theory
Predicting your own latents cuts the sample cost from exponential to flat News
A new proof shows that on hierarchically structured data, learning from tokens needs training examples growing exponentially with the depth of the hidden structure while predicting your own representations needs a number that stays constant.
Why Asking an AI the Same Question 10,000 Times Barely Helps News
A new analysis shows that sampling many answers from an AI and picking the most common one hits a hard ceiling because the samples are correlated, not independent, so thousands of extra tries can be worth only a couple of genuinely new ones.
Three Popular Ways to Train Reasoning AIs Turn Out to Be One Formula News
A new proof shows that three widely used reinforcement-learning recipes for training reasoning models - GRPO, Dr. GRPO, and DAPO - are all just different operations on a single number, the spread of rewards within a group of sampled answers.
What if a word were a rotation? A more mathematical way to build AI News
A fresh, abstract idea: treat what a model attends to not as plain lists of numbers but as geometric moves like rotations — so useful symmetries come 'for free.' Elegant and early. (A deeper, technical read.)