BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
Summary
This paper introduces BDH-CQ, a 150M-parameter reasoning model that combines in-context learning with recurrent latent reasoning, achieving 29.5% pass@2 on ARC-AGI-1 at very low inference cost and establishing a new cost-accuracy frontier.
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Paper page - BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
Source: https://huggingface.co/papers/2608.09888
Abstract
A 150M-parameter reasoning model using recurrent latent reasoning and in-context learning achieves a new cost-accuracy frontier on ARC-AGI-1.
We introduce BDH-CQ, a reasoning model that combinesin-context learningwithrecurrent latent reasoning. Inputs presented at inference time continuously update the model’s recurrent memory; the model then solves a query through iterative computation in ahigh-dimensional latent space, without verbalizing its intermediate reasoning. We evaluate the model on the publicARC-AGI-1evaluation 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 reportedARC-AGI-1cost-accuracy Pareto frontier, establishing a new state of the art in benchmark cost efficiency.
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