theory
AlphaEvolve helped tighten the matrix multiplication exponent, and the proof was checked in exact arithmetic News
A new paper establishes a certified upper bound of 2.371177 on the matrix multiplication exponent, improving the previous best of 2.371339, by reformulating the core optimization problem and refining the resulting algorithm with DeepMind's AlphaEvolve.
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.)