AI Governance

Opening — Why this matters now Continual learning is supposed to be the adult version of fine-tuning: learn new things, keep the old ones, don’t embarrass yourself. Yet large language models still forget with the enthusiasm of a goldfish. Recent work complicated this picture by arguing that much of what we call forgetting isn’t real memory loss at all. It’s misalignment. This paper pushes that idea further — and sharper. It shows that most modern task alignment is shallow, fragile, and only a few tokens deep. And once you see it, a lot of puzzling behaviors suddenly stop being mysterious. ...

Opening — Why this matters now The AI industry loves scale. More data, bigger models, broader benchmarks. But sign language quietly exposes the blind spot in that philosophy: not all motion is generic. When communication depends on millimeter-level finger articulation and subtle hand–body contact, “good enough” pose estimation becomes linguistically wrong. This paper introduces DexAvatar, a system that does something unfashionable but necessary—it treats sign language as its own biomechanical and linguistic domain, not a noisy subset of everyday motion. ...

Opening — Why this matters now Graph learning has quietly run into a ceiling. Not because graph neural networks (GNNs) are weak, but because they are confidently opinionated. Once you choose a GNN, you lock in assumptions about where signal should live: in node features, in neighborhoods, in homophily, in motifs. That works—until it doesn’t. ...

Opening — Why this matters now LLM safety has become a strangely expensive habit. Every new model release arrives with grand promises of alignment, followed by a familiar reality: massive moderation datasets, human labeling bottlenecks, and classifiers that still miss the subtle stuff. As models scale, the cost curve of “just label more data” looks less like a solution and more like a slow-burning liability. ...

Opening — Why this matters now Large language models are getting better at everything—reasoning, coding, writing, even pretending to think. Yet beneath the polished surface lies an old, uncomfortable question: are these models learning, or are they remembering? The distinction used to be academic. It no longer is. As models scale, so does the risk that they silently memorize fragments of their training data—code snippets, proprietary text, personal information—then reproduce them when prompted. Recent research forces us to confront this problem directly, not with hand-waving assurances, but with careful isolation of where memorization lives inside a model. ...

Opening — Why this matters now Multi-agent systems are finally leaving the toy world. Autonomous traders negotiate with other bots. Supply-chain agents coordinate across firms. AI copilots increasingly share environments with other AI copilots. And yet, most multi-agent reinforcement learning (MARL) systems are still stuck with a primitive handicap: agents cannot meaningfully understand what other agents are doing. ...

Opening — Why this matters now Everyone wants cheaper A/B tests. Preferably ones that run overnight, don’t require legal approval, and don’t involve persuading an ops team that this experiment definitely won’t break production. LLM-based persona simulation looks like the answer. Replace humans with synthetic evaluators, aggregate their responses, and voilà—instant feedback loops. Faster iteration, lower cost, infinite scale. What could possibly go wrong? ...

Opening — Why this matters now Embodied AI has quietly crossed a dangerous threshold. Vision‑language models no longer just talk about actions — they execute them. In kitchens, labs, warehouses, and increasingly public spaces, agents now translate natural language into physical force. The problem is not that they misunderstand instructions. The problem is that they understand them too literally, too confidently, and without an internal sense of consequence. ...

Opening — Why this matters now Healthcare AI has entered its most dangerous phase: the era where models look good enough to trust. Clinician‑level benchmark scores are routinely advertised, pilots are quietly expanding, and decision‑support tools are inching closer to unsupervised use. Yet beneath the reassuring metrics lies an uncomfortable truth — high accuracy does not equal safe reasoning. ...

Opening — Why this matters now We keep telling ourselves a comforting story: if an AI explanation isn’t good enough, just refine it. Add another round. Add another chart. Add another paragraph. Surely clarity is a monotonic function of effort. This paper politely demolishes that belief. As agentic AI systems—LLMs that reason, generate code, analyze results, and then revise themselves—move from demos into decision‑support tools, explanation quality becomes a first‑order risk. Not model accuracy. Not latency. Explanation quality. Especially when the audience is human, busy, and allergic to verbose nonsense. ...