As the first post of 2026, I wanted to share some of the research trends that I think might be super influential in this year frontier model breakthroughs. These are development that have already showed quite a bit of promise and seems ready for most ambitious implementations If you've been watching the loss curves over the last decade, the recipe has been surprisingly simple: take a Transformer, throw a massive pile of internet text at it, crank up the GPU cluster, and wait. And the 'bitter lesson' held true'scale was all you needed. We built these incredible 'stochastic parrots' that could complete your code, write poetry, and pass the bar exam, all by just really, really wanting to predict the next token. But if you look at the research papers dropping recently, the vibe has shifted. We are hitting a point where just 'scaling up' the pre-training run is seeing diminishing returns. We don't just want models that can talk smooth; we want models that can think straight. As we look...
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