Regulation

Opening — Why this matters now Electric grids are becoming less predictable, more distributed, and less forgiving. Renewables fluctuate, demand spikes move faster, and operators must make decisions across sprawling networks under hard physical constraints. Meanwhile, everyone would like AI to optimize infrastructure—preferably yesterday. There is one awkward detail: power grids are not ad-click systems. When recommendation engines fail, users get odd suggestions. When grid control fails, cities get darkness. ...

Opening — Why this matters now Every industry has a bottleneck disguised as tradition. In academia, it is peer review: noble in theory, overloaded in practice, and increasingly powered by caffeine and resentment. The paper AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot reports something more consequential than a conference experiment. It documents a live deployment where 22,977 submissions each received an official AI-generated review in under 24 hours. No sandbox. No toy benchmark. Real papers, real authors, real consequences. ...

Opening — Why this matters now Everyone wants AI that can reason. Preferably about things that matter: machinery, logistics, engineering diagrams, medical imaging, factory operations. Unfortunately, many systems marketed as “reasoning models” are still glorified pattern matchers with a flair for confident prose. This paper, Reward Design for Physical Reasoning in Vision-Language Models, asks a sharper question: if we reward an AI differently, what kind of reasoning behavior do we get? The answer is refreshingly inconvenient. There is no universal reward signal that makes models smarter. There are only trade-offs, incentives, and consequences. Rather like management. ...

Opening — Why this matters now The AI industry has quietly entered a dangerous phase: we are measuring everything, and understanding very little. If you ask five vendors whether their model is “safe,” you will likely get five confident “yes” answers—each backed by benchmarks, metrics, and charts. The problem is not the lack of evaluation. It is that the evaluations no longer agree on what they are measuring. ...

Opening — Why this matters now The current generation of LLM-powered systems can write code, suggest optimizations, and even debug their own outputs. Impressive, yes—but fundamentally limited. Most of these systems are still operating at the function level, not the system level. That distinction matters more than people admit. In real-world optimization—logistics, routing, scheduling, portfolio construction—the performance edge rarely comes from a clever function. It comes from how the entire algorithm is structured, decomposed, and coordinated. And until recently, that remained stubbornly human territory. ...

Opening — Why this matters now Everyone wants AI that can “just figure it out.” Describe a supply chain problem, a scheduling constraint, or a pricing objective—and expect the system to generate a mathematically sound optimization model. That’s the dream. And increasingly, it’s the pitch behind AI copilots in enterprise decision-making. The paper fileciteturn0file0 quietly dismantles that assumption. ...

Opening — Why this matters now For all the noise around “AI-powered education,” most platforms still behave like glorified video players with quizzes stapled on. Personalization, in practice, often means rearranging the same content for everyone—slightly faster for some, slightly slower for others. That model is reaching its limits. As AI systems become more capable in real-time decision-making, the expectation is shifting: learning systems should not just deliver content, but respond to learners as they evolve. Static personalization is no longer sufficient; adaptive intelligence is the new baseline. ...

Opening — Why this matters now Electric vehicles are no longer a pilot project—they are infrastructure. And infrastructure, unlike PowerPoint, has a habit of exposing weak assumptions. The problem is not just where vehicles go, but whether they make it there without quietly dying mid-route. Routing for EV fleets introduces a constraint traditional logistics never had to respect: energy is no longer an afterthought—it is the system. ...

Opening — Why this matters now AI agents have quietly crossed a threshold: they no longer forget everything between conversations. And yet, they still behave like they do. Despite persistent memory layers—vector databases, RAG pipelines, archival stores—most agents fail at something deceptively simple: answering questions that require time, change, or context. Ask an agent what happened first, what changed, or how multiple events relate, and the system often collapses into guesswork. ...

Opening — Why this matters now There’s a quiet bottleneck in agentic AI that most demos conveniently ignore: reward design. Search agents—those increasingly fashionable LLM-powered systems that browse, retrieve, and reason—are trained like obedient students. They are rewarded when they produce the correct answer. The catch? Someone needs to define that answer in advance. ...