Mixture of Experts, MoR and Self-Attention: Architectures of Efficiency. A comparative analysis. MoE скачать в хорошем качестве

Mixture of Experts, MoR and Self-Attention: Architectures of Efficiency. A comparative analysis. MoE

In the quest for more powerful AI, we’ve often followed a simple rule: deeper is better. We add more layers, more parameters, and more compute. But what if the secret to the next leap in intelligence isn't about how much we add, but how smartly we reuse what we already have? For years, Transformers have processed every token—whether it's a simple 'the' or a complex mathematical variable—with the exact same amount of computational effort. But not all tokens are created equal. Today, we’re breaking down Mixture-of-Recursions, or MoR, a framework that finally brings 'thinking on demand' to the Transformer. But it’s not just about the math; it’s about the hardware. We’re going deep on how this architecture solves the memory bottleneck with innovative KV caching strategies that maximize throughput and slash inference costs. If you’ve been following the rise of Mixture-of-Experts (MoE), this is the next logical step in the evolution of latent space reasoning.