动态大型概念模型：自适应语义空间中的潜在推理

摘要

大型语言模型（LLMs）对所有词元采用统一计算模式，然而语言实际呈现出高度非均匀的信息密度特性。这种词元均质化处理机制既在局部可预测片段上浪费算力，又对语义关键转折处的计算资源分配不足。我们提出动态大概念模型（DLCM）——一种分层语言建模框架，该框架通过潜在表征自动学习语义边界，并将计算重心从词元转移至压缩后的概念空间，从而提升推理效率。DLCM采用端到端方式发现可变长度概念，无需依赖预定义语言单元。分层压缩机制从根本上改变了模型的扩展规律：我们首次提出压缩感知的缩放定律，将词元级容量、概念级推理能力与压缩比进行解耦，实现在固定浮点运算量下的理性计算分配。为稳定训练这一异构架构，我们进一步开发解耦型μP参数化方案，支持跨宽度与压缩模式的零样本超参数迁移。在实用场景下（压缩比R=4，即每个概念平均对应四个词元），DLCM将约三分之一的推理算力重新分配给高容量推理主干网络，在保持推理浮点运算量不变的条件下，于12个零样本基准测试中实现平均+2.69%的性能提升。

English

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.

动态大型概念模型：自适应语义空间中的潜在推理

Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space

摘要

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