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EntropyMoE introduces an entropy-aware Mixture-of-Experts architecture for tokenizer-free LLMs, using dynamic byte patches as routing units to enable sparse conditional computation. Experiments show it achieves the lowest held-out bits-per-byte among baselines while maintaining downstream accuracy.
Introduces Sparse-Reslim, a plug-in routing module that processes only 25% of spatial tokens through expensive transformer blocks for efficient weather prediction, achieving up to 3.18x speedup and improved forecast accuracy.
A Zhihu contributor's half-year-old prediction that the next Transformer would absorb loops, recurrent state, sparse routing, and latent reasoning is gaining relevance as Loop Engineering advances. The article explores how future Transformer architectures may evolve into hybrid models blending linear-complexity layers for background context with attention for precise reasoning, plus finer-grained sparsity and native System 2 reasoning.