We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuously update the model's recurrent memory; the model then solves a query through iterative computation in a high-dimensional latent space, without verbalizing its intermediate reasoning. We evaluate the model on the public ARC-AGI-1 evaluation set and use controlled ARC-like interventions to study what it learns from demonstrations, how consistently it applies an inferred transformation, and which concepts remain difficult. A 150M-parameter configuration reaches 29.5% pass@2 at a computed inference cost of \$0.0007 per task. This operating point breaks through the previously reported ARC-AGI-1 cost-accuracy Pareto frontier, establishing a new state of the art in benchmark cost efficiency.
Mostly right but don’t forget that technological advancement never leads to cheaper more affordable components, and more equality for the people
Just the opposite the big techs continues growing faster utilizing even more ressources
Mostly right but don’t forget that technological advancement never leads to cheaper more affordable components, and more equality for the people Just the opposite the big techs continues growing faster utilizing even more ressources
I disagree. Look at how many poor countries have been transformed just by virtue of cheap smartphones.
Give them a device to communicate with one another and suddenly their life becomes a heck of a lot easier.