Evolution & Foundation: AI Shares Creative Control

• URL: http://arxiv.org/abs/2606.16849v2
• Date: Wed, 17 Jun 2026 10:18:34 GMT
• Title: Evolution & Foundation: AI Shares Creative Control
• Abstract summary: This paper investigates the creative process of automated design and artistic evaluation using an evolutionary system.<n>We consider how a multimodal artificial intelligence model can communicate and guide a combined generative and evolutionary computational system.
• Score: 1.3968275725004586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the creative process of automated design and artistic evaluation using an evolutionary system. We consider how a multimodal artificial intelligence (AI) model can communicate and guide a combined generative and evolutionary computational system. This creates a framework for the evolution of aesthetically pleasing complex 3D organic forms by integrating genetic algorithms with the visual reasoning capabilities of large-scale AI foundation models. The framework shifts the artist role from that of intensive direct selection to one of system design; transferring detailed step-by-step curation to an AI agent capable of multimodal aesthetic judgement. This framework enables the human artist/designer to rapidly traverse large areas of multi-dimensional evolutionary parameter space to find creative outcomes based on their semantic targets. Detailed audit trails of the AI's aesthetic reasoning are generated for each experiment. Interactive visualisation tools, together with AI-generated summaries and evolutionary narratives, enable deep exploration into each evolutionary experiment and providing a transparent insight into the AI-guided process.

Related papers

• Intentmaking and Sensemaking: Human Interaction with AI-Guided Mathematical Discovery [72.49593499512092]
  We identify and characterize a distinct workflow we term intentmaking.<n>We frame this as a natural extension to sensemaking.<n>Our documentation of these themes suggests an approach to designing AI tools for scientific discovery.
  arXiv  Detail & Related papers  (2026-05-07T09:30:25Z)
• Evolving Cognitive Architectures [51.56484100374058]
  This article proposes a research and development direction that would lead to the creation of next-generation intelligent technical systems.<n>A distinctive feature of these systems is their ability to undergo evolutionary change.
  arXiv  Detail & Related papers  (2025-12-29T10:09:20Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems [0.6526824510982802]
  This paper explores the emergent paradigm of human-AI co-creation, where AI is not merely used for automation or efficiency gains, but actively participates in ideation, visual conceptualization, and decision-making.<n>Specifically, we investigate the use of large language models (LLMs) like GPT-4 and multimodal diffusion models such as Stable Diffusion as creative agents that engage designers in iterative cycles of proposal, critique, and revision.
  arXiv  Detail & Related papers  (2025-07-22T04:29:33Z)
• Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework [0.0]
  This paper develops a typology of agentic AI systems, introducing eight dimensions that define their cognitive and environmental agency in an ordinal structure.<n>The framework enables researchers and practitioners to analyze varying levels of agency in AI systems.
  arXiv  Detail & Related papers  (2025-07-07T14:05:30Z)
• Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy [0.8399688944263844]
  AI is reshaping inverse design in manufacturing, enabling high-performance discovery in materials, products, and processes.<n>However, purely data-driven approaches often struggle in realistic manufacturing settings characterized by sparse data, high-dimensional design spaces, and complex constraints.<n>This perspective proposes an integrated framework built on three complementary pillars: domain knowledge to establish physically meaningful objectives and constraints while removing variables with limited relevance, physics-informed machine learning to enhance generalization under limited or biased data, and large language model-based interfaces to support intuitive, human-centered interaction.
  arXiv  Detail & Related papers  (2025-05-29T08:15:27Z)
• AI-powered virtual eye: perspective, challenges and opportunities [9.758442949590599]
  We envision the "virtual eye" as a next-generation, AI-powered platform that uses interconnected foundation models to simulate the eye's intricate structure and biological function across all scales.<n>Despite challenges in interpretability, ethics, data processing and evaluation, the virtual eye holds the potential to revolutionize personalized ophthalmic care and accelerate research into ocular health and disease.
  arXiv  Detail & Related papers  (2025-05-07T14:48:56Z)
• Building Artificial Intelligence with Creative Agency and Self-hood [0.0]
  This paper is an invited layperson summary for The Academic of the paper referenced on the last page. We summarize how the formal framework of autocatalytic networks offers a means of modeling the origins of self-organizing, self-sustaining structures.
  arXiv  Detail & Related papers  (2024-06-09T22:28:11Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• Creative Discovery using QD Search [4.941630596191806]
  This paper introduces a method that combines evolutionary optimisation with AI-based image classification to perform quality-diversity search. We tested our method on a generative system that produces abstract drawings.
  arXiv  Detail & Related papers  (2023-05-08T05:11:02Z)

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.