A model is usually treated like a finished machine. You send text in, get text out, and pretend the interesting part happens somewhere behind a curtain. If the answer is weak, the industry has a familiar menu: prompt harder, fine-tune, route to a bigger model, or pay the tax of yet another orchestration layer. Very elegant, in the way a pile of adapters behind a monitor is elegant. ...
CT scans are not photographs. This is a small fact with expensive consequences. A normal image model can pretend that visual understanding is mostly a matter of looking at a flat picture. A CT volume does not offer that courtesy. It is dense, three-dimensional, and full of clinically relevant details that may occupy only a small part of the scan. Feed the whole thing into a multimodal large language model, and the model faces a choice: compress the volume aggressively, sample a few slices, or ask the GPU to become a radiologist with a power bill. ...
The hospital problem is not that EEG is too small. It is that EEG refuses to stay the same shape. A hospital does not run machine learning inside a clean benchmark. It runs it across devices, departments, vendors, technicians, recording protocols, and patients who rarely behave like textbook signals. Electroencephalography, or EEG, makes this especially inconvenient. The signal is long, noisy, clinically useful, and structurally inconsistent. Different datasets may use different electrode counts. Different institutions may follow different montage conventions. A model that looks competent on one electrode layout can become less confident when the scalp is wired slightly differently. Apparently, brains did not agree to standardize themselves for our convenience. ...
Pruning Is a Game, and Most Weights Lose Pruning usually sounds like housekeeping. Train the model. Rank the weights. Remove the small ones. Fine-tune the survivor. Pretend the whole exercise was more scientific than it looked in the notebook. That workflow has worked well enough to become familiar. But familiarity is not explanation. It tells us how to remove model components after training; it says less about why some components become removable in the first place. The paper Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks asks a sharper question: what if pruning is not merely an external compression operation, but the outcome of competition inside the model?1 ...
TL;DR for operators Efficient LLMs are not just “smaller Transformers with a haircut.” That is the comfortable misconception, and like many comfortable things in enterprise AI, it becomes expensive once real users arrive. The survey reviewed here maps the major architectural routes for making large language models faster, cheaper, and more deployable: linear sequence models, sparse attention, efficient full attention, sparse mixture-of-experts, hybrid architectures, diffusion LLMs, and multimodal extensions.1 Its practical value is not that it declares a single winner. It does something more useful: it tells operators which bottleneck each family is trying to remove. ...