[2608.09888] BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

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arXiv:2608.09888 (cs)

[Submitted on 10 Aug 2026]

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

Authors:Björn Engdahl, Adrian Kosowski, Jan Chorowski, Zuzanna Stamirowska, Przemysław Uznański, Junlin Jiang, Rohan Phadke, Remigiusz Kinas, Richard Zhong<br>View a PDF of the paper titled BDH-CQ: In-Context Learning with Recurrent Latent Reasoning, by Bj\"orn Engdahl and 8 other authors

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Abstract:We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuously update the model's recurrent memory; the model then solves a query through iterative computation in a high-dimensional latent space, without verbalizing its intermediate reasoning. We evaluate the model on the public ARC-AGI-1 evaluation 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 reported ARC-AGI-1 cost-accuracy Pareto frontier, establishing a new state of the art in benchmark cost efficiency.

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Subjects:

Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)

Cite as:<br>arXiv:2608.09888 [cs.NE]

(or<br>arXiv:2608.09888v1 [cs.NE] for this version)

https://doi.org/10.48550/arXiv.2608.09888

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arXiv-issued DOI via DataCite

Submission history<br>From: Jan Chorowski [view email]<br>[v1]<br>Mon, 10 Aug 2026 17:39:16 UTC (887 KB)

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