# Classical Sorting Algorithms as a Model of Morphogenesis: self-sorting arrays reveal unexpected competencies in a minimal model of basal intelligence

arXiv: https://arxiv.org/abs/2401.05375 · HTML: https://mlsanity.com/paper/2401.05375/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 173 references that are in the map.
- First posted on arXiv 15 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Technological Approach to Mind Everywhere (TAME): an experimentally-grounded framework for understanding diverse bodies and minds](https://mlsanity.com/paper/2201.10346/) (2021)
- [There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines](https://mlsanity.com/paper/2212.10675/) (2022)
- [Biological Robots: Perspectives on an Emerging Interdisciplinary Field](https://mlsanity.com/paper/2207.00880/) (2022)
- [Learning of signaling networks: molecular mechanisms](https://mlsanity.com/paper/2001.11679/) (2020)
- [Information flow through a model of the C. elegans klinotaxis circuit](https://mlsanity.com/paper/1502.04262/) (2015)
- [Embodying probabilistic inference in biochemical circuits](https://mlsanity.com/paper/1806.10161/) (2018)
- [Robustness for Free: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits](https://mlsanity.com/paper/2311.01245/) (2023)

## Built on by (most cited first, 5 of 5)

- [Artificial Consciousness as Interface Representation](https://mlsanity.com/paper/2508.04383/) (2025)
- [Topological constraints on self-organisation in locally interacting systems](https://mlsanity.com/paper/2501.13188/) (2025)
- [AI-driven control of bioelectric signalling for real-time topological reorganization of cells](https://mlsanity.com/paper/2503.13489/) (2025)
- [Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?](https://mlsanity.com/paper/2509.10875/) (2025)
- [Language Game: Talking to Non-Human Systems](https://mlsanity.com/paper/2605.16321/) (2026)

## Abstract (arXiv)

The emerging field of Diverse Intelligence seeks to identify, formalize, and understand commonalities in behavioral competencies across a wide range of implementations. Especially interesting are simple systems that provide unexpected examples of memory, decision-making, or problem-solving in substrates that at first glance do not appear to be complex enough to implement such capabilities. We seek to develop tools to help understand the minimal requirements for such capabilities, and to learn to recognize and predict basal forms of intelligence in unconventional substrates. Here, we apply novel analyses to the behavior of classical sorting algorithms, short pieces of code which have been studied for many decades. To study these sorting algorithms as a model of biological morphogenesis and its competencies, we break two formerly-ubiquitous assumptions: top-down control (instead, showing how each element within a array of numbers can exert minimal agency and implement sorting policies from the bottom up), and fully reliable hardware (instead, allowing some of the elements to be "damaged" and fail to execute the algorithm). We quantitatively characterize sorting activity as the traversal of a problem space, showing that arrays of autonomous elements sort themselves more reliably and robustly than traditional implementations in the presence of errors. Moreover, we find the ability to temporarily reduce progress in order to navigate around a defect, and unexpected clustering behavior among the elements in chimeric arrays whose elements follow one of two different algorithms. The discovery of emergent problem-solving capacities in simple, familiar algorithms contributes a new perspective to the field of Diverse Intelligence, showing how basal forms of intelligence can emerge in simple systems without being explicitly encoded in their underlying mechanics.