Vibe Modeling: Challenges and Opportunities

• URL: http://arxiv.org/abs/2507.23120v1
• Date: Wed, 30 Jul 2025 21:50:06 GMT
• Title: Vibe Modeling: Challenges and Opportunities
• Abstract summary: Textitvibe modeling is a novel approach to integrate the best of both worlds (AI and MDE) to speed up the development of reliable complex systems.<n>We outline the key concepts of vibe modeling and highlight the opportunities and open challenges it presents for the future of modeling.
• Score: 2.3170227013988947
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems. New types of user interfaces, the need for intelligent components, sustainability concerns, ... bring new challenges that we need to handle. In the last years, model-driven engineering (MDE) has been key to improving the quality and productivity of software development, but models themselves are becoming increasingly complex to specify and manage. At the same time, we are witnessing the growing popularity of vibe coding approaches that rely on Large Language Models (LLMs) to transform natural language descriptions into running code at the expenses of code vulnerabilities, scalability issues and maintainability concerns. In this paper, we introduce the concept of \textit{vibe modeling} as a novel approach to integrate the best of both worlds (AI and MDE) to speed up the development of reliable complex systems. We outline the key concepts of vibe modeling and highlight the opportunities and open challenges it presents for the future of modeling.

Related papers

• The Case for Vibe Modeling: A Missing Step in AI-Based Trustworthy Software Development [0.7783262415147654]
  We argue that current AI-based development practices focus too heavily on the direct generation of code.<n>We argue for vibe modeling as a lightweight intermediate abstraction between natural language interaction and code generation.
  arXiv  Detail & Related papers  (2026-07-30T09:37:02Z)
• A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
  The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
  arXiv  Detail & Related papers  (2026-07-10T04:02:22Z)
• Accelerating AI-Powered Research: The PuppyChatter Framework for Usable and Flexible Tooling [1.631115063641726]
  PuppyChatter is a novel framework designed to preserve the intuitive simplicity of vendor-specific SDKs while adhering to the vendor-neutrality principles characteristic of model abstraction.
  arXiv  Detail & Related papers  (2026-05-18T03:33:05Z)
• Vibe-driven model-based engineering [1.7471807152477032]
  Textitvibe-driven model-based engineering is a novel approach to integrate the best of both worlds (AI and MDE) to accelerate the development of reliable complex systems.
  arXiv  Detail & Related papers  (2026-04-12T13:47:06Z)
• Generative AI and the Transformation of Software Development Practices [0.0]
  Generative AI is reshaping how software is designed, written, and maintained.<n>This paper examines how AI-assisted techniques are changing software engineering practice.
  arXiv  Detail & Related papers  (2025-10-12T22:02:10Z)
• Continual Learning for Generative AI: From LLMs to MLLMs and Beyond [56.29231194002407]
  We present a comprehensive survey of continual learning methods for mainstream generative AI models.<n>We categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based.<n>We analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones.
  arXiv  Detail & Related papers  (2025-06-16T02:27:25Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications [57.38526350775472]
  This tutorial provides a systematic introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies.<n>We outline the background of 6G communications, review the technological evolution from LAMs to Agentic AI, and clarify the tutorial's motivation and main contributions.
  arXiv  Detail & Related papers  (2025-05-28T12:54:07Z)
• LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models [50.259006481656094]
  We present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models. Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer. We present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.
  arXiv  Detail & Related papers  (2024-04-03T23:57:34Z)
• On the Challenges and Opportunities in Generative AI [157.96723998647363]
  We argue that current large-scale generative AI models exhibit several fundamental shortcomings that hinder their widespread adoption across domains.<n>We aim to provide researchers with insights for exploring fruitful research directions, thus fostering the development of more robust and accessible generative AI solutions.
  arXiv  Detail & Related papers  (2024-02-28T15:19:33Z)
• Low-Modeling of Software Systems [2.3170227013988947]
  New types of user interfaces, the need for intelligent components, sustainability concerns,... bring new challenges that we need to handle. In this paper, we present the concept of low-modeling as a solution to enhance current model-driven engineering techniques.
  arXiv  Detail & Related papers  (2024-02-28T14:50:27Z)
• Reusable MLOps: Reusable Deployment, Reusable Infrastructure and Hot-Swappable Machine Learning models and services [0.0]
  We introduce a new sustainable concept in the field of AI/ML operations - called Reusable MLOps. We reuse the existing deployment and infrastructure to serve new models by hot-swapping them without tearing down the infrastructure or the microservice.
  arXiv  Detail & Related papers  (2024-02-19T23:40:46Z)
• TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System [14.019244136838017]
  TrainerAgent is a multi-agent framework including Task, Data, Model and Server agents. These agents analyze user-defined tasks, input data, and requirements (e.g., accuracy, speed), optimizing them from both data and model perspectives to obtain satisfactory models, and finally deploy these models as online service. This research presents a significant advancement in achieving desired models with increased efficiency and quality as compared to traditional model development.
  arXiv  Detail & Related papers  (2023-11-11T17:39:24Z)
• Model-based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era [2.712076884994214]
  We propose the concept of 'Model-based Programming' and present a novel programming language - M Language. M Language treats models as basic computational units, enabling developers to concentrate more on crucial tasks.
  arXiv  Detail & Related papers  (2023-05-12T09:38:11Z)
• INTERN: A New Learning Paradigm Towards General Vision [117.3343347061931]
  We develop a new learning paradigm named INTERN. By learning with supervisory signals from multiple sources in multiple stages, the model being trained will develop strong generalizability. In most cases, our models, adapted with only 10% of the training data in the target domain, outperform the counterparts trained with the full set of data.
  arXiv  Detail & Related papers  (2021-11-16T18:42:50Z)
• Data-Driven and SE-assisted AI Model Signal-Awareness Enhancement and Introspection [61.571331422347875]
  We propose a data-driven approach to enhance models' signal-awareness. We combine the SE concept of code complexity with the AI technique of curriculum learning. We achieve up to 4.8x improvement in model signal awareness.
  arXiv  Detail & Related papers  (2021-11-10T17:58:18Z)

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.