BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

TL;DR - BDH-CQ is a reasoning model that folds in-context demonstrations into a recurrent memory and then solves queries via iterative computation in latent space, reaching 29.5% pass@2 on ARC-AGI-1 at $0.0007 per task. It matters because it claims a new state of the art on the ARC-AGI-1 cost-accuracy Pareto frontier with only 150M parameters.

• Combines in-context learning with recurrent latent reasoning: inference-time inputs continuously update recurrent memory, and the query is solved by iterating in a high-dimensional latent space rather than emitting verbalized chain-of-thought.
• Evaluated on the public ARC-AGI-1 evaluation set; a 150M-parameter configuration reports 29.5% pass@2 at a computed inference cost of $0.0007 per task.
• The authors position this operating point as breaking the previously reported ARC-AGI-1 cost-accuracy Pareto frontier — the headline claim is benchmark cost efficiency, not raw accuracy.
• Controlled ARC-like interventions are used as analysis probes: what the model extracts from demonstrations, how consistently it applies an inferred transformation, and which concepts remain hard.