Opening — Why this matters now World Models are having a quiet renaissance. Once framed as a curiosity for imagination-driven agents, they are now central to planning, robotics, and representation learning. Yet for all the architectural creativity, progress in the field has been oddly brittle. Results are impressive on paper, fragile in practice, and frustratingly hard to reproduce. ...
Opening — Why this matters now Large language models have learned to talk their way through reasoning. But the real world does not speak in tokens. It moves, collides, folds, and occludes. As multimodal models mature, a quiet question has become unavoidable: is language really the best internal medium for thinking about physical reality? ...
Opening — Why this matters now Clinical AI has quietly hit a ceiling. Over the past five years, large language models trained on electronic health records (EHRs) have delivered impressive gains: better coding, stronger risk prediction, and even near‑physician exam performance. But beneath those wins lies an uncomfortable truth. Most clinical foundation models still treat patients as documents—static records to be summarized—rather than systems evolving over time. ...
Opening — Why this matters now Enterprise AI has entered an awkward phase. On paper, frontier LLMs can reason, plan, call tools, and even complete multi-step tasks. In practice, they quietly break things. Not loudly. Not catastrophically. Just enough to violate a policy, invalidate a downstream record, or trigger a workflow no one notices until audit season. ...
Opening — Why this matters now Robotics has quietly entered an awkward phase. Models can see remarkably well and talk impressively about tasks—but when it comes to executing long-horizon, high-precision actions in the physical world, performance still collapses in the details. Grasp slips. Motions jitter. Multimodal uncertainty wins. At the same time, video generation models have undergone a renaissance. Large diffusion-based video models now encode temporal causality, implicit physics, and motion continuity at a scale robotics has never had access to. The obvious question follows: ...
Opening — Why this matters now GUI agents are everywhere in demos and nowhere in production. They click, scroll, and type impressively—right up until the task requires foresight. The moment an interface branches, refreshes, or hides its intent behind two more screens, today’s agents revert to trial-and-error behavior. The core problem isn’t vision. It’s imagination. ...
Opening — Why this matters now Language agents are no longer satisfied with short conversations and disposable prompts. They want places—environments where actions have consequences, memory persists, and the world does not politely forget everything after the next API call. Unfortunately, today’s tooling offers an awkward choice: either rigid web applications backed by databases, or fully generative world models that hallucinate their own physics and promptly lose the plot. ...
The dominant paradigm in LLM agents today is autoregressive reasoning: think step by step, commit token by token. This approach works decently for small tasks — write a tweet, answer a math question — but it quickly falters when the goal requires deep planning, multiple decision branches, or adapting to partially observable environments. Imagine trying to plan a vacation or operate a flight search website while thinking only one move ahead. ...