Creativity in AI as Emergence from Domain-Limited Generative Models

TL;DR

This paper presents a new perspective on AI creativity, viewing it as an emergent property arising from domain-limited generative models interacting with their environment, rather than solely as an evaluative measure.

Contribution

It introduces a conceptual framework decomposing creativity into four components and emphasizes modeling creativity as an emergent phenomenon in multimodal generative systems.

Findings

Creativity emerges from the interaction of generative dynamics and domain-specific representations.

A four-component decomposition of creativity is proposed: pattern generation, world models, grounding, arbitrarity.

Grounding creativity in structural interactions offers a new technical framework for AI research.

Abstract

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.