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BDH-CQ: A 150M model just broke the ARC-AGI cost curve
AI, But Simple Issue #115
Edwin Dong
August 17, 2026
BDH-CQ: A 150M model just broke the ARC-AGI cost curve
AI, But Simple Issue #115
Many of you will remember the BDH architecture from a few weeks prior (if not, I’ve linked it above).
The same research team at Pathway recently built a new reasoning system on top of it, and it’s been the top paper on Huggingface this past week.
They call it BDH-CQ (Engdahl et al., 2026), and through combining in-context learning and recurrent latent reasoning, it obtains outstanding benchmark results… just not in the way you think.
Instead of pushing for pure benchmark accuracy, BDH-CQ represents a potential paradigm shift in cost-efficiency scaling.
One of the key results shows a 5-point benchmark decrease (-14%) on ARC-AGI 1 for an 11x cost reduction per task compared to GPT-5.6 Luna.
It’s able to challenge the previously reported cost-accuracy Pareto frontier while holding just 150M parameters.
The key contribution is a recurrent latent reasoning system. Typical reasoning models will generate intermediate thinking tokens in a scratchpad, consume them, and then continue to reason.
BDH-CQ poses a pretty interesting question: do we really need to resolve reasoning into discrete words (tokens)?
We’ll cover the surprising benchmark results, how BDH-CQ scales, get into how the architecture actually works, then discuss some key limitations from a detailed analysis from the paper.