# Participatory Evolution of Artificial Life Systems via Semantic Feedback

arXiv: https://arxiv.org/abs/2507.03839 · HTML: https://mlsanity.com/paper/2507.03839/

## 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 (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts](https://mlsanity.com/paper/2109.08857/) (2021)
- [Automating the Search for Artificial Life with Foundation Models](https://mlsanity.com/paper/2412.17799/) (2024)
- [Networked Multi-Agent Reinforcement Learning with Emergent Communication](https://mlsanity.com/paper/2004.02780/) (2020)
- [Covariance Matrix Adaptation Evolution Strategy for Low Effective Dimensionality](https://mlsanity.com/paper/2412.01156/) (2024)
- [Exploring Fungal Morphology Simulation and Dynamic Light Containment from a Graphics Generation Perspective](https://mlsanity.com/paper/2409.05171/) (2024)

## 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.