Tagged “attention”

5 posts.

All Model Architecture13 Training & Alignment8 Inference & Serving11 Agents & Orchestration10 Reasoning & Evaluation7 Safety, Security & Governance3 Platforms & Practice11

Model Architecture 23 min

Context Rot: Why Bigger Context Windows Don't Mean Better Retrieval

A million-token window promises perfect recall of everything you feed it. Controlled tests on 18 frontier models show recall degrading steadily, unevenly, and well before the window fills, a pattern researchers now call context rot.

llm long-context rag context-engineering ∑ ◫

Model Architecture 20 min

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 projectio…

transformers attention kv-cache inference ∑ ◫

Inference & Serving 21 min

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.

flashattention gpu attention transformers ∑ ◫

Model Architecture 4 min

The Length of a Thought: Why Context Windows Became the New Battleground

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."

transformers attention context scaling

Model Architecture 18 min

Trainable Sparse Attention: When the Model Learns What to Skip

For years, sparse attention was a bolt-on applied after training to shrink the KV cache. NSA, MoBA, and DeepSeek's DSA flip that order, training the sparsity end to end and reporting up to 11x faster decoding without losing accuracy.

attention long-context sparse-attention transformers ∑ ◫