BDH-CQ: Recurrent Latent Reasoning Model Breaks ARC-AGI-1 Cost-Efficiency Frontier
Pathway
BDH-CQ combines in-context learning with recurrent latent reasoning: it updates a recurrent memory from inputs given at inference time and solves queries through iterative computation in latent space, without producing a verbalized chain-of-thought. A 150M-parameter configuration reaches 29.5% pass@2 on the public ARC-AGI-1 evaluation set at $0.0007 per task, beating the previously reported cost-accuracy Pareto frontier for that benchmark.
Why it matters
Received 681 upvotes on HuggingFace Daily Papers; shows a non-chain-of-thought, small-model path to competitive ARC-AGI performance at very low inference cost, relevant to the ongoing debate on whether verbalized reasoning is necessary for strong performance.
Importance: 4/5
Notable paper with 681 HuggingFace Daily Papers upvotes (>=100 bump threshold) breaking a cost-accuracy Pareto frontier.