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BDH CQ, a 150M parameter research model from Pathway that iterates on puzzles internally rather than writing out a chain of thought, solves about 3 in 10 of François Chollet's ARC AGI 1 abstract reasoning puzzles within two tries, at a
BDH-CQ, a 150-million-parameter research model from Pathway, scored 29.5% pass@2 on the public ARC-AGI-1 evaluation set for a paper-computed $0.0007 per puzzle, according to a preprint posted to arXiv on 10 August 2026. That is less than a tenth of a cent per task. The authors frame the result as a new point on the cost-accuracy curve for the benchmark.
The mechanism is what makes the cost-accuracy point worth attention. BDH-CQ does not verbalize. The system writes no chain of thought at all; at inference time, the input continuously updates a recurrent memory, and the model iterates on the puzzle inside a high-dimensional latent space, the authors explain. Verbal chain-of-thought is the dominant alternative in current open models. Skipping it is where the cost savings appear to come from.
ARC-AGI-1 is the abstract-reasoning benchmark François Chollet built to be hard for large language models. Its puzzles are grid-transformation problems that resist pattern-matching at scale. Pass@2 means the model gets credit if it solves the puzzle within two attempts, so 29.5% is roughly three in ten. That is meaningful, not solved, not human-level. It is one data point on one benchmark.
The architecture is also strictly in-context. No parameters are updated at inference time, and neither the task identifiers nor the demonstration pairs from the evaluation set participate in training. Adaptation happens entirely in the recurrent memory, which keeps BDH-CQ a fixed 150M parameters across runs. That constraint matters, because it separates the model's "learning" of a new puzzle from anything resembling fine-tuning.
The evaluation goes beyond a single benchmark sweep. Beyond the public ARC-AGI-1 score, the authors run controlled ARC-like interventions to probe what the model actually learns from the demonstration pairs, how consistently it applies an inferred transformation, and which concept types remain difficult. That structure is closer to a behavior probe than a leaderboard submission, and it is the part of the paper that should survive independent reproduction even if the headline number moves.
BDH-CQ sits in a lineage with Pathway's prior BDH, or Baby Dragon Hatchling, work, with a latent-reasoning twist on top. The community thread on r/MachineLearning reads the new paper as that continuation, not as a brand-new paradigm. Authors include Björn Engdahl, Adrian Kosowski, Jan Chorowski, Zuzanna Stamirowska, Przemysław Uznański, Junlin Jiang, Rohan Phadke, Remigiusz Kinas, and Richard Zhong, with affiliations at Pathway, Bielik AI, and New York University.
The cost-accuracy claim comes with conditions. The $0.0007 figure is paper-computed, not measured production cost. The authors are estimating inference cost on their own accounting, which makes any cost comparison apples-to-apples only on that accounting; it is not a bill from a cloud provider. This is also one configuration on one benchmark. The paper does not run head-to-head against the strongest published baselines at the same parameter count or compute budget. The "previously reported cost-accuracy Pareto frontier" the authors claim to break is read against the baselines they cite, not an independent sweep. ARC-AGI-2, which is harder, is not in scope.
The preprint is not peer-reviewed. It surfaced through Reddit r/MachineLearning rather than a conference, and third-party AI-generated summaries like emergentmind.com are useful for discovery but carry no editorial weight.
What survives a basic check: a 150M-parameter model can hit 29.5% pass@2 on ARC-AGI-1 without verbal chain-of-thought, and the authors can credibly claim a new point on the cost-accuracy curve under their own accounting. What does not survive: any read of this as AGI progress, any read of the hidden scratchpad as "thinking like a human," and any read of the cost figure as a production price. The mechanism is interesting. The benchmark is real. The headline is one configuration, one benchmark, one paper.
Independent reproduction of the cost-accuracy point is the next data point. 29.5% pass@2 is, for now, the cheapest credible non-trivial ARC-AGI-1 result on the public record, not a settled finding.