# Continual General Chunking Problem and SyncMap

arXiv: https://arxiv.org/abs/2006.07853 · HTML: https://mlsanity.com/paper/2006.07853/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Symmetrical SyncMap for Imbalanced General Chunking Problems](https://mlsanity.com/paper/2310.10045/) (2023)
- [Unsupervised Cognition](https://mlsanity.com/paper/2409.18624/) (2024)
- [Dynamical Equations With Bottom-up Self-Organizing Properties Learn Accurate Dynamical Hierarchies Without Any Loss Function](https://mlsanity.com/paper/2302.02140/) (2023)
- [SyncMapV2: Robust and Adaptive Unsupervised Segmentation](https://mlsanity.com/paper/2506.16297/) (2025)
- [RECAP: Local Hebbian Prototype Learning as a Self-Organizing Readout for Reservoir Dynamics](https://mlsanity.com/paper/2603.06639/) (2026)
- [Stable Unsupervised Continual Chunking with Sheaf SyncMap](https://mlsanity.com/paper/2609.25143/) (2026)

## Abstract (arXiv)

Humans possess an inherent ability to chunk sequences into their constituent parts. In fact, this ability is thought to bootstrap language skills and learning of image patterns which might be a key to a more animal-like type of intelligence. Here, we propose a continual generalization of the chunking problem (an unsupervised problem), encompassing fixed and probabilistic chunks, discovery of temporal and causal structures and their continual variations. Additionally, we propose an algorithm called SyncMap that can learn and adapt to changes in the problem by creating a dynamic map which preserves the correlation between variables. Results of SyncMap suggest that the proposed algorithm learn near optimal solutions, despite the presence of many types of structures and their continual variation. When compared to Word2vec, PARSER and MRIL, SyncMap surpasses or ties with the best algorithm on 66% of the scenarios while being the second best in the remaining 34%. SyncMap's model-free simple dynamics and the absence of loss functions reveal that, perhaps surprisingly, much can be done with self-organization alone. Code available at https://github.com/zweifel/SyncMap.