# A Survey of Vibe Coding with Large Language Models arXiv: https://arxiv.org/abs/2510.12399 ยท HTML: https://mlsanity.com/paper/2510.12399/ ## Key facts - Cited by 27 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - First posted on arXiv 14 Oct 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Built on by (most cited first, 27 of 27) - [DeepCode: Open Agentic Coding](https://mlsanity.com/paper/2512.07921/) (2025) - [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025) - [Semantic Soft Bootstrapping: Long Context Reasoning in LLMs without Reinforcement Learning](https://mlsanity.com/paper/2512.05105/) (2025) - [Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems](https://mlsanity.com/paper/2605.26302/) (2026) - [LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment](https://mlsanity.com/paper/2511.17676/) (2025) - [Your Code Agent Can Grow Alongside You with Structured Memory](https://mlsanity.com/paper/2603.13258/) (2026) - [CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing](https://mlsanity.com/paper/2605.02910/) (2026) - [SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering](https://mlsanity.com/paper/2605.17526/) (2026) - [CoTDeceptor:Adversarial Code Obfuscation Against CoT-Enhanced LLM Code Agents](https://mlsanity.com/paper/2512.21250/) (2025) - [Vibe Coding in Practice: Flow, Technical Debt, and Guidelines for Sustainable Use](https://mlsanity.com/paper/2512.11922/) (2025) - [Steering LLMs via Scalable Interactive Oversight](https://mlsanity.com/paper/2602.04210/) (2026) - [Feedback-based Automated Verification in Vibe Coding of CAS Adaptation Built on Constraint Logic](https://mlsanity.com/paper/2602.18607/) (2026) - [From Human Interfaces to Agent Interfaces: Rethinking Software Design in the Age of AI-Native Systems](https://mlsanity.com/paper/2603.20300/) (2026) - [Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study](https://mlsanity.com/paper/2606.12231/) (2026) - [Emerging from Ground: Addressing Intent Deviation in Tool-Using Agents via Deriving Real Calls into Virtual Trajectories](https://mlsanity.com/paper/2601.15120/) (2026) - [Prompt Driven Development with Claude Code: Building a Complete TUI Framework for the Ring Programming Language](https://mlsanity.com/paper/2601.17584/) (2026) - [Is Vibe Coding the Future? An Empirical Assessment of LLM Generated Codes for Construction Safety](https://mlsanity.com/paper/2604.12311/) (2026) - [Shift-Up: A Framework for Software Engineering Guardrails in AI-native Software Development -- Initial Findings](https://mlsanity.com/paper/2604.20436/) (2026) - [When RAG Chatbots Expose Their Backend: An Anonymized Case Study of Privacy and Security Risks in Patient-Facing Medical AI](https://mlsanity.com/paper/2605.00796/) (2026) - [AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers](https://mlsanity.com/paper/2605.18476/) (2026) - [Vibe-FDTR: An agent-oriented framework for reproducible frequency-domain thermoreflectance data analysis](https://mlsanity.com/paper/2607.28200/) (2026) - [The Third Restructuring of Software Form: From the Three-Tier Architecture to Storage, Models, and Agents](https://mlsanity.com/paper/2608.20201/) (2026) - [Large Language Models at the Intersection of Software Engineering and Software Security:An Evidence-Centered Structured Survey and Research Agenda](https://mlsanity.com/paper/2608.21107/) (2026) - [Introducing Consort: A Spec-First Agent Framework for Enforced, Test-Driven Development on Live Database Branches](https://mlsanity.com/paper/2609.09671/) (2026) - [Test-Driven Approaches to Software Engineering with Large Language Models: A Survey of Phases, Tasks, and Agent Skills](https://mlsanity.com/paper/2609.12012/) (2026) - [Vibe Analysis: Exploring LLM Adoption by Data Visualization Practitioners](https://mlsanity.com/paper/2609.31922/) (2026) - [Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents](https://mlsanity.com/paper/2610.20344/) (2026) ## Abstract (arXiv) The advancement of large language models (LLMs) has catalyzed a paradigm shift from code generation assistance to autonomous coding agents, enabling a novel development methodology termed "Vibe Coding" where developers validate AI-generated implementations through outcome observation rather than line-by-line code comprehension. Despite its transformative potential, the effectiveness of this emergent paradigm remains under-explored, with empirical evidence revealing unexpected productivity losses and fundamental challenges in human-AI collaboration. To address this gap, this survey provides the first comprehensive and systematic review of Vibe Coding with large language models, establishing both theoretical foundations and practical frameworks for this transformative development approach. Drawing from systematic analysis of over 1000 research papers, we survey the entire vibe coding ecosystem, examining critical infrastructure components including LLMs for coding, LLM-based coding agent, development environment of coding agent, and feedback mechanisms. We first introduce Vibe Coding as a formal discipline by formalizing it through a Constrained Markov Decision Process that captures the dynamic triadic relationship among human developers, software projects, and coding agents. Building upon this theoretical foundation, we then synthesize existing practices into five distinct development models: Unconstrained Automation, Iterative Conversational Collaboration, Planning-Driven, Test-Driven, and Context-Enhanced Models, thus providing the first comprehensive taxonomy in this domain. Critically, our analysis reveals that successful Vibe Coding depends not merely on agent capabilities but on systematic context engineering, well-established development environments, and human-agent collaborative development models.