An LLM agent with a million-token window still forgets what you told it last week. Memory is not a bigger context; it is a separate system that decides what to write down, how to summarize it, and what to pull back. Here is how t…
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.
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…
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 c…
A 250-question benchmark carries a standard error of about three percentage points. Most of the model comparisons published on top of such benchmarks cannot distinguish the models they are comparing. Evaluations are experiments, …
Most agent frameworks treat state as an afterthought, tacking memory onto a chain of LLM calls. LangGraph inverts the design: the graph is the state machine, every node transition is checkpointed, and cycles are first-class citiz…
Most teams reach for fine-tuning when they should reach for retrieval. The cost is not just dollars - it is months of confused engineers trying to figure out why the model started lying again.
A 7B model that thinks for thirty seconds can outscore a 70B model that answers instantly. Test-time compute is the lever that makes this possible, and it is quietly rewriting the economics of model design.