Opening — Why this matters now Knowledge graphs were supposed to be the clean room of AI reasoning. Structured. Relational. Logical. And yet, the more we scale them, the more they behave like messy organizations: dense departments talking over each other, sparse teams forgotten in the corner, and semantic memos that don’t quite align with operational reality. ...
Opening — Why This Matters Now Everyone wants “agentic AI.” Few are willing to admit that most agents today are glorified interns with a checklist. Reinforcement learning (RL) systems remain powerful—but painfully narrow. They master what we explicitly reward. Nothing more. The real bottleneck isn’t compute. It isn’t model size. It’s imagination—specifically, how rewards are defined. ...
Opening — Why this matters now Large models can write poetry, generate code, and debate philosophy. Yet show them a bouncing ball in a physics simulator and ask, “Why did that happen?”—and things get awkward. The problem is not intelligence in the abstract. It is interface. Language models operate in a world of tokens. Physics simulators operate in a world of state vectors and time steps. Somewhere between $(x_t, y_t, v_t)$ and “the ball bounced off the wall,” meaning gets lost. ...
Opening — Why This Matters Now Large language models are increasingly being framed as clinical agents — systems that read notes, synthesize findings, and recommend actions. The problem is not that they are always wrong. The problem is that they can be right for the wrong reasons. In high-stakes environments like emergency medicine, reasoning quality matters as much as the final label. A discharge decision supported by incomplete logic is not “almost correct.” It is a liability. ...
Opening — Why this matters now For two years, the industry has treated reasoning as a scaling problem. Bigger models. Longer context. More tokens. Perhaps a tree search if one feels adventurous. But humans don’t solve problems by “thinking harder” in one fixed way. We switch modes. We visualize. We branch. We compute. We refocus. We verify. ...
Opening — The Promise of Autonomous AIOps (and the Reality Check) Autonomous cloud operations sound inevitable. Large Language Models (LLMs) can summarize logs, generate code, and reason across messy telemetry. So why are AI agents still so bad at something as operationally critical as Root Cause Analysis (RCA)? A recent empirical study on the OpenRCA benchmark gives us an uncomfortable answer: the problem is not the model tier. It is the architecture. ...
Opening — Why This Matters Now Large Reasoning Models (LRMs) have discovered a curious habit: they keep thinking long after they already know the answer. In the race toward higher benchmark scores, more tokens became the default solution. Need better math accuracy? Add 3,000 reasoning tokens. Want stronger medical QA performance? Let the model “think harder.” Compute is cheap—until it isn’t. ...
Opening — Why this matters now Everyone wants “agentic AI.” Few are prepared to train it properly. As large language models evolve into tool-using, multi-step decision makers, the bottleneck is no longer raw model scale. It is environment scale. Real-world reinforcement learning (RL) for agents is expensive, fragile, and rarely reproducible. Public benchmarks contain only a handful of environments. Real APIs throttle you. Human-crafted simulations do not scale. ...
Opening — Why this matters now Large language models are no longer judged by whether they work, but by whether we can trust how they work. In regulated domains—finance, law, healthcare—the question is no longer abstract. It is operational. And increasingly uncomfortable. The paper behind this article tackles an issue the industry prefers to wave away with scale and benchmarks: memorization. Not the vague, hand-wavy version often dismissed as harmless, but a specific, measurable phenomenon that quietly undermines claims of generalization, privacy, and robustness. ...
Opening — Why this matters now Explainable AI has always promised clarity. For years, that promise was delivered—at least partially—through feature attributions, saliency maps, and tidy bar charts explaining why a model predicted this instead of that. Then AI stopped predicting and started acting. Tool-using agents now book flights, browse the web, recover from errors, and occasionally fail in slow, complicated, deeply inconvenient ways. When that happens, nobody asks which token mattered most. They ask: where did the agent go wrong—and how did it get there? ...