{"id":6142,"date":"2026-10-19T16:49:22","date_gmt":"2026-10-19T16:49:22","guid":{"rendered":"https:\/\/i10xnews.kinsta.cloud\/?p=6142"},"modified":"2026-10-19T17:20:40","modified_gmt":"2026-10-19T17:20:40","slug":"bdh-cq-arc-agi-cheap-latent-reasoning","status":"publish","type":"post","link":"https:\/\/i10x.ai\/news\/bdh-cq-arc-agi-cheap-latent-reasoning","title":{"rendered":"A 150M Model Hits 29.5% on ARC-AGI-1 for Under a Tenth of a Cent"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">\u26a1 Quick Take<\/h2>\n\n<ul class=\"wp-block-list\"><li><strong>The finding:<\/strong> A 150M-parameter BDH-CQ configuration scores 29.5% pass@2 on public ARC-AGI-1 at a computed $0.00070 per task.<\/li><li><strong>Pareto break:<\/strong> That operating point breaks the previously reported ARC-AGI-1 cost\u2013accuracy frontier.<\/li><li><strong>Independently checked:<\/strong> A black-box audit by Bielik and NYU co-authors reproduced the 29.5% pass@2 score.<\/li><\/ul>\n\n\n<p class=\"wp-block-paragraph\">ARC-AGI rewards systems that learn a transformation from few demos. Pathway researchers introduce BDH-CQ: demos update a recurrent memory, then the model solves the query through iterative latent computation without verbalizing chain-of-thought.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1344\" height=\"768\" src=\"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/10\/bdh-cq-arc-agi-cheap-latent-reasoning-cost.webp\" alt=\"Stat card: 150M parameters, 29.5 percent pass at two, 0.0007 dollars per task, about 57 times cheaper than GPT-5.6 Luna Low on July ARC Prize costs.\" class=\"wp-image-6141\" srcset=\"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/bdh-cq-arc-agi-cheap-latent-reasoning-cost.webp 1344w, https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/bdh-cq-arc-agi-cheap-latent-reasoning-cost-300x171.webp 300w, https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/bdh-cq-arc-agi-cheap-latent-reasoning-cost-1024x585.webp 1024w, https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/bdh-cq-arc-agi-cheap-latent-reasoning-cost-768x439.webp 768w\" sizes=\"auto, (max-width: 1344px) 100vw, 1344px\" \/><figcaption class=\"wp-element-caption\">i10X chart based on data from Engdahl et al. (2026), arXiv:2608.09888, main results and cost comparison (arXiv non-exclusive license).<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd2c What the study shows<\/h2>\n\n\n<p class=\"wp-block-paragraph\">Default point: ~0.85 H200 GPU-seconds per task; at $3\/H200-hour that is $0.00070. Versus ARC Prize July 2026 costs, about 57\u00d7 cheaper than GPT-5.6 Luna (Low) at 34.2% \/ $0.040 \u2014 or ~11\u00d7 after OpenAI&#8217;s later 80% API price cut.<\/p>\n\n\n<ul class=\"wp-block-list\"><li><strong>Controlled ARC-like interventions:<\/strong> Probe what is learned from demos, consistency of the inferred transform, and hard concepts.<\/li><li><strong>Small-N family profiles:<\/strong> With ten tasks per family, Wilson intervals are wide \u2014 treat concept scores as descriptive.<\/li><\/ul>\n\n\n<h2 class=\"wp-block-heading\">\u2705 Why it matters: accuracy per dollar<\/h2>\n\n\n<p class=\"wp-block-paragraph\">Frontier LLMs can score higher on ARC but at cents per task. BDH-CQ targets the cheap high-volume regime.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83e\uddea Method in brief<\/h2>\n\n\n<p class=\"wp-block-paragraph\">Recurrent latent reasoning without verbal CoT; cost from H200-seconds; independent black-box audit.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u2753 What remains open<\/h2>\n\n<ul class=\"wp-block-list\"><li><strong>Computed vs billed cost:<\/strong> Uses assumed H200 hourly rate.<\/li><li><strong>Public eval set:<\/strong> Private ARC leaderboard may differ.<\/li><\/ul>\n\n\n<h2 class=\"wp-block-heading\">\ud83e\udded Context<\/h2>\n\n\n<p class=\"wp-block-paragraph\">Related efficient reasoning in <a href=\"\/news\/category\/studies\/reasoning\">Reasoning<\/a> and <a href=\"\/news\/category\/studies\/efficiency-training\">Efficiency &amp; Training<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udee0\ufe0f What it means for ARC-style systems<\/h2>\n\n<ul class=\"wp-block-list\"><li><strong>Consider latent recurrence:<\/strong> Not only long CoT.<\/li><li><strong>Publish $\/task:<\/strong> Pareto charts need both axes.<\/li><li><strong>Invite audits:<\/strong> Reproduction builds trust.<\/li><\/ul>\n\n\n<h2 class=\"wp-block-heading\">\u270d\ufe0f About the analysis<\/h2>\n\n\n<p class=\"wp-block-paragraph\">This article is based on the full text of the preprint &#8220;BDH-CQ: In-Context Learning with Recurrent Latent Reasoning&#8221; (arXiv:2608.09888v1, submitted August 10, 2026). All numbers are taken from the paper&#8217;s text, tables and figures; i10X did not rerun the experiments. The paper is distributed under arXiv&#8217;s non-exclusive license, so i10X does not reproduce its figures; the chart was drawn by i10X from numbers reported in main results and cost comparison.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd2d i10x Perspective<\/h2>\n\n\n<p class=\"wp-block-paragraph\">29.5% will not win the ARC prize \u2014 but at under a tenth of a cent it redraws the efficiency frontier. That is a different kind of SOTA, and a useful one.<\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>BDH-CQ combines in-context learning with recurrent latent reasoning. Independent audit confirms 29.5% pass@2 at $0.0007\/task \u2014 ~57\u00d7 cheaper than GPT-5.6 Luna (Low) on ARC Prize costs.<\/p>\n","protected":false},"author":2,"featured_media":6292,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"paper_title":"BDH-CQ: In-Context Learning with Recurrent Latent Reasoning","paper_url":"https:\/\/arxiv.org\/abs\/2608.09888","pdf_url":"https:\/\/arxiv.org\/pdf\/2608.09888v1","doi":"","arxiv_id":"2608.09888","authors":"Bj\u00f6rn Engdahl, Adrian Kosowski, Jan Chorowski, Zuzanna Stamirowska, Przemys\u0142aw Uzna\u0144ski, Junlin Jiang, Rohan Phadke, Remigiusz Kinas, Richard Zhong","institutions":"Pathway; Bielik AI; New York University","paper_date":"2026-08-10","venue":"arXiv (cs.NE), version 1","peer_reviewed":false,"code_url":"","data_url":"","funding_coi":"Pathway research team.","figure_license":"Chart drawn by i10X from main results and cost comparison (arXiv non-exclusive license)","paper_license":"arXiv non-exclusive license","replication":"no","study_models":"BDH-CQ 150M; compared to GPT-5.6 Luna (Low) on ARC Prize costs","study_sample":"Public ARC-AGI-1 evaluation set","footnotes":"","_yoast_wpseo_primary_category":""},"categories":[56,52],"tags":[],"companies":[38],"products":[],"class_list":["post-6142","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-reasoning","category-studies","company-open-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>BDH-CQ: Breaking the ARC-AGI Cost\u2013Accuracy Frontier - i10X News<\/title>\n<meta name=\"description\" content=\"Pathway&#039;s BDH-CQ: 150M params, 29.5% ARC-AGI-1 pass@2 at $0.0007\/task. 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