# Evolving Neural Networks Reveal Emergent Collective Behavior from Minimal Agent Interactions

arXiv: https://arxiv.org/abs/2410.19718 · HTML: https://mlsanity.com/paper/2410.19718/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 52 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Self-Organization and Artificial Life](https://mlsanity.com/paper/1903.07456/) (2019)
- [Intrinsically motivated collective motion](https://mlsanity.com/paper/1907.08097/) (2019)

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

- [SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks](https://mlsanity.com/paper/2605.28764/) (2026)

## Abstract (arXiv)

Understanding the mechanisms behind emergent behaviors in multi-agent systems is critical for advancing fields such as swarm robotics and artificial intelligence. In this study, we investigate how neural networks evolve to control agents' behavior in a dynamic environment, focusing on the relationship between the network's complexity and collective behavior patterns. By performing quantitative and qualitative analyses, we demonstrate that the degree of network non-linearity correlates with the complexity of emergent behaviors. Simpler behaviors, such as lane formation and laminar flow, are characterized by more linear network operations, while complex behaviors like swarming and flocking show highly non-linear neural processing. Moreover, specific environmental parameters, such as moderate noise, broader field of view, and lower agent density, promote the evolution of non-linear networks that drive richer, more intricate collective behaviors. These results highlight the importance of tuning evolutionary conditions to induce desired behaviors in multi-agent systems, offering new pathways for optimizing coordination in autonomous swarms. Our findings contribute to a deeper understanding of how neural mechanisms influence collective dynamics, with implications for the design of intelligent, self-organizing systems.