Autoregressive models write left to right, one token at a time. Diffusion language models reveal a whole sequence at once and sharpen it over a handful of steps. That single change rewrites the latency math, and in 2025 it stoppe…
Every attention head learns three weight matrices for query, key, and value. A 2026 study trained models up to 1.2B parameters to ask which of them are actually load-bearing, and found that keys and values can share one projectio…
An A100 can execute 312 trillion half-precision operations per second but can only pull about 2 terabytes from memory in that same second. FlashAttention made attention fast not by computing less, but by refusing to touch memory.
A model trained on 4,096 tokens can be coaxed into reading a quarter-million without retraining from scratch. The trick is not bigger attention; it is lying to the model about position, and splitting the sequence across a ring of GPUs.
Swap the Texas features for British Columbia and Claude answers Victoria instead of Austin. That single intervention is the strongest evidence yet that a language model performs genuine multi-step reasoning inside one forward pas…
Pure Mamba was supposed to replace attention. Instead the most efficient open models in 2025 are roughly seven-eighths Mamba and one-eighth attention. The reason is the KV cache, and what each layer can and cannot remember.
Chain-of-thought made models reason out loud, one word at a time. A new line of work lets them reason in the silent space between words, trading auditability for compute that does not have to be spelled out.
A 36.7-million-parameter graph network produces a 10-day global forecast in under a minute and beats the world's best physics-based system on 90 percent of verification targets. It also cannot start without that system's data ass…
Every frontier LLM released since 2023 uses some form of Mixture of Experts. The idea is deceptively simple: build a model with hundreds of billions of parameters but activate only a fraction for each token. The result is dense-m…
Every token an LLM generates forces it to reload the keys and values of every token that came before. Multi-Head Latent Attention rewrites that trade by caching one compressed vector instead of dozens of separate heads, cutting m…
A 512-token model could read a paragraph. A million-token model can read a novel. The journey between those two numbers is a story about quadratic cost, clever approximations, and what it means for a machine to "remember."
Llama 2 and Llama 3 share an attention implementation almost line for line. One constant differs: the RoPE base went from 10,000 to 500,000. That number sets the entire wavelength spectrum of the model's position signal, and it h…