Assembling the CREW: A Collaborative Multi-agent Reinforcement Learning Framework for Automated Related Work Generation

2026-09-14

AI Summary

本文提出CREW框架，通过多智能体强化学习动态协作生成相关工作综述，解决了传统方法中静态协作的局限性，提高了文献合成的质量并降低了成本。

Abstract

Automatic Related Work Generation (RWG) significantly reduces the human time and effort required to author the Related Work Section (RWS) of a research paper. However, prior methods leveraging multi-agent Large Language Models (LLMs) typically rely on a predefined workflow, where each agent is responsible for a specific step in the entire process. This rigid, static inter-agent coordination limits the adaptive collaboration required to synthesize complex scientific literature. To address this limitation, we propose CREW (Collaborative Reinforcement Learning for Related Work Generation), a novel framework where LLM agents bypass heuristic pipelines to dynamically coordinate by autonomously selecting actions, such as Retrieve, Disseminate, Compose, and Critique, driven by a policy optimized via Independent Proximal Policy Optimization (IPPO). Extensive experiments on a standard RWG benchmark demonstrate that our approach yields substantial quality improvements over strong existing baselines, while significantly reducing token costs. Code is available at https://github.com/YenPBao/CREW-Collaborative-MARL.git

Problem

• Automatic Related Work Generation
• Multi-agent Reinforcement Learning
• Large Language Models
• Collaborative Framework

Innovation

• Collaborative Reinforcement Learning
• Dynamic Coordination
• Independent Proximal Policy Optimization (IPPO)
• Automatic Related Work Generation