Opening — Why this matters now Large language models are getting bigger, slower, and—paradoxically—more forgetful in all the wrong places. Despite trillion‑token training runs, practitioners still complain about brittle reasoning, hallucinated facts, and sudden regressions after fine‑tuning. The paper behind this article argues that the problem is not insufficient memory, but poorly allocated memory. ...
Opening — Why this matters now Large language models are getting better at everything—writing, coding, reasoning, and politely apologizing when they hallucinate. Yet beneath these broad performance gains lies a quieter, more structural issue: memorization does not happen evenly. Some parts of the training data exert disproportionate influence, acting as gravitational wells that trap model capacity. These are what the paper terms memorization sinks. ...
Opening — Why this matters now Large Language Models (LLMs) are often praised for what they generalize. Yet, beneath the surface, a less glamorous behavior quietly persists: they remember—sometimes too well. In an era where models are trained on ever-larger corpora under increasing regulatory scrutiny, understanding when memorization occurs, why it happens, and how it can be isolated is no longer an academic indulgence. It is an operational concern. ...
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 Clinical AI has entered an uncomfortable phase of maturity. Models are no longer failing loudly; they are failing quietly. They produce fluent answers, pass public benchmarks, and even outperform physicians on narrowly defined tasks — until you look closely at what those benchmarks are actually measuring. The paper at hand dissects one such case: MedCalc-Bench, the de‑facto evaluation standard for automated medical risk-score computation. The uncomfortable conclusion is simple: when benchmarks are treated as static truth, they slowly drift away from clinical reality — and when those same labels are reused as reinforcement-learning rewards, that drift actively teaches models the wrong thing. ...