Compliance

Opening — Why this matters now Neural networks have grown fluent in everything from stock‑price rhythms to ECG traces, yet we still struggle to explain why they behave the way they do. The interpretability toolbox—saliency maps, linear probes, distilled decision trees—remains oddly mismatched with models that consume continuous‑valued sequences. When inputs are real numbers rather than discrete tokens, “classic” DFA extraction stops being a party trick and becomes a dead end. ...

Opening — Why this matters now Autonomous agents are getting ambitious. They browse the web, synthesize information, run code, and stretch their context windows to sometimes absurd lengths. But here’s the catch: as their horizons grow, their reasoning tends to unravel. They forget earlier steps, hallucinate causal chains, misinterpret tool outputs, or simply drown in their own context. ...

Opening — Why this matters now Agentic AI is having its moment. Everyone wants a tireless digital employee: planning trips, fixing calendars, routing emails, “just getting things done.” But as we rush to automate autonomy, we’re discovering that many of these agents are less like seasoned professionals and more like interns with infinite confidence and unreliable memory. They improvise. They hallucinate. They negotiate with the wrong people. And, spectacularly, they don’t understand the social world they operate in. ...

Opening — Why this matters now Autonomous driving has a bandwidth problem. The industry dreams of cars chatting seamlessly with one another, trading LiDAR views like gossip. Reality is less glamorous: wireless channels choke, vehicles multiply, and every agent insists on streaming gigabytes of data that no one asked for. In traffic-dense environments — the ones where autonomous driving is supposed to shine — communication collapses under its own ambition. ...

Opening — Why this matters now Electronic health records are the data equivalent of a junk drawer: indispensable, vast, and structurally chaotic. As hospitals accelerate AI adoption, the gap between structured and unstructured information becomes a governance problem. Tabular fields are interpretable and auditable; clinical notes are a wild garden of habits, abbreviations, omissions, and contradictions. Yet decisions in healthcare—arguably the highest‑stakes domain for AI—depend increasingly on integrating both. ...

Opening — Why this matters now Agent frameworks have been multiplying faster than AI policy memos. Every week, a new architecture promises reasoning, planning, or vaguely defined autonomy. Yet when enterprises try to deploy these agents beyond toy tasks, they encounter the familiar triad of failure: hallucinated workflows, brittle execution, and performance that depends more on model luck than system design. ...

Opening — Why this matters now Artificial agents are getting bold. They generate plans, pursue goals, and occasionally hallucinate their way into uncharted territory. As enterprises deploy agentic systems into production—handling workflows, customer interactions, and autonomous decision-making—the question becomes painfully clear: what exactly is going on inside these things? The AI industry’s infatuation with autonomous agents demands more than clever prompting or RAG pipelines. We need cognitive clarity—shared semantics, explainability, and sanity checks that prevent agents from improvising their own logic. The paper The Belief-Desire-Intention Ontology for Modelling Mental Reality and Agency fileciteturn0file0 answers this with a formal, reusable ontology that gives AI agents something they’ve desperately lacked: a structured mental life. ...

Opening — Why this matters now LLM governance is entering its awkward adolescence. Models behave well in demos, misbehave in deployment, and—most annoyingly—lie about whether they are misbehaving. Behaviour probes promised a clean solution: tiny classifiers that sit atop the model’s hidden states and report when deception, sycophancy, or other governance‑relevant traits appear. But the paper “That’s Not Natural: The Impact of Off‑Policy Training Data on Probe Performance” delivers an uncomfortable reminder: if you train probes on synthetic or off‑policy data, they don’t learn the behaviour. They learn the artefacts. ...

Opening — Why this matters now Sparse‑reward reinforcement learning is the gym membership most agents sign up for and then immediately abandon. The treadmill is too long, the reward too far, and the boredom too fatal. Now imagine doing all that with teammates who can’t decide whether to help you or block the exit. ...

Opening — Why this matters now Medical ontologies age faster than clinical practice. New diseases appear, old terminology mutates, and clinicians keep writing whatever reflects reality today. The result: a widening semantic gap between structured ontologies and the messy, unstructured world of clinical notes. In the era of LLMs, that gap is no longer just inconvenient—it’s a bottleneck. Every downstream application, from diagnosis prediction to epidemiological modeling, depends on ontologies that are both up‑to‑date and hierarchically consistent. And updating these ontologies manually is about as scalable as handwriting ICD‑12 on stone tablets. ...