Opening — Why this matters now As LLM agents graduate from clever chatbots to decision‑making systems, their failures are becoming less amusing and more expensive. We are no longer talking about wrong trivia answers; we are talking about broken schedules, invalid plans, unsafe workflows, and agents confidently violating constraints they were never told—explicitly—not to break. ...
Opening — Why this matters now Ports are supposed to be automated. In practice, many of their most critical decisions still depend on a small priesthood of optimization specialists, tribal operational knowledge, and painfully slow deployment cycles. Vehicle Dispatching Systems (VDSs) — the logic that tells fleets of AGVs where to go and when — are a prime example. They promise up to 30% efficiency gains, yet stubbornly resist scaling from one terminal to another. ...
Opening — Why this matters now For the past two years, AI progress has been narrated as a story of scale: more parameters, more data, more compute. Yet the ARC-AGI leaderboard keeps delivering an inconvenient counterexample. Small, scratch-trained models—no web-scale pretraining, no trillion-token diet—are routinely humiliating far larger systems on abstract reasoning tasks. This paper asks the uncomfortable question: where is the reasoning actually coming from? ...
Opening — Why this matters now Healthcare AI has quietly run into a contradiction. We want models that are richer—multi-modal, context-aware, clinically nuanced—yet we increasingly deploy them in environments that are poorer: fewer samples, missing modalities, limited compute, and growing scrutiny over energy use. Transformers, the industry’s favorite hammer, are powerful but notoriously wasteful. In medicine, that waste is no longer academic; it is operational. ...
Opening — Why this matters now LLMs no longer live alone. They rank against each other on leaderboards, bid for tasks inside agent frameworks, negotiate in shared environments, and increasingly compete—sometimes quietly, sometimes explicitly. Once models are placed side-by-side, performance stops being purely absolute. Relative standing suddenly matters. This paper asks an uncomfortable question: do LLMs care about losing—even when losing costs them nothing tangible? ...
Opening — Why this matters now Medical AI has become very good at answering questions. Unfortunately, medicine rarely works that way. Pathology, oncology, and clinical decision-making are not single-query problems. They are investigative processes: observe, hypothesize, cross-check, revise, and only then conclude. Yet most medical AI benchmarks still reward models for producing one-shot answers — neat, confident, and often misleading. This mismatch is no longer academic. As multimodal models edge closer to clinical workflows, the cost of shallow reasoning becomes operational, regulatory, and ethical. ...
Opening — Why this matters now Legal AI has a habit of oversimplifying judgment. In the race to automate legal reasoning, we have learned how to encode rules, then factors, and eventually hierarchies of factors. But something stubborn keeps leaking through the abstractions: strength. Not whether a reason exists — but how strongly it exists. ...
Opening — Why this matters now Medical AI has a credibility problem. Not because large language models (LLMs) can’t answer medical questions—they increasingly can—but because they often arrive at correct answers for the wrong reasons. In medicine, that distinction is not academic. A shortcut that accidentally lands on the right diagnosis today can quietly institutionalize dangerous habits tomorrow. ...
Opening — Why this matters now Reinforcement learning has always had an uncomfortable secret: most of the intelligence is smuggled in through the reward function. We talk about agents learning from experience, but in practice, someone—usually a tired engineer—decides what “good behavior” numerically means. As tasks grow longer-horizon, more compositional, and more brittle to specification errors, this arrangement stops scaling. ...
Opening — Why this matters now Regulated industries love spreadsheets and hate surprises. Finance, healthcare, and insurance all depend on tabular data—and all have strict constraints on where that data is allowed to go. Shipping sensitive tables to an API-hosted LLM is often a non‑starter. Yet small, on‑prem language models have a reputation problem: they speak fluently but stumble over arithmetic. ...