{"slug": "bdh-cq-in-context-learning-with-recurrent-latent-reasoning", "title": "BDH-CQ: In-Context Learning with Recurrent Latent Reasoning", "summary": "Researchers introduced BDH-CQ, a reasoning model combining in-context learning with recurrent latent reasoning, achieving 29.5% pass@2 on the ARC-AGI-1 evaluation set with a 150M-parameter configuration at a computed inference cost of $0.0007 per task, breaking the previously reported cost-accuracy Pareto frontier and establishing a new state of the art in benchmark cost efficiency.", "body_md": "# Computer Science > Neural and Evolutionary Computing\n\n[Submitted on 10 Aug 2026]\n\n# Title:BDH-CQ: In-Context Learning with Recurrent Latent Reasoning\n\n[View PDF](/pdf/2608.09888)\n\n[HTML (experimental)](https://arxiv.org/html/2608.09888v1)\n\nAbstract: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.\n\n### Current browse context:\n\ncs.NE\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/bdh-cq-in-context-learning-with-recurrent-latent-reasoning", "canonical_source": "https://arxiv.org/abs/2608.09888", "published_at": "2026-08-22 08:59:01+00:00", "updated_at": "2026-08-22 09:13:29.220095+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research"], "entities": ["BDH-CQ", "ARC-AGI-1"], "alternates": {"html": "https://wpnews.pro/news/bdh-cq-in-context-learning-with-recurrent-latent-reasoning", "markdown": "https://wpnews.pro/news/bdh-cq-in-context-learning-with-recurrent-latent-reasoning.md", "text": "https://wpnews.pro/news/bdh-cq-in-context-learning-with-recurrent-latent-reasoning.txt", "jsonld": "https://wpnews.pro/news/bdh-cq-in-context-learning-with-recurrent-latent-reasoning.jsonld"}}