The AI Ascend blog.

Jun 18, 2026

Borrowed Intelligence: How Knowledge Distillation Builds Small Language Models That Punch Above Their Weight

A 2-billion-parameter model that trades blows with one ten times its size is not an accident of architecture. It is the product of a teacher pouring its full probability distribution into a student, token by token.

22 min read

Jun 16, 2026

Do Transformers Need Three Projections? Rethinking Q, K, and V

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 projection for a 50% KV cache cut at roughly 3% perplexity cost.

21 min read

Jun 13, 2026

Constitutional AI and RLAIF: Scaling Oversight Without Scaling Labels

Human preference labels are the most expensive ingredient in a modern aligned model. Constitutional AI replaced most of them with a written document and a model judging itself, and the idea quietly took over the alignment stack.

20 min read

Jun 13, 2026

RL from Verifiable Rewards: Training Models on Answers That Can Be Checked

Replace the reward model with a function that simply checks the answer, and a frontier reasoning model falls out of pure reinforcement learning. The catch is what 'checkable' quietly assumes, and what the model learns to exploit.

23 min read

Jun 10, 2026

Diffusion Language Models: Writing Text by Denoising, Not Predicting the Next Token

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 stopped being a research curiosity.

22 min read

Jun 09, 2026

From 4K to a Million Tokens: How RoPE Scaling, YaRN, and Ring Attention Stretch the Context Window

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.

22 min read

Jun 07, 2026

FlashAttention and the Memory Wall: Why Attention Was Never Compute-Bound

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.

23 min read

Jun 05, 2026

Dynamic Workflows: When the Agent Writes Its Own Orchestration

A static agent pipeline is a diagram you draw before you know the task. A dynamic workflow is a program the agent writes once it sees the task, then runs deterministically. The difference reshapes how fan-out, verification, and cooperation get built.

25 min read

Jun 05, 2026

Half Mamba, Half Attention: Why Hybrid State-Space Models Took Over

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.

21 min read