DLCM paper: modeling concepts instead of tokens, with a compression-aware scaling law
burny_tech · x · 2026-09-03
A new arXiv paper (2512.24617) proposes 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. Key contributions: a compression-aware scaling law disentangling token capacity, concept-level reasoning capacity, and compression ratio for principled compute allocation; a decoupled muP parametrization enabling zero-shot hyperparameter transfer across widths and compression regimes; and a related Next-Latent Prediction method claimed to unlock up to 3.3x faster inference via self-speculative decoding. 19 authors including Xingwei Qu, Ge Zhang, and Wenhao Huang.
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