LLM$\times$MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System

• URL: http://arxiv.org/abs/2510.10890v1
• Date: Mon, 13 Oct 2025 01:38:37 GMT
• Title: LLM$\times$MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System
• Authors: Yu Chao, Siyu Lin, xiaorong wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun,
• Abstract summary: LLM x MapReduce-V3 is a hierarchically modular agent system for long-form survey generation.<n>System captures research perspectives to generate a comprehensive skeleton, which is then developed into an in-depth survey.<n>Human evaluations demonstrate that our system surpasses representative baselines in both content depth and length.
• Score: 55.33058620876928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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