Researchers introduced BDH-CQ, a reasoning system that learns from task examples by updating internal memory, then solves new problems through iterative computation in a latent (hidden numerical) workspace without converting intermediate steps into language. A 150M-parameter configuration achieved 29.5% pass@2 on the ARC-AGI-1 benchmark at $0.00070 per task.
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