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, …
A language model's stated confidence is a number, not a probability. Conformal prediction offers the opposite trade: it promises nothing about any single answer and something exact about the long run, from any scorer, with one as…
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…
In 1951 Claude Shannon estimated the entropy of English by having people guess the next letter. In 2023 a 70-billion-parameter language model compressed a gigabyte of Wikipedia to 8.3% of its size, beating every compressor ever p…
A prompt optimiser that never touches a weight has been reported to beat GRPO by around six points using up to 35 times fewer rollouts. That result only makes sense once you stop treating the prompt as writing and start treating …
An LLM-written pseudo-document lifts BM25 by 15 nDCG@10 points on TREC DL 2019, and turns a 16 ms search into one that waits over two seconds for the model. Query understanding decides what the engine searches for before ranking …
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.