# Creativity in AI as Emergence from Domain-Limited Generative Models

arXiv: https://arxiv.org/abs/2601.08388 · HTML: https://mlsanity.com/paper/2601.08388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 13 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs](https://mlsanity.com/paper/2305.16339/) (2023)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [On the Creativity of Large Language Models](https://mlsanity.com/paper/2304.00008/) (2023)
- [Assessing and Understanding Creativity in Large Language Models](https://mlsanity.com/paper/2401.12491/) (2024)
- [Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity](https://mlsanity.com/paper/2303.12003/) (2023)
- [Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach](https://mlsanity.com/paper/2504.15784/) (2025)
- [The General Theory of General Intelligence: A Pragmatic Patternist Perspective](https://mlsanity.com/paper/2103.15100/) (2021)
- [DeepCreativity: Measuring Creativity with Deep Learning Techniques](https://mlsanity.com/paper/2201.06118/) (2022)
- [Evaluating Large Language Model Creativity from a Literary Perspective](https://mlsanity.com/paper/2312.03746/) (2023)
- [Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability](https://mlsanity.com/paper/2504.12320/) (2025)
- [Creativity of Deep Learning: Conceptualization and Assessment](https://mlsanity.com/paper/2012.02282/) (2020)

## Abstract (arXiv)

Creativity in artificial intelligence is most often addressed through evaluative frameworks that aim to measure novelty, diversity, or usefulness in generated outputs. While such approaches have provided valuable insights into the behavior of modern generative models, they largely treat creativity as a property to be assessed rather than as a phenomenon to be explicitly modeled. In parallel, recent advances in large-scale generative systems, particularly multimodal architectures, have demonstrated increasingly sophisticated forms of pattern recombination, raising questions about the nature and limits of machine creativity. This paper proposes a generative perspective on creativity in AI, framing it as an emergent property of domain-limited generative models embedded within bounded informational environments. Rather than introducing new evaluative criteria, we focus on the structural and contextual conditions under which creative behaviors arise. We introduce a conceptual decomposition of creativity into four interacting components-pattern-based generation, induced world models, contextual grounding, and arbitrarity, and examine how these components manifest in multimodal generative systems. By grounding creativity in the interaction between generative dynamics and domain-specific representations, this work aims to provide a technical framework for studying creativity as an emergent phenomenon in AI systems, rather than as a post hoc evaluative label.