Recurrent latent reasoning (thinking without verbalizing) combined with in-context learning enables smaller models to solve complex reasoning tasks more cost-efficiently than larger models that explain their work.
BDH-CQ is a reasoning model that learns from examples at inference time and solves problems through hidden, iterative computation rather than step-by-step explanations. On the ARC-AGI benchmark, a small 150M-parameter version achieves 29.5% accuracy while costing just $0.0007 per task—better cost-efficiency than previous approaches.