• Minglu Fang
• Kexin Wang
• Longxuan Yu
• Ziling Zeng
• Jiahui Zhao
• Yifei Hu
• Ali Asadipour
• Yitong Sun

Abstract

We present a semantic feedback framework that enables natural language to guide the evolution of artificial life systems. Integrating a prompt-to-parameter encoder, a CMA-ES optimizer, and CLIP-based evaluation, the system allows user intent to modulate both visual outcomes and underlying behavioral rules. Implemented in an interactive ecosystem simulation, the framework supports prompt refinement, multi-agent interaction, and emergent rule synthesis. User studies show improved semantic alignment over manual tuning and demonstrate the system’s potential as a platform for participatory generative design and open-ended evolution.

https://doi.org/10.64560/32131236

Keywords