Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 36 references that are in the map. First posted on arXiv 25 Feb 2026.
Builds on
12 of 36 references.
References · 12
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,002
- 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
- 2024Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researcherscited by 348
- 2023Can Large Language Models Transform Computational Social Science?cited by 245
- 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 174
- 2024Mapping the Increasing Use of LLMs in Scientific Paperscited by 112
- 2025SciSciGPT: Advancing Human-AI Collaboration in the Science of Sciencecited by 26
- 2025Navigating the Risks of Using Large Language Models for Text Annotation in Social Science Researchcited by 4
- 2023Generative AI has lowered the barriers to computational social sciencescited by 2
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Citing papers per year: 2026: 3.
3 papers.