{"ID":2898527,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03839","arxiv_id":"2507.03839","title":"Participatory Evolution of Artificial Life Systems via Semantic Feedback","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.03839","url_pdf":"https://arxiv.org/pdf/2507.03839v1","authors":"[\"Shuowen Li\",\"Kexin Wang\",\"Minglu Fang\",\"Danqi Huang\",\"Ali Asadipour\",\"Haipeng Mi\",\"Yitong Sun\"]","published":"2025-07-04T23:51:50Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.GR\"]","methods":"[]","has_code":false}