Benchmarks are the places where AI systems go to look impressive. That is not automatically a problem. A good benchmark clarifies what a system can do, what it cannot do, and where progress is real. A bad benchmark performs a more theatrical function: it lets researchers win a carefully chosen game, write a confident conclusion, and quietly hope nobody asks whether the result survives contact with another task. ...
Training budgets are where elegant architecture slogans go to be audited. The usual response to a model that needs better accuracy is painfully familiar: make it larger, train it longer, feed it more data, and then pretend the GPU bill is a philosophical problem. The paper Motivation Is Something You Need takes a more interesting route. It asks whether a model needs to be large all the time, or whether extra capacity can be activated only when training signals suggest the model is “getting somewhere.”1 ...
Simulation has an awkward little secret: the hard part is often not writing code. It is choosing the right numerical method before the code exists. Anyone can ask an LLM to produce a solver for an advection equation, a heat equation, or a Navier–Stokes toy problem. The result may even run. That is not the same as being numerically sane. A PDE solver can be syntactically valid, computationally impressive, and mathematically ridiculous at the same time. In scientific computing, this is not a charming personality flaw. It is how bad answers acquire nice plots. ...