Compliance

Opening — Why this matters now Everyone is chasing longer context windows. Million-token prompts. Endless chat logs. The assumption is simple: if the model can see everything, it will remember correctly. This paper shows why that assumption fails. In long-horizon, goal-driven interactions, errors rarely come from missing information. They come from retrieving the wrong information—facts that are semantically similar but contextually incompatible. Bigger windows amplify the problem. Noise scales faster than relevance. ...

Opening — Why this matters now Architectural studios are quietly changing. Not with robotic arms or parametric scripts, but with prompts. Text-to-image models now sit beside sketchbooks, offering instant massing ideas, stylistic variations, and visual shortcuts that once took hours. The promise is obvious: faster ideation, lower friction, fewer blank pages. The risk is less visible. When creativity is partially outsourced, what happens to confidence, authorship, and cognitive effort? ...

Opening — Why this matters now Genomics QA is no longer a toy problem for language models. It sits at the uncomfortable intersection of messy biological databases, evolving schemas, and questions that cannot be answered from static training data. GeneGPT proved that LLMs could survive here—barely. This paper shows why surviving is not the same as scaling. ...

Opening — Why this matters now Multi-agent AI is no longer a lab curiosity. Tool-using LLM agents already negotiate, cooperate, persuade, and sometimes sabotage—often without humans in the loop. What looks like “emergent intelligence” at first glance is, more precisely, a set of interaction effects layered on top of massive pre-trained priors. And that distinction matters. Traditional multi-agent reinforcement learning (MARL) gives us a language for agents that learn from scratch. LLM-based agents do not. They arrive already socialized. ...

Opening — Why this matters now Most AI control systems are still designed around a brittle assumption: either the agent satisfies everything, or the problem is declared unsolvable. That logic collapses quickly in the real world. Robots run out of battery. Services compete for shared resources. Environments act adversarially, not politely. In practice, goals collide. ...

Opening — Why this matters now Most LLM safety failures don’t look dramatic. They look fluent. A model doesn’t suddenly turn malicious. It drifts there — token by token — guided by coherence, momentum, and the quiet incentive to finish the sentence it already started. Jailbreak attacks exploit this inertia. They don’t delete safety alignment; they outrun it. ...

Opening — Why this matters now Conversational AI is no longer a novelty interface. It is infrastructure: answering customer tickets, tutoring students, advising patients, and quietly reshaping how humans externalize cognition. Yet, the dominant alignment loop—reinforcement learning from human feedback (RLHF)—still depends on something profoundly inefficient: asking people after the fact what they thought. ...

Opening — Why this matters now Brain–computer interfaces (BCIs) have quietly crossed a threshold. They are no longer laboratory curiosities; they are clinical tools, assistive technologies, and increasingly, commercial products. That transition comes with an uncomfortable triad of constraints: generalization, security, and privacy. Historically, you could optimize for two and quietly sacrifice the third. The paper behind SAFE challenges that trade-off—and does so without the usual academic hand-waving. ...

Opening — Why this matters now Drug discovery has a scale problem. Not a small one. A billion-compound problem. Chemical space has outpaced every classical screening method we have—experimental or computational. Docking strains at a few million compounds. Diffusion models demand structural data that simply doesn’t exist for most targets. Meanwhile, enumerated libraries like Enamine REAL quietly crossed 70+ billion molecules, and nobody bothered to ask whether our AI tooling is actually ready for that reality. ...

Opening — Why this matters now In theory, multi‑view multi‑label learning is a gift: more modalities, richer semantics, better predictions. In practice, it is a recurring disappointment. Sensors fail, annotations are partial, budgets run out, and the elegant assumption of “complete views with full labels” quietly collapses. What remains is the real industrial problem: fragmented features and half‑known truths. ...