Participatory Evolution of Artificial Life Systems via Semantic Feedback
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3757369.3767620.pdf
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3.54 MB
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Author(s)
Li, Shuowen
Wang, Kexin
Fang, Minglu
Huang, Danqi
Asadipour, Ali
Mi, Haipeng
Sun, Yitong
Date Issued
December 14, 2025
Publisher
ACM|SIGGRAPH Asia 2025 Art Papers
Citation
Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, and Yitong Sun. 2025. Participatory Evolution of Artificial Life Systems via Semantic Feedback. In Proceedings of the SIGGRAPH Asia 2025 Art Papers (SA Art Papers '25). Association for Computing Machinery, New York, NY, USA, Article 24, 1–10.
Version
Final published version
Abstract
We present a semantic-feedback framework that treats natural language as a regulatory signal for evolving artificial-life systems. Instead of using prompts to select finished images, text in our system shapes the dynamics of an interactive ecosystem, allowing audiences to cultivate behaviors over time. The framework couples a learned mapping from prompts to simulation parameters with evolutionary search and vision–language evaluation, so user intent modulates both visible outcomes and the underlying generative rules. It supports iterative prompt refinement, multi-agent interaction, and the synthesis of new collective rules from community input. In a user study, participants achieved higher semantic alignment and reported a greater sense of control than with manual tuning, while behaviors remained diverse across generations. As an art-led contribution, the work reframes authoring as participatory cultivation and advances open-ended evolution as a socially distributed, not solely algorithmic, process; as a tool contribution, it offers a practical platform for co-creative generative design.
Description
SA Art Papers ’25, Hong Kong, Hong Kong
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3757369.3767620
