Cognaptus Insights

Opening — Why this matters now The AI alignment debate has a familiar rhythm: align the values first, deploy later. Sensible, reassuring—and increasingly detached from reality. In practice, we are already delegating consequential decisions to systems we do not fully understand, let alone perfectly align. Trading algorithms rebalance portfolios, recommendation engines steer attention, and autonomous agents negotiate, schedule, and filter on our behalf. The real question is no longer “Is the AI aligned?” but “Is it aligned enough to justify delegation, given what it can do better than us?” ...

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 can solve math problems. The more interesting question in 2025 is whether they can learn how to reason, at scale, across contexts that are long, messy, and computationally expensive. Most math datasets answer the first question. Nemotron-Math answers the second — and does so with a surprisingly pragmatic eye on cost. ...

Opening — Why this matters now For years, AI interpretability has promised transparency while quietly delivering annotations, probes, and post-hoc stories that feel explanatory but often fail the only test that matters: can they predict what the model will actually do next? As large language models become agents—capable of long-horizon planning, policy evasion, and strategic compliance—interpretability that merely describes activations after the fact is no longer enough. What we need instead is interpretability that anticipates behavior. That is the ambition behind Predictive Concept Decoders (PCDs). ...

Opening — Why this matters now Large Language Models have learned how to think out loud. What they still struggle with is knowing when that thinking is wrong — while it is happening. In high‑stakes domains like mathematics, finance, or policy automation, delayed error detection is not a feature; it is a liability. Most modern reasoning pipelines still follow an awkward split: first generate reasoning, then verify it — often with a separate model. Humans do not work this way. We reason and judge simultaneously. This paper asks a simple but uncomfortable question: what if LLMs were trained to do the same? ...

Opening — Why this matters now Large language models don’t think—but they do accumulate influence. And that accumulation is exactly where most explainability methods quietly give up. As LLMs move from single-shot text generators into multi-step reasoners, agents, and decision-making systems, we increasingly care why an answer emerged—not just what token attended to what prompt word. Yet most attribution tools still behave as if each generation step lives in isolation. That assumption is no longer just naïve; it is actively misleading. ...

Opening — Why this matters now Modern deep learning training is an odd contradiction. We obsess over architectures, data curation, and trillion-token scaling laws—then quietly accept Cosine Annealing as if it were gravity. Learning rate schedules are often inherited, not argued for. This paper challenges that complacency with a scheduler that does something almost offensive in its simplicity: it just watches the loss and reacts. ...

Opening — Why this matters now As LLM agents graduate from clever chatbots to decision‑making systems, their failures are becoming less amusing and more expensive. We are no longer talking about wrong trivia answers; we are talking about broken schedules, invalid plans, unsafe workflows, and agents confidently violating constraints they were never told—explicitly—not to break. ...

Opening — Why this matters now Retrieval-Augmented Generation has a dirty secret: it keeps retrieving more context while quietly getting no smarter. As context windows balloon to 100K tokens and beyond, RAG systems dutifully shovel in passages—Top‑5, Top‑10, Top‑100—hoping recall will eventually rescue accuracy. It doesn’t. Accuracy plateaus. Costs rise. Attention diffuses. The model gets lost in its own evidence pile. ...

Opening — Why this matters now Ports are supposed to be automated. In practice, many of their most critical decisions still depend on a small priesthood of optimization specialists, tribal operational knowledge, and painfully slow deployment cycles. Vehicle Dispatching Systems (VDSs) — the logic that tells fleets of AGVs where to go and when — are a prime example. They promise up to 30% efficiency gains, yet stubbornly resist scaling from one terminal to another. ...