Assurance

Opening — Why this matters now Modern machine learning has a data problem that money can’t easily solve: abundance without discernment. Models are no longer starved for samples; they’re overwhelmed by datasets—entire repositories, institutional archives, and web-scale collections—most of which are irrelevant, redundant, or quietly harmful. Yet the industry still behaves as if data arrives as loose grains of sand. In practice, data arrives in boxes: datasets bundled by source, license, domain, and institutional origin. Selecting the right boxes is now the binding constraint. ...

Opening — Why this matters now Health misinformation is not a fringe problem anymore. It is algorithmically amplified, emotionally charged, and often wrapped in scientific‑looking language that fools both humans and machines. Most AI fact‑checking systems respond by doing more — more retrieval, more reasoning, more prompts. This paper argues the opposite: do less first, think harder only when needed. ...

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 Large scientific user facilities run on scarcity. Beam time, telescope hours, clean-room slots—there are never enough to go around. Every cycle, hundreds of proposals compete for a fixed, immovable resource. The uncomfortable truth is that proposal selection is not about identifying absolute excellence; it is about ranking relative merit under pressure, time constraints, and human fatigue. ...

Opening — Why this matters now In a world drowning in data yet starved for shared meaning, scientific fields increasingly live or die by their metadata. The promise of reproducible AI, interdisciplinary collaboration, and automated discovery hinges not on bigger models but on whether we can actually agree on what our terms mean. The paper under review offers a timely slice of humility: vocabulary—yes, vocabulary—is the next frontier of AI-assisted infrastructure. ...

Opening — Why this matters now Modern AI accelerators are magnificent in the same way a glass skyscraper is magnificent: shimmering, efficient, and one stray fracture away from a catastrophic afternoon. As LLMs balloon into the tens or hundreds of billions of parameters, their hardware substrates—A100s, TPUs, custom ASICs—face reliability challenges that traditional testing workflows simply cannot keep up with. Random fault injection? Too slow. Formal methods? Too idealistic. Evolutionary search? Too myopic. ...

Opening — Why This Matters Now Probabilistic graphical models (PGMs) have long powered everything from supply‑chain optimisations to fraud detection. But as modern AI systems become more modular—and more opaque—the industry is rediscovering an inconvenient truth: our tools for representing uncertainty remain tangled in their own semantics. The paper at hand proposes a decisive shift. Instead of treating graphs and probability distributions as inseparable twins, it reframes them through categorical semantics, splitting syntax from semantics with surgical precision. ...

Opening — Why This Matters Now As warehouses, fulfillment centers, and robotics-heavy factories race toward full automation, a familiar problem quietly dictates their upper bound of efficiency: how to make thousands of robots move without tripping over each other. Multi-Agent Path Finding (MAPF) has long promised elegant solutions. But elegant, in robotics, is too often synonymous with naïve. Most planners optimize for a clean mathematical abstraction of the world—one where robots don’t have acceleration limits, never drift off schedule, and certainly never pause because they miscommunicated with a controller. ...

Opening — Why This Matters Now In a year obsessed with ever-larger models and ever-deeper agent stacks, it’s refreshing—almost suspiciously so—to see a paper argue for less. Less prompting, less inference-time orchestration, less dependence on monolithic LLMs as ever-present copilots. Instead: one conversation, one dump of knowledge, then autonomy. This is the premise behind SCOPE—a hierarchical planning approach that asks an LLM for help exactly once. And then never again. ...

Opening — Why this matters now In an era where AI systems negotiate, persuade, and increasingly act on our behalf, we still lack a principled account of what it even means for a belief to survive communication. We hand-wave “misalignment” as if it were a software bug, when the deeper problem is representational geometry: yours, mine, and the model’s. When values are vectors, persuasion isn’t magic—it’s linear algebra with an identity crisis. ...