AI代理可自主完成社科研究全流程，但难替代理论创新。
Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?
- 构建认知任务框架，划分研究活动的可编码性与隐性知识需求
- 26项技能插件实现从选题到投稿的18阶段自动化流程，53个质量关卡
- 适合需高效执行的研究者，但理论原创仍需人类主导
AI代理——具备持续状态、工具访问和专业技能的多步骤推理系统——标志着社会科学研究中的一次质变。与仅响应单次提问的聊天机器人不同，现代AI代理能读取文件、运行代码、查询数据库、网络搜索并调用领域专用技能，独立完成完整研究流程。本文提出‘氛围研究’（vibe researching）概念，以覆盖研究全周期的26项技能插件（scholar-skill）为案例，该插件在18个协调阶段中设有53个质量关卡。通过构建双维度认知任务框架（可编码性与隐性知识要求），识别出非顺序性的委托边界：贯穿每个研究阶段，而非阶段之间。论文认为AI代理擅长速度、覆盖范围与方法支撑，但在理论原创性和领域隐性知识上存在短板。最后分析了三大职业影响——增强的脆弱条件、分层风险与教学危机，并提出五条负责任的氛围研究原则。
原文摘要 · Abstract (English)
AI agents -- systems that execute multi-step reasoning workflows with persistent state, tool access, and specialist skills -- represent a qualitative shift from prior automation technologies in social science. Unlike chatbots that respond to isolated queries, AI agents can now read files, run code, query databases, search the web, and invoke domain-specific skills to execute entire research pipelines autonomously. This paper introduces the concept of vibe researching -- the AI-era parallel to vibe coding -- and uses scholar-skill, a 26-skill plugin for Claude Code covering the full research pipeline from idea to submission across 18 orchestrated phases with 53 quality gates, as an illustrative case. I develop a cognitive task framework that classifies research activities along two dimensions -- codifiability and tacit knowledge requirement -- to identify a delegation boundary that is cognitive, not sequential: it cuts through every stage of the research pipeline, not between stages. I argue that AI agents excel at speed, coverage, and methodological scaffolding but struggle with theoretical originality and tacit field knowledge. The paper concludes with an analysis of three implications for the profession -- augmentation with fragile conditions, stratification risk, and a pedagogical crisis -- and proposes five principles for responsible vibe researching.
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