Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, Yitong Sun
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.
Read the full article at: arxiv.org
Complexity Digest 