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Related Experiment Video

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Autonomous language-image generation loops converge to generic visual motifs.

Arend Hintze1,2, Frida Proschinger Åström1, Jory Schossau2

  • 1Department of Data Analytics, Dalarna University, Falun, Sweden.

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Summary

Autonomous AI systems, like Stable Diffusion XL (SDXL) and Large Language and Vision Assistant (LLaVA), tend to produce generic creative outputs. This research reveals a significant collapse in diversity, highlighting the need for new AI mechanisms.

Keywords:
attractor dynamicsautonomous AI systemscomputational creativityconvergencedriftgenerative AIlarge language modelvision-language models

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Area of Science:

  • Artificial Intelligence
  • Machine Creativity
  • Human-Computer Interaction

Background:

  • AI-to-AI creative systems offer novel avenues for machine creativity.
  • Concerns exist regarding the potential for these systems to generate homogenous outputs.

Purpose of the Study:

  • To investigate the convergence tendencies of autonomous AI-to-AI creative systems.
  • To analyze the diversity and motifs generated by iterated cross-modal AI feedback loops.

Main Methods:

  • Developed iterative feedback loops between Stable Diffusion XL (image generation) and LLaVA (image description).
  • Executed 700 trajectories across diverse prompts and 7 temperature settings over 100 iterations.
  • Quantitatively analyzed visual outputs for dominant motifs and aesthetic patterns.

Main Results:

  • All AI-to-AI creative system runs converged to highly similar, generic visuals, termed 'visual elevator music'.
  • Identified 12 dominant motifs characterized by commercially safe aesthetics (e.g., stormy lighthouses, palatial interiors).
  • Convergence persisted across different model pairs, suggesting structural limitations in cross-modal AI.

Conclusions:

  • Autonomous AI-to-AI systems exhibit inherent homogenizing tendencies, collapsing creative diversity.
  • This convergence mirrors human cultural transmission biases but results in complete diversity loss.
  • There is a critical need for anti-convergence mechanisms and human-AI collaboration to foster genuine creative diversity.