Participatory Evolution of Artificial Life Systems via Semantic Feedback
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 4 Jul 2025.
Builds on
10 of 23 references.
References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
- 2021Text2Mesh: Text-Driven Neural Stylization for Meshescited by 334
- 2021Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Conceptscited by 40
- 2024Automating the Search for Artificial Life with Foundation Modelscited by 20
- 2020Networked Multi-Agent Reinforcement Learning with Emergent Communicationcited by 13
- 2024Covariance Matrix Adaptation Evolution Strategy for Low Effective Dimensionalitycited by 2
- 2024Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspectivecited by 1
Built on by
Not cited yet.