Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model 事件

Adaptive Targeted Dynamic Chunking for Tokenization-Free Hierarchical Model arXiv:2605.30080v1 Announce Type: new Abstract: Tokenization-free hierarchical models are emerging as a promising alternative to traditional Large Language Models (LLMs), addressing inherent preprocessing issues such as vocabulary design complexity, out-of-vocabulary (OOV) errors, and language-specific constraints. However, a significant challenge in these byte-level methods is the optimization of the compression ratio,