ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer

Modern language models (LMs) still rely on fixed, pre-defined subword tokenizations. Once a tokenizer is trained, the LM can only operate at this fixed level of granularity, which often leads to brittle and counterintuitive behaviors even in otherwise strong reasoning models. We introduce \textbf{ByteFlow Net}, a new architecture that removes tokenizers entirely and instead enables models to learn their own segmentation of raw byte streams into semantically meaningful units. Our approach is grounded in information theory: ByteFlow Net performs compression-driven segmentation based on coding rate of latent representation, allowing the model to dynamically evaluate the information cost of grouping bytes and decide chunk boundaries during processing. Unlike prior self-tokenizing methods that depend on brittle heuristics with human-designed inductive biases, ByteFlow Net adapts its internal representation granularity to the input itself. Experiments demonstrate that this compression-based chunking strategy yields substantial performance gains, with ByteFlow Net outperforming both BPE-based Transformers and previous byte-level architectures. These results suggest that end-to-end, tokenizer-free modeling is not only feasible but also more effective, opening a path toward more adaptive, robust, and information-grounded language models.