用模块化智能体系统，让大模型自动生成深度综述。
LLM$\times$MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System
- 将综述生成拆分为独立模块，通过MCP服务器协同工作。
- 多轮交互下生成内容更深入、篇幅更长的综述骨架。
- 适合需要定制化研究视角的学者快速构建高质量综述。
我们提出 LLM×MapReduce-V3，一种用于长篇综述生成的分层模块化智能体系统。在前序工作 LLM×MapReduce-V2 基础上，本版本引入多智能体架构，将骨架初始化、摘要构建、骨架优化等功能组件分别实现为独立的模型-上下文-协议（MCP）服务器。这些原子服务器可组合成更高层级的服务，形成分层结构。高层规划智能体根据MCP工具描述与执行历史动态调度模块流程。该模块化设计支持人机协同干预，提升用户对研究过程的控制力与可定制性。通过多轮交互，系统能精准捕捉研究意图，生成全面的综述骨架，并进一步扩展为深度综述。人类评估显示，该系统在内容深度与长度上均优于代表性基线，验证了基于MCP的模块化规划的有效性。
原文摘要 · Abstract (English)
We introduce LLM x MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM x MapReduce-V2, this version incorporates a multi-agent architecture where individual functional components, such as skeleton initialization, digest construction, and skeleton refinement, are implemented as independent model-context-protocol (MCP) servers. These atomic servers can be aggregated into higher-level servers, creating a hierarchically structured system. A high-level planner agent dynamically orchestrates the workflow by selecting appropriate modules based on their MCP tool descriptions and the execution history. This modular decomposition facilitates human-in-the-loop intervention, affording users greater control and customization over the research process. Through a multi-turn interaction, the system precisely captures the intended research perspectives to generate a comprehensive skeleton, which is then developed into an in-depth survey. Human evaluations demonstrate that our system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.
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