# From Manifestations to Cognitive Architectures: a Scalable Framework

arXiv: https://arxiv.org/abs/2406.09823 · HTML: https://mlsanity.com/paper/2406.09823/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 14 Jun 2024.

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

- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [How do neurons operate on sparse distributed representations? A mathematical theory of sparsity, neurons and active dendrites](https://mlsanity.com/paper/1601.00720/) (2016)

## Built on by (most cited first, 4 of 4)

- [Unsupervised Cognition](https://mlsanity.com/paper/2409.18624/) (2024)
- [Beating Transformers using Synthetic Cognition](https://mlsanity.com/paper/2504.07619/) (2025)
- [The Function-Representation Model of Computation](https://mlsanity.com/paper/2410.07928/) (2024)
- [Optimisation Is Not What You Need](https://mlsanity.com/paper/2507.03045/) (2025)

## Abstract (arXiv)

The Artificial Intelligence field is flooded with optimisation methods. In this paper, we change the focus to developing modelling methods with the aim of getting us closer to Artificial General Intelligence. To do so, we propose a novel way to interpret reality as an information source, that is later translated into a computational framework able to capture and represent such information. This framework is able to build elements of classical cognitive architectures, like Long Term Memory and Working Memory, starting from a simple primitive that only processes Spatial Distributed Representations. Moreover, it achieves such level of verticality in a seamless scalable hierarchical way.