Attention

Opening — Why this matters now Long-context models have become the quiet arms race of the LLM ecosystem. Every few months, someone announces another context window milestone—128k, 1M, or “effectively unlimited.” The implication is obvious and seductive: if a model can read everything, it must understand everything. The paper behind this article is less impressed. It asks a colder question: what actually happens inside a model as context grows, and whether more tokens translate into more usable intelligence—or just more noise politely attended to. ...

Opening — When more layers stop meaning more intelligence The scaling era taught us a simple mantra: stack more layers, get better models. Then deployment happened. Suddenly, latency, energy bills, and GPU scarcity started asking uncomfortable questions—like whether every layer in a 40-layer Transformer is actually doing any work. This paper answers that question with unsettling clarity: many attention layers aren’t lazy—they’re deliberately silent. And once you notice that, pruning them becomes less of an optimization trick and more of a design correction. ...

Opening — Why this matters now Text-to-image diffusion models are often marketed as masters of compositional imagination: just add more words, and the model will obligingly combine them into a coherent visual scene. In practice, however, this promise quietly collapses the moment multiple concepts compete for attention. A landmark swallows an object. An artist style erases the product. One concept wins, the other simply vanishes. ...

Opening — Why this matters now Multimodal Large Language Models (MLLMs) can reason, explain, and even philosophize about images—until they’re asked to notice something small. A number on a label. A word in a table. The relational context that turns a painted line into a parking space instead of a traffic lane. The industry’s default fix has been straightforward: crop harder, zoom further, add resolution. Yet performance stubbornly plateaus. This paper makes an uncomfortable but important claim: the problem is not missing pixels. It’s missing structure. ...