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Singapore-based AI startup Sapient Intelligence has developed a new AI architecture that can match, and in some cases vastly outperform, large language models (LLMs) on complex reasoning tasks, all while being significantly smaller and more data-efficient.
The architecture, known as the Hierarchical Reasoning Model (HRM), is inspired by how the human brain utilizes distinct systems for slow, deliberate planning and fast, intuitive computation. The model achieves impressive results with a fraction of the data and memory required by today’s LLMs. This efficiency could have important implications for real-world enterprise AI applications where data is scarce and computational resources are limited.
The limits of chain-of-thought reasoning
When faced with a complex problem, current LLMs largely rely on chain-of-thought (CoT) prompting, breaking down problems into intermediate text-based steps, essentially forcing the model to “think out loud” as it works toward a solution.
While CoT has improved the reasoning abilities of LLMs, it has fundamental limitations. In their paper, researchers at Sapient Intelligence argue that “CoT for reasoning is a crutch, not a satisfactory solution. It relies on brittle, human-defined decompositions where a single misstep or a misorder of the steps can derail the reasoning process entirely.”
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This dependency on generating explicit language tethers the model’s reasoning to the token level, often requiring massive amounts of training data and producing long, slow responses. This approach also overlooks the type of “latent reasoning” that occurs internally, without being explicitly articulated in language.
As the researchers note, “A more efficient approach is needed to minimize these data requirements.”
A hierarchical approach inspired by the brain
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