Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?

• URL: http://arxiv.org/abs/2602.22401v2
• Date: Fri, 27 Feb 2026 18:49:27 GMT
• Title: Vibe Researching as Wolf Coming: Can AI Agents with Skills Replace or Augment Social Scientists?
• Abstract summary: This paper introduces the concept of vibe researching -- the AI-era parallel to vibe coding (Karpathy, 2025)<n>I develop a cognitive task framework that classifies research activities along two dimensions -- codifiability and tacit knowledge requirement.<n>I argue that AI agents excel at speed, coverage, and methodological scaffolding but struggle with theoretical originality and tacit field knowledge.
• Score: 4.181639770490221
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 (Karpathy, 2025) -- and uses scholar-skill, a 23-skill plugin for Claude Code covering the full research pipeline from idea to submission, 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.

Related papers

• Method, Mind, and Morality: How People Make Sense of Artificial Intelligence [10.2319621391971]
  How can humans make sense of the rapid takeoff of artificial intelligence (AI)?<n>We studied the sensemaking dynamics of AI through an open-ended, mixed-methods study with computational text analysis of millions of AI-related newspaper articles and social media posts.<n>We identify a range of sociological frames (interpretive schemas that structure collective cognition) and show how AI professionals use frames to address significant cognitive challenges.
  arXiv  Detail & Related papers  (2026-08-25T15:52:55Z)
• AI for Auto-Research: Roadmap & User Guide [107.0834449839233]
  We present an end-to-end analysis of AI across the complete research lifecycle.<n>We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy.<n>We show that greater automation can obscure rather than eliminate failure modes.
  arXiv  Detail & Related papers  (2026-05-18T17:08:26Z)
• AcademiClaw: When Students Set Challenges for AI Agents [69.70303995121154]
  AcademiClaw is a benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic disconnects.<n>We show that even the best of models achieves only a 55% pass rate.<n>We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community.
  arXiv  Detail & Related papers  (2026-05-04T14:40:42Z)
• Generative-AI and the transformation of workforce. A job postings-driven analysis [7.765662835417495]
  This paper investigates how generative-artificial intelligence AI is reshaping job requirements, skill compositions and sectoral dynamics across global labor markets.<n>It examines the evolving frequency and framing of AI-related competencies in job postings, exploring whether generative-AI functions primarily as an augmentative or substitutive force in the workplace.
  arXiv  Detail & Related papers  (2026-04-07T17:02:43Z)
• AI Co-Scientist for Ranking: Discovering Novel Search Ranking Models alongside LLM-based AI Agents with Cloud Computing Access [60.37646376282544]
  We present an AI Co-Scientist framework that automates the full search ranking research pipeline.<n>This is the first study in the ranking community to utilize an AI Co-Scientist framework for algorithmic research.
  arXiv  Detail & Related papers  (2026-03-23T09:29:08Z)
• The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning [22.71288370686935]
  This paper is a practical guide to AI-assisted research in mathematics and machine learning.<n>It is organized into three parts: (I) a five-level taxonomy of AI integration, (II) an open-source framework that turns CLI coding agents into autonomous research assistants, and (III) case studies from deep learning and mathematics.
  arXiv  Detail & Related papers  (2026-03-16T21:02:06Z)
• SoK: Agentic Skills -- Beyond Tool Use in LLM Agents [6.356997609995175]
  Agentic systems increasingly rely on reusable procedural capabilities, textita.k.a., agentic skills, to execute long-horizon reliably.<n>This paper maps the skill layer across the full lifecycle (discovery, practice, distillation, storage, composition, evaluation, and update)<n>We analyze the security and governance implications of skill-based agents, covering supply-chain risks, prompt injection via skill payloads, and trust-tiered execution.
  arXiv  Detail & Related papers  (2026-02-24T13:11:38Z)
• AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agents [49.67355440164857]
  We introduce AIRS-Bench, a suite of 20 tasks sourced from state-of-the-art machine learning papers.<n>Airs-Bench tasks assess agentic capabilities over the full research lifecycle.<n>We open-source the AIRS-Bench task definitions and evaluation code to catalyze further development in autonomous scientific research.
  arXiv  Detail & Related papers  (2026-02-06T16:45:02Z)
• The AI Roles Continuum: Blurring the Boundary Between Research and Engineering [0.0]
  We propose a framework in which Research Scientists, Research Engineers, Applied Scientists, and Machine Learning Engineers occupy overlapping positions rather than discrete categories.<n>We show that core competencies such as distributed systems design, large-scale training and optimization, rigorous experimentation, and publication-minded inquiry are now broadly shared across titles.<n>Treating roles as fluid rather than siloed shortens research-to-production loops, improves iteration velocity, and strengthens organizational learning.
  arXiv  Detail & Related papers  (2025-12-31T05:00:58Z)
• AI Sprints: Towards a Critical Method for Human-AI Collaboration [0.0]
  This article introduces the possibility for new forms of humanistic inquiry through what I term 'AI sprints'<n>I demonstrate how tight loops of iterative development can adapt data and book sprint methodologies whilst acknowledging the profound transformations generative AI introduces.<n>The paper contributes both a practical methodology for intensive AI-augmented research and a theoretical framework for understanding the transformations of this hybrid method.
  arXiv  Detail & Related papers  (2025-12-13T15:56:11Z)
• Rethinking Science in the Age of Artificial Intelligence [0.0]
  We argue that AI must augment but not replace human judgment in academic academic such as peer review, ethical evaluation, and validation.<n>This paper calls for the deliberate adoption of AI within the scientific practice through policies that promote transparency, validation, and accountability.
  arXiv  Detail & Related papers  (2025-11-13T17:26:00Z)
• What Work is AI Actually Doing? Uncovering the Drivers of Generative AI Adoption [1.4977849232424492]
  This study investigates which intrinsic task characteristics drive users' decisions to delegate work to AI systems.<n>This research provides the first systematic evidence linking real-world generative AI usage to a comprehensive, multi-dimensional framework of intrinsic task characteristics.
  arXiv  Detail & Related papers  (2025-10-26T19:13:37Z)
• Executable Knowledge Graphs for Replicating AI Research [65.41207324831583]
  Executable Knowledge Graphs (xKG) is a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature.<n>Code will released at https://github.com/zjunlp/xKG.
  arXiv  Detail & Related papers  (2025-10-20T17:53:23Z)
• SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam? [51.112225746095746]
  We introduce X-Master, a tool-augmented reasoning agent designed to emulate human researchers.<n>X-Masters sets a new state-of-the-art record on Humanity's Last Exam with a score of 32.1%.
  arXiv  Detail & Related papers  (2025-07-07T17:50:52Z)
• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.<n>This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI [10.084913433923566]
  AI-based systems impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse.<n>This study examines the use of Large Language Models (LLM) for AI ethics in practice.<n>We design a prototype, where agents engage in structured discussions on real-world AI ethics issues from the AI Incident Database.
  arXiv  Detail & Related papers  (2024-10-25T20:17:59Z)
• Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI [116.8199519880327]
  Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General Intelligence (AGI)<n>In this survey, we give a comprehensive exploration of the latest advancements in Embodied AI.
  arXiv  Detail & Related papers  (2024-07-09T14:14:47Z)
• Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning [50.47568731994238]
  Key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL) This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
  arXiv  Detail & Related papers  (2023-12-22T17:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.