This July 2025 paper introduces Hierarchical Networks (H-Nets), a novel architecture designed to move beyond traditional tokenization in large language models by implementing dynamic chunking. This mechanism allows the model to automatically learn content- and context-dependent segmentation strategies directly from raw data, eliminating the need for predefined pre-processing steps like byte-pair encoding (BPE). H-Nets utilize a recursive, multi-stage structure that processes data at varying levels of abstraction, from bytes to more complex semantic units. Experiments demonstrate that H-Nets, particularly multi-stage configurations, outperform tokenized Transformers in perplexity, downstream tasks, and robustness to textual perturbations, especially in languages and modalities with weak or absent tokenization cues, such as Chinese, code, and DNA sequences. The authors highlight that this end-to-end learning of data chunking represents a significant step towards more generalized and efficient foundation models.
Source:
https://arxiv.org/html/2507.07955
