# Evolutionary ecology of words

arXiv: https://arxiv.org/abs/2505.05863 · HTML: https://mlsanity.com/paper/2505.05863/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 11 of 11)

- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Language Model Crossover: Variation through Few-Shot Prompting](https://mlsanity.com/paper/2302.12170/) (2023)
- [Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap](https://mlsanity.com/paper/2401.10034/) (2024)
- [Machine Psychology](https://mlsanity.com/paper/2303.13988/) (2023)
- [An evolutionary model of personality traits related to cooperative behavior using a large language model](https://mlsanity.com/paper/2310.05976/) (2023)
- [Collective Innovation in Groups of Large Language Models](https://mlsanity.com/paper/2407.05377/) (2024)
- [From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models](https://mlsanity.com/paper/2407.09502/) (2024)
- [Large Language Models as In-context AI Generators for Quality-Diversity](https://mlsanity.com/paper/2404.15794/) (2024)
- [Evolutionary latent space search for driving human portrait generation](https://mlsanity.com/paper/2204.11887/) (2022)

## Built on by (most cited first, 1 of 1)

- [How memory can affect collective and cooperative behaviors in an LLM-Based Social Particle Swarm](https://mlsanity.com/paper/2604.12250/) (2026)

## Abstract (arXiv)

We propose a model for the evolutionary ecology of words as one attempt to extend evolutionary game theory and agent-based models by utilizing the rich linguistic expressions of Large Language Models (LLMs). Our model enables the emergence and evolution of diverse and infinite options for interactions among agents. Within the population, each agent possesses a short word (or phrase) generated by an LLM and moves within a spatial environment. When agents become adjacent, the outcome of their interaction is determined by the LLM based on the relationship between their words, with the loser's word being replaced by the winner's. Word mutations, also based on LLM outputs, may occur. We conducted preliminary experiments assuming that ``strong animal species" would survive. The results showed that from an initial population consisting of well-known species, many species emerged both gradually and in a punctuated equilibrium manner. Each trial demonstrated the unique evolution of diverse populations, with one type of large species becoming dominant, such as terrestrial animals, marine life, or extinct species, which were ecologically specialized and adapted ones across diverse extreme habitats. We also conducted a long-term experiment with a large population, demonstrating the emergence and coexistence of diverse species.