Large Language Models

Opening — Why this matters now For years, debates about large language models (LLMs) have circled the same tired question: Do they really understand what they’re saying? The answer—still no—has been treated as a conversation stopper. But recent “reasoning models” have made that question increasingly irrelevant. A new generation of AI systems can now reason through problems step by step, critique their own intermediate outputs, and iteratively refine solutions. They do this without grounding, common sense, or symbolic understanding—yet they still solve tasks previously reserved for humans. That contradiction is not a bug in our theory of AI. It is a flaw in our theory of reasoning. ...

Opening — Why this matters now For years, the received wisdom in AI planning was blunt: language models can’t really plan. Early benchmarks—especially Blocksworld—made that verdict look almost charitable. Models hallucinated invalid actions, violated preconditions, and confidently declared failure states as success. The field responded by bolting on external verifiers, symbolic planners, or human-in-the-loop corrections. ...

Opening — Why this matters now Every time a new benchmark is released, the same ritual follows: models race to the top, leaderboards reshuffle, and a few months later—sometimes weeks—we quietly realize the benchmark has been memorized, gamed, or both. The uncomfortable truth is that static questions are no longer a reliable way to measure rapidly evolving language models. ...

Opening — Why this matters now Graph learning has quietly run into a ceiling. Not because graph neural networks (GNNs) are weak, but because they are confidently opinionated. Once you choose a GNN, you lock in assumptions about where signal should live: in node features, in neighborhoods, in homophily, in motifs. That works—until it doesn’t. ...

Opening — Why this matters now Large language models are getting better at everything—reasoning, coding, writing, even pretending to think. Yet beneath the polished surface lies an old, uncomfortable question: are these models learning, or are they remembering? The distinction used to be academic. It no longer is. As models scale, so does the risk that they silently memorize fragments of their training data—code snippets, proprietary text, personal information—then reproduce them when prompted. Recent research forces us to confront this problem directly, not with hand-waving assurances, but with careful isolation of where memorization lives inside a model. ...

Opening — Why this matters now Healthcare AI has entered its foundation model phase. LLMs trained on trillions of tokens are being casually proposed for everything from triage to prognosis, often with an implicit assumption: bigger models must understand patients better. This paper quietly punctures that assumption. By benchmarking LLMs against smaller, task‑focused language models (SLMs) on shock prediction in ICUs, the authors confront a question most vendors avoid: Do LLMs actually predict future clinical deterioration better—or do they merely sound more convincing? ...

Opening — Why this matters now For years, we have comforted ourselves with a tidy distinction: models generalize, databases memorize. Recent research quietly dismantles that boundary. As LLMs scale, memorization is no longer an edge case—it becomes a structural property. That matters if you care about data leakage, IP exposure, or regulatory surprises arriving late but billing retroactively. ...

Opening — Why this matters now Large Language Models have already aced exams, written code, and argued philosophy with unsettling confidence. The obvious next step was inevitable: can they do science? Not assist, not summarize—but reason, explore, and discover. The paper behind this article asks that question without romance. It evaluates LLMs not as chatbots, but as proto‑scientists, and then measures how far the illusion actually holds. ...

Opening — Why this matters now Large Language Models are increasingly invited into optimization workflows. They write solvers, generate heuristics, and occasionally bluff their way through mathematical reasoning. But a more uncomfortable question has remained largely unanswered: do LLMs actually understand optimization problems—or are they just eloquent impostors? This paper tackles that question head‑on. Instead of judging LLMs by what they say, it examines what they encode. And the results are quietly provocative. ...

Opening — Why this matters now AI keeps passing medical exams, acing board-style questions, and politely explaining pathophysiology on demand. Naturally, someone always asks the dangerous follow-up: So… can we let it talk to patients now? This paper answers that question with clinical bluntness: not without supervision, and certainly not without consequences. When large language models (LLMs) are exposed to raw, unstructured patient narratives—the kind doctors hear every day—their performance degrades in a very specific, pathological way. The authors call it AI-MASLD: AI–Metabolic Dysfunction–Associated Steatotic Liver Disease. ...