综述：图机制在 LLM 智能体系统中的角色分类与评估框架
Graph-Mediated Large Language Model Systems in the Agentic Era: Reasoning, Knowledge, Retrieval, Execution, and Coordination
想搞清楚图结构在多智能体和 RAG 系统里到底起什么作用，这篇综述把五类功能和评估方法都梳理了。
这篇 arXiv 综述提出一个操作层面的“角色—位置”分类法，把图在 LLM 系统中的功能分为推理、知识与证据、检索与记忆、执行、协调五大类。文章比较了图观察与外部计算如何引导决策、记忆表示与更新如何支撑后续任务、依赖关系如何约束执行与信息传递。作者还整理了基准测试、对比设计、过程诊断和系统成本，形成一个评估框架。最后指出自适应计算与停止、记忆维护、依赖感知恢复三个开放问题。
Graph-Mediated Large Language Model Systems in the Agentic Era: Reasoning, Knowledge, Retrieval, Execution, and Coordination
Graphs play an increasingly important role in large language model (LLM) systems, supporting structural reasoning, knowledge grounding, retrieval, and memory. As these systems evolve toward the agentic era, graphs also organize execution dependencies and multi-agent communication, linking intermediate observations and retained information to subsequent decisions. However, understanding their contribution requires distinguishing the function of each graph operation, the component responsible for its computation, and the subsequent use of its output. This survey introduces an operation-level role--locus taxonomy for reasoning, knowledge and evidence, retrieval and memory, execution, and coordination. We compare how graph observations and external computations guide decisions, how memory representations and updates support later tasks, and how dependencies constrain execution and information delivery. These comparisons distinguish improvements in local metrics from downstream task performance while retaining the conditions under which results were reported. We then organize benchmarks, comparison designs, process diagnostics, and system costs into an evaluation framework. Finally, we identify open challenges in adaptive computation and stopping, memory maintenance, and dependency-aware recovery. The survey provides a basis for assessing when graph mechanisms are useful and for evaluating their effects across components and stages.
