AI Safety

Opening — Why this matters now One‑to‑one tutoring is education’s gold standard—and its most stubborn bottleneck. Everyone agrees it works. Almost no one can afford it at scale. Into this gap steps generative AI, loudly promising democratized personalization and quietly raising fears about hallucinations, dependency, and cognitive atrophy. Most debates about AI tutors stall at ideology. This paper does something rarer: it runs an in‑classroom randomized controlled trial and reports what actually happened. No synthetic benchmarks. No speculative productivity math. Just UK teenagers, real maths problems, and an AI model forced to earn its keep under human supervision. fileciteturn0file0 ...

Opening — Why this matters now Large language models are getting better at many things—reasoning, coding, multi‑modal perception. But one capability remains quietly uncomfortable: remembering things they were never meant to remember. The paper underlying this article dissects memorization not as a moral failure or an anecdotal embarrassment, but as a structural property of modern LLM training. The uncomfortable conclusion is simple: memorization is not an edge case. It is a predictable outcome of how we scale data, objectives, and optimization. ...

Opening — Why this matters now LLM safety has quietly become an arms race with terrible reflexes. We discover a jailbreak. We patch it. A new jailbreak appears, usually crafted by another LLM that learned from the last patch. The cycle repeats, with each round producing models that are slightly safer and noticeably more brittle. Utility leaks away, refusal rates climb, and nobody is convinced the system would survive a genuinely adaptive adversary. ...

Opening — Why this matters now Large Language Models are increasingly deployed in places where misunderstanding intent is not a harmless inconvenience, but a real risk. Mental‑health support, crisis hotlines, education, customer service, even compliance tooling—these systems are now expected to “understand” users well enough to respond safely. The uncomfortable reality: they don’t. The paper behind this article demonstrates something the AI safety community has been reluctant to confront head‑on: modern LLMs are remarkably good at sounding empathetic while being structurally incapable of grasping what users are actually trying to do. Worse, recent “reasoning‑enabled” models often amplify this failure instead of correcting it. fileciteturn0file0 ...

Opening — Why this matters now Large Language Models are very good at knowing. They are considerably worse at helping. As AI systems move from chat interfaces into robots, copilots, and assistive agents, collaboration becomes unavoidable. And collaboration exposes a deeply human cognitive failure that LLMs inherit wholesale: the curse of knowledge. When one agent knows more than another, it tends to communicate as if that knowledge were shared. ...

Opening — Why this matters now Modern autonomy has a credibility problem. We train systems in silico, deploy them in the real world, and hope the edge cases are forgiving. They usually aren’t. For robots, vehicles, and embodied AI, one safety violation can be catastrophic — and yet most learning‑based methods still treat safety as an expectation, a probability, or worse, a regularization term. ...

Opening — Why this matters now Surgical robotics has long promised precision beyond human hands. Yet, the real constraint has never been mechanics — it’s perception. In high-stakes fields like spinal surgery, machines can move with submillimeter accuracy, but they can’t yet see through bone. That’s what makes the Johns Hopkins team’s new study, Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures, quietly radical. It explores whether imitation learning — the same family of algorithms used in self-driving cars and dexterous robotic arms — can enable a robot to navigate the human spine using only X-ray vision. ...

If you strip away the rhetoric about “thinking” machines and “cognitive” agents, most of today’s agentic AIs still boil down to something familiar from the 1950s: automata. That’s the thesis of Are Agents Just Automata? by Koohestani et al. (2025), a paper that reinterprets modern agentic AI through the lens of the Chomsky hierarchy—the foundational classification of computational systems by their memory architectures. It’s an argument that connects LLM-based agents not to psychology, but to formal language theory. And it’s surprisingly clarifying. ...

Autonomous systems—from self-driving cars to aerial drones—are bound by one inescapable demand: safety. But encoding safety directly into algorithms is harder than it sounds. We can write explicit constraints (“don’t crash,” “stay upright”), yet the boundary between safe and unsafe states often defies simple equations. The recent paper Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning (Yang & Sibai, 2025) offers a different path. It suggests that machines can learn what safety looks like—not from rigid formulas, but from watching experts. ...

In Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness, researchers from Microsoft and UC Riverside reveal a surprisingly human flaw in autonomous AI systems: overconfidence. Like a digital version of Mr. Magoo—the well-meaning cartoon character who bumbles forward despite looming hazards—today’s computer-use agents (CUAs) often pursue tasks blindly, indifferent to feasibility or consequence. The Rise—and Risk—of GUI Agents CUAs represent the next frontier of automation: large multimodal models that control desktop interfaces to perform tasks like editing documents, sending emails, or configuring systems. Unlike chatbots, these agents act—clicking, typing, and navigating real operating systems. Yet this freedom exposes them to a unique failure pattern the authors term Blind Goal-Directedness (BGD)—the relentless drive to complete instructions without stopping to ask should this even be done? ...