In a new research paper published Aug. 10 on the preprint server arXiv, scientists at AI company Pathway detailed the technical foundations of its new BDH-CQ model. This follows a precursor model known as "Dragon Hatchling" that the scientists created in 2025, which was designed to accurately simulate how the neurons in the brain connected and strengthened during the learning experience.
The 2019 benchmark, known as ARC-AGI, uses nonverbal reasoning puzzles — such as rotating a series of shapes to complete a sequence — to measure the cognitive ability of AI systems. Whereas humans are highly skilled at inferring the rules of these types of puzzles through trial and error, early AI systems were historically much less skilled.
For example, while OpenAI's entry-level lightweight reasoning model GPT 5.6 Luna (Low) achieved a slightly higher score, the study stated that this "modest accuracy gain" cost roughly 11 times as much as BDH-CQ in terms of relative token costs — the metering system that AI companies use to measure the cost of running AI systems. This type of AI model architecture, if adopted widely, could have a dramatic impact on the overall cost and scale of AI deployments, the scientists believe.
The researchers, however, said these results also imply that the model's cognition capabilities could scale significantly when expanded to larger parameter sizes.
Most mainstream AI models, such as those powering Claude and ChatGPT, are based on "transformer models," so called because they transform user inputs into interconnected mathematical reference points. These systems look at every word within an input simultaneously, which allows them to infer context from position, such as deciding based on nearby words whether the word "bark" refers to dogs or trees.
Leading AI models have been criticized for being expensive to run. (Image credit: Jaque Silva/NurPhoto via Getty Images)These models have significant advantages over earlier architectures, which would often forget the start of an input by the time they reached the end. However, transformer architectures can struggle with longer or more complex prompts, as the computational complexity of evaluating the prompt increases quadratically — meaning that doubling the length of an input uses four times as much processing power.
AI's next generation?
Conventional transformer-based token generation is prone to causing memory bottlenecks, as AI re-reads every previous word in the conversation with every new word generated. Eventually, this will clog up the memory in the graphics processing units (GPUs) used for AI operations.
Transformer-based models retain prompts and interaction histories as a long string of numerical values representing the text of requests. That string then expands as new tokens are added through processing the request. BDH-CQ uses numerical arrays to represent the underlying rules and contextual patterns of a task, using numbers to track relationships between chunks of information rather than defining them in text.
To execute tasks, BDH-CQ implements a "latent reasoning engine" as its internal workspace. Using numbers to represent the different elements of a prompt or problem, it carries out a series of iterative recurrent loops to determine the best answer to return based on the prompt. The model takes the output of the last loop, assesses how the result could be improved based on its training data, and feeds back the previous output as the starting point for the next iteration. It repeats this for a pre-set number of loops, with each iteration theoretically closer to the desired outcome.
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"I've followed Pathway closely and replicated their ARC-AGI-1 results myself," Kaiser said in a statement. "Pathway shows that model architecture, not just scale, can drive the next leap in AI reasoning."
Pathway plans to scale the BDH architecture up to 600 billion parameters and apply its vector-based reasoning to more challenging benchmarks, such as ARC-AGI-2 and ARC-AGI-3, as well as develop a fully-fledged large language model (LLM) based on the technology, which would provide a basis for building text-based chatbots. The company hopes the technology can be applied to complex reasoning problems in sectors such as cybersecurity incident response and industrial operations.
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