Opening — Why this matters now Long-context multimodal models are starting to look fluent enough to pass surface-level exams on scientific papers. They answer questions correctly. They summarize convincingly. And yet, something feels off. The answers often arrive without a visible path—no trail of figures, no textual anchors, no defensible reasoning chain. In other words, the model knows what to say, but not necessarily why it is true. ...
Opening — Why this matters now Explainability for large language models has reached an uncomfortable stage of maturity. We have methods. We have surveys. We even have regulatory pressure. What we do not have—at least until now—is a reliable way to tell whether an explanation actually reflects how a model behaves, rather than how comforting it sounds. ...
Opening — Why this matters now LLM agents are getting disturbingly good at finishing tasks. They click the right buttons, traverse web pages, solve text-based games, and close tickets. Benchmarks applaud. Dashboards glow green. Yet something feels off. Change the environment slightly, rotate the layout, tweak the constraints — and suddenly the same agent behaves like it woke up in a stranger’s apartment. The problem isn’t execution. It’s comprehension. ...
Opening — Why this matters now Large Language Models have become fluent planners. Ask them to outline a strategy, decompose a task, or explain why something should work, and they rarely hesitate. Yet when placed inside an environment where actions cost resources, mistakes compound, and time does not politely pause, that fluency often collapses. ...
Opening — Why this matters now Reinforcement learning has a credibility problem. Models ace their benchmarks, plots look reassuringly smooth, and yet the moment the environment changes in a subtle but meaningful way, performance falls off a cliff. This is usually dismissed as “out-of-distribution behavior” — a polite euphemism for we don’t actually know what our agent learned. ...
Opening — Why this matters now LLM-as-judge has quietly become infrastructure. It ranks models, filters outputs, trains reward models, and increasingly decides what ships. The industry treats these judges as interchangeable instruments—different thermometers measuring the same temperature. This paper suggests that assumption is not just wrong, but dangerously so. Across thousands of evaluations, LLM judges show near-zero agreement with each other, yet striking consistency with themselves. They are not noisy sensors of a shared truth. They are stable, opinionated evaluators—each enforcing its own private theory of quality. ...
Opening — Why this matters now Agentic AI has entered its Minecraft phase again. Not because blocks are trendy, but because open-world games remain one of the few places where planning, memory, execution, and failure collide in real time. Yet most agent benchmarks still cheat. They rely on synthetic prompts, privileged world access, or oracle-style evaluation that quietly assumes the agent already knows where everything is. The result: impressive demos, fragile agents, and metrics that flatter models more than they inform builders. ...
Opening — Why this matters now Robotics has reached an awkward adolescence. Vision–Language–Action (VLA) models can now describe the world eloquently, name objects with near-human fluency, and even explain why a task should be done a certain way—right before dropping the object, missing the grasp, or confidently picking up the wrong thing. This is not a data problem. It’s a diagnostic one. ...
Opening — Why this matters now Every time a new benchmark is released, the same ritual follows: models race to the top, leaderboards reshuffle, and a few months later—sometimes weeks—we quietly realize the benchmark has been memorized, gamed, or both. The uncomfortable truth is that static questions are no longer a reliable way to measure rapidly evolving language models. ...
Opening — Why this matters now AI agents are having a good year. They write code, refactor repositories, debug production bugs, and occasionally embarrass junior developers. Naturally, biology is next. Spatial transcriptomics—arguably one of the messiest, most insight-rich data domains in modern life science—looks like a perfect proving ground. If agents can reason over spatial biology data, the promise is compelling: fewer bottlenecks, faster discovery, and less dependence on scarce bioinformatics talent. ...