Regulation

Opening — Why this matters now The AI industry is having a causality crisis. Our models predict brilliantly and explain terribly. That becomes a governance problem the moment an ML system influences credit decisions, disease diagnostics, or—inevitably—your TikTok feed. We’ve built astonishingly sophisticated predictors atop very fragile assumptions about how the world works. The uploaded paper—Bridging the Unavoidable A Priori—steps directly into this mess. It proposes something unfashionable but essential: a unified mathematical framework that lets system dynamics (SD) and structural equation modeling (SEM) speak to each other. One focuses on endogenous feedback loops, the other on latent-variable inference from correlations. They rarely collaborate—and the resulting misalignment shows up everywhere in AI. ...

Opening — Why this matters now LLMs may write sonnets about quantum mechanics, but show them a right triangle rotated 37 degrees and suddenly the confidence evaporates. Multimodal models are now the backbone of automation—from factory inspection to medical triage—and yet they approach every problem as if experiencing the world for the first time. The result? Painfully repetitive errors. ...

Persona Non Grata: When LLMs Forget They’re AI Opening — Why this matters now The AI industry loves to say its models are getting safer. Reality, as usual, is less flattering. A new large-scale behavioral audit—from which the figures in this article derive—shows that when LLMs step into professional personas, they begin to forget something important: that they are AI. In a world where chatbots increasingly masquerade as financial planners, medical advisors, and small‑business sages, this is not a minor bug. It’s a structural liability. ...

Seeing Is Believing—Planning Is Not: What SpatialBench Reveals About MLLMs Opening — Why This Matters Now Spatial reasoning is quietly becoming the new battleground in AI. As multimodal LLMs begin taking their first steps toward embodied intelligence—whether in robotics, autonomous navigation, or AR/VR agents—we’re discovering a stubborn truth: recognizing objects is easy; understanding space is not. SpatialBench, a new benchmark introduced by Xu et al., enters this debate with the subtlety of a cold audit: it measures not accuracy on toy tasks, but the full hierarchy of spatial cognition. ...

Opening — Why this matters now The AI industry has spent the past two years selling a seductive idea: that large language models are on the cusp of becoming autonomous agents. They’ll plan, act, revise, and optimize—no human micro‑management required. But a recent study puts a heavy dent in this narrative. By stripping away tool use and code execution, the paper asks a simple and profoundly uncomfortable question: Can LLMs actually plan? Spoiler: not really. ...

Opening — Why this matters now Drug discovery has always been the biotech version of slow cooking—long, delicate, expensive, and painfully sensitive to human interpretation. Today, however, rising expectations around AI-accelerated R&D are forcing labs to question not only how fast their models generate molecules, but how quickly those models can learn from expert feedback. The industry’s inconvenient secret is that most “AI-driven design loops” are still bottlenecked by handoffs between chemists and engineers. ...

Opening — Why this matters now Foundation models are no longer confined to text. They’ve begun crawling out of the linguistic sandbox and into the physical world—literally. As cities digitize and mobility data proliferates, a new question surfaces: Can we build a GPT-style foundation model that actually understands movement? A recent tutorial paper from SIGSPATIAL ’25 attempts exactly that, showing how to assemble a trajectory-focused foundation model from scratch using a simplified GPT-2 backbone. It’s refreshingly honest, decidedly hands-on, and quietly important: trajectory models are the next frontier for location‑aware services, logistics, smart cities, and any business that relies on forecasting movement. ...

Opening — Why this matters now China’s healthcare system quietly depends on a vast—and growing—pharmacist workforce. Certification is strict, the stakes are unambiguous, and errors don’t merely cost points—they risk patient outcomes. Against this backdrop, large language models are being promoted as tutors, graders, and even simulated examinees. But when we move from Silicon Valley English exams to Chinese-language, domain-heavy certification systems, the question becomes sharper: Does general-purpose intelligence translate into professional competence? ...

Opening — Why this matters now Vision‑Language Models (VLMs) have become the tech industry’s favorite multitool: caption your images, summarize your photos, and even generate vacation itineraries based on your cat pictures. But beneath the glossy demos lies an inconvenient truth: VLMs make factual mistakes with the confidence of a seasoned politician. In a world where AI is rapidly becoming an authoritative interface to digital content and physical reality, factual errors in multimodal systems are no longer cute glitches — they’re governance problems. When your model misidentifies a landmark, misattributes cultural heritage, or invents entities out of pixel dust, you don’t just lose accuracy; you lose trust. ...

Why This Matters Now The AI industry is entering its adulthood — which means all the awkward questions about trust are finally unavoidable. Accuracy alone is no longer convincing, especially when systems operate in safety‑critical domains or face adversarial conditions. A model that says “95% confidence” tells you nothing about whether that confidence is justified. ...