Social, and Citation networks. To address these limitations, we propose GraphCogent, a collaborative agent framework inspired by human Working Memory Model that decomposes graph reasoning into specialized cognitive processes: sense, buffer, and execute. The framework consists of three modules: Sensory Module standardizes diverse graph text representations via subgraph sampling, Buffer Module integrates and indexes graph data across multiple formats, and Execution Module combines tool calling and tool creation for efficient reasoning. We also introduce Graph4real, a comprehensive benchmark that contains four domains of real-world graphs (Web, Transportation, Social, and Citation) to evaluate LLMs' graph reasoning capabilities. Our Graph4real covers 21 different graph reasoning tasks, categorized into three types (Structural Querying, Algorithmic Reasoning, and Predictive Modeling tasks), with graph scales up to 10 times larger than existing benchmarks. Experiments show that Llama3.1-8B based GraphCogent achieves a 50% improvement over massive-scale LLMs like DeepSeek-R1 (671B). Compared to state-of-the-art agent-based baseline, our framework outperforms by 20% in accuracy while reducing token usage by 80% for in-toolset tasks and 30% for out-toolset tasks. Code will be available after review.

翻译：大语言模型（LLMs）在小规模图推理任务上表现出良好性能，但在处理具有复杂查询的真实世界图时往往失效。这一现象源于LLMs的工作记忆约束，导致其无法在长上下文范围内保持长程图拓扑结构的同时维持连贯的多步推理。然而，真实世界图（如Web网络、交通网络、社交网络和引文网络）通常结构复杂。为应对这些局限，我们提出GraphCogent——一个受人类工作记忆模型启发的协作智能体框架，将图推理分解为三个专门化认知过程：感知、缓冲与执行。该框架包含三个模块：感知模块通过子图采样标准化多样化的图文本表示，缓冲模块集成并索引多格式图数据，执行模块结合工具调用与工具创建以实现高效推理。我们还提出了Graph4real，一个涵盖四类真实世界图（Web、交通、社交和引文网络）的综合基准，用于评估LLMs的图推理能力。Graph4real包含21种不同的图推理任务，分为三种类型（结构查询、算法推理和预测建模任务），其图规模可达现有基准的10倍。实验表明，基于Llama3.1-8B的GraphCogent相比DeepSeek-R1（671B）等超大规模LLMs实现了50%的性能提升。与最先进的基于智能体的基线方法相比，本框架在工具集内任务中准确率提升20%的同时减少80%的token消耗，在工具集外任务中减少30%的token消耗。代码将在评审后公开。