Opening — Why this matters now As large language models quietly migrate from text generators to decision makers, the industry has developed an unhealthy obsession with the wrong question: Did the model choose the same option as a human? Accuracy, F1, and distributional overlap have become the default proxies for alignment. They are also deeply misleading. ...
Opening — Why this matters now Public-sector AI has a credibility problem. Not because it cannot optimize—but because it optimizes too cleanly. In health system planning, decisions are rarely about pure efficiency. They are negotiated compromises shaped by terrain, politics, institutional memory, and hard-earned intuition. Classic optimization methods politely ignore all that. This paper tackles a question many planners quietly ask but rarely formalize: Can we let algorithms optimize without silencing human judgment—and still keep mathematical guarantees intact? ...
Opening — Why this matters now Agentic large language models are increasingly marketed as generalist planners: systems that can reason, act, and adapt across domains without bespoke algorithmic scaffolding. The pitch is seductive—why maintain a zoo of solvers when a single agent can plan everything from code refactors to satellite schedules? AstroReason-Bench arrives as a cold shower. ...
Opening — Why this matters now The AI industry has developed an unhealthy obsession with thinking longer. More tokens, deeper chains, bigger context windows—surely that must mean better reasoning. Except, increasingly, it doesn’t. Large Reasoning Models (LRMs) often reason past the point of usefulness, slipping into self-validation loops or overwriting correct answers with unnecessary exploration. This paper proposes a heretical idea in the age of scaling: maybe the model doesn’t need to think more—it needs to know when to stop. ...
Opening — Why this matters now Predictive Process Monitoring (PPM) has always promised operational foresight: knowing how long a case will take, whether a costly activity will happen, or when things are about to go wrong. The catch has been brutally consistent — you need a lot of data. Thousands of traces. Clean logs. Stable processes. ...
Opening — Why this matters now Multi-agent LLM systems are having a moment. Software engineering agents argue with each other, math solvers debate proofs, and code reviewers nitpick outputs like caffeinated interns. The results are often impressive—and painfully expensive. Token budgets explode, latency compounds, and the coordination logic starts to look like an over-managed meeting that should have been an email. ...
Opening — Why this matters now Functional dependency (FD) discovery has quietly become a victim of its own success. Modern algorithms can enumerate everything—and that is precisely the problem. On realistic schemas, exhaustive FD discovery produces hundreds of thousands of valid dependencies, most of which are technically correct and practically useless. Computationally expensive. Cognitively overwhelming. Operationally irrelevant. ...
Opening — Why this matters now Large language models have learned how to talk. That part is mostly solved. The harder problem—quietly surfacing beneath the hype—is whether they can stay in character. The explosion of role‑playing agents (RPLAs) is not driven by novelty alone. It reflects a structural shift in how humans want to interact with AI: not as tools, but as persistent entities with memory, motivation, and recognizable behavior. When an AI tutor forgets who it is, or a game NPC contradicts its own values mid‑conversation, immersion collapses instantly. The paper reviewed here treats that collapse as a technical failure, not a UX quirk—and that framing is overdue. fileciteturn0file0 ...
Opening — Why this matters now Multimodal models have become unnervingly confident readers of documents. Hand them a PDF, a scanned exam paper, or a photographed worksheet, and they will happily extract text, diagrams, and even implied structure. The problem is not what they can read. It is what they refuse to unread. In real classrooms, mathematics exam papers are not pristine artifacts. They are scribbled on, folded, stained, partially photographed, and occasionally vandalized by enthusiastic graders. Yet most document benchmarks still assume a polite world where inputs are complete and legible. This gap matters. An AI system that confidently invents missing math questions is not merely wrong—it is operationally dangerous. ...
Opening — Why this matters now Tool-using agents are no longer a novelty. They are quietly becoming the default interface between LLMs and the real world: APIs, databases, search engines, execution environments. Yet most reinforcement learning pipelines still behave as if every step in a trajectory deserves the same bonus. That assumption was tolerable when tasks were short. It collapses when agents think, call tools, fail, retry, and recover over ten or more turns. ...