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.
@article{arxiv.2507.03839,
title = {Participatory Evolution of Artificial Life Systems via Semantic Feedback},
author = {Shuowen Li and Kexin Wang and Minglu Fang and Danqi Huang and Ali Asadipour and Haipeng Mi and Yitong Sun},
journal= {arXiv preprint arXiv:2507.03839},
year = {2025}
}