| 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 |
| date | 2026-08-10 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.09888 |
| affiliation | Pathway |
| upvotes | 745 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{engdahl2026bdh,
title={BDH-CQ: In-Context Learning with Recurrent Latent Reasoning},
author={Björn Engdahl and Adrian Kosowski and Jan Chorowski and Zuzanna Stamirowska and Przemysław Uznański and Junlin Jiang and Rohan Phadke and Remigiusz Kinas and Richard Zhong},
year={2026},
eprint={2608.09888},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.09888}
}