用本地化多智能体系统自动整理文献并撰写相关工作
ResearchPilot: A Local-First Multi-Agent System for Literature Synthesis and Related Work Drafting
- 基于多智能体协作，从论文摘要中提取结构化信息
- 支持本地运行，可自定义模型密钥和嵌入方式
- 适合科研人员快速生成文献综述初稿
ResearchPilot 是一个开源、可自托管的多智能体文献综述辅助系统。给定自然语言研究问题后，它从 Semantic Scholar 和 arXiv 检索论文，从摘要中提取结构化发现，整合跨论文模式，并生成带引用意识的相关工作段落。系统采用 FastAPI、Next.js、DSPy、SQLite 与 Qdrant 构建，支持本地优先架构，可接入自备密钥的模型及本地或远程嵌入。本文介绍系统设计、类型化智能体接口、持久化与历史搜索机制，以及工程权衡。不强调算法新颖性，而是作为系统贡献展示，通过自动化测试和端到端本地运行进行评估。讨论了外部 API 限速、仅依赖摘要提取、语料覆盖不全和缺乏引用验证等局限。
原文摘要 · Abstract (English)
ResearchPilot is an open-source, self-hostable multi-agent system for literature-review assistance. Given a natural-language research question, it retrieves papers from Semantic Scholar and arXiv, extracts structured findings from paper abstracts, synthesizes cross-paper patterns, and drafts a citation-aware related-work section. The system combines FastAPI, Next.js, DSPy, SQLite, and Qdrant in a local-first architecture that supports bring-your-own-key model access and remote-or-local embeddings. This paper describes the system design, typed agent interfaces, persistence and history-search mechanisms, and the engineering tradeoffs involved in building a transparent research assistant. Rather than claiming algorithmic novelty, we present ResearchPilot as a systems contribution and evaluate it through automated tests and end-to-end local runs. We discuss limitations including external API rate limits, abstract-only extraction, incomplete corpus coverage, and the lack of citation verification.
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