# Emergent aggregation from collective foraging

arXiv: https://arxiv.org/abs/2608.28046 · HTML: https://mlsanity.com/paper/2608.28046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 28 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Optimal foraging strategies can be learned](https://mlsanity.com/paper/2303.06050/) (2023)
- [Learning to flock through reinforcement](https://mlsanity.com/paper/1911.01697/) (2019)
- [Reinforcement Learning for Active Matter](https://mlsanity.com/paper/2503.23308/) (2025)
- [Learning to reset in target search problems](https://mlsanity.com/paper/2503.11330/) (2025)

## Abstract (arXiv)

Collective behaviour in living systems is usually modelled as the outcome of a direct social drive: agents are rewarded, or hard-wired, to align with or approach their neighbours. Here we show that aggregation can instead emerge from an indirect objective. We let reinforcement learning foragers, initially performing a random walk, optimize their dynamics from a purely individual reward for finding replenishable targets, while perceiving only their conspecifics and never the targets themselves. As the visual range grows, the agents undergo a sharp crossover from an environment-tuned individual search to a scale-agnostic collective one, and this crossover coincides with the onset of spatial aggregation. Thus a collective phase arises as a by-product of optimal foraging, without any direct reward for grouping. A minimal analytical first-passage model reproduces the transition as a crossover between the two search strategies. Our results identify indirect, resource-driven reward as a generic route to emergent collective phenomena.