Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
Authors: name: Xingwei Qu, name: Shaowen Wang, name: Zihao Huang, name: Kai Hua, name: Fan Yin, name: Rui-Jie Zhu, name: Jundong Zhou, name: Qiyang Min, name: Zihao Wang, name: Yizhi Li, name: Tianyu Zhang, name: He Xing, name: Zheng Zhang, name: Yuxuan Song, name: Tianyu Zheng, name: Zhiyuan Zeng, name: Chenghua Lin, name: Ge Zhang, name: Wenhao Huang
Organization: ByteDance Seed

Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical transitions. We propose Dynamic Large Concept Models (DLCM), a hierarchical language modeling framework that learns semantic boundaries from latent representations and shifts computation from tokens to a compressed concept space where reasoning is more efficient. DLCM discovers variable-length concepts end-to-end without relying on predefined linguistic units. Hierarchical compression fundamentally changes scaling behavior. We introduce the first compression-aware scaling law, which disentangles token-level capacity, concept-level reasoning capacity, and compression ratio, enabling principled compute allocation under fixed FLOPs. To stably train this heterogeneous architecture, we further develop a decoupled μP parametrization that supports zero-shot hyperparameter transfer across widths and compression regimes. At a practical setting (R=4, corresponding to an average of four tokens per concept), DLCM reallocates roughly one-third of inference compute into a higher-capacity reasoning backbone, achieving a +2.69\% average improvement across 12 zero-shot benchmarks under matched inference FLOPs.