New AI architecture delivers 100x faster reasoning than LLMs with just 1,000 training examples

New AI architecture delivers 100x faster reasoning than LLMs with just 1,000 training examples | VentureBeat

Hierarchical Reasoning Models (HRM) tackle complex reasoning tasks while being smaller, faster, and more data-efficient than large AI models.

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Singapore-based startup Sapient Intelligence just dropped the Hierarchical Reasoning Model (HRM), a brain-inspired AI that trades the bulky “chain-of-thought” tricks of today’s LLMs for a two-tiered, recurrent setup—slow, big-picture planning plus fast, detail-churning loops. The result? Near-perfect scores on insanely hard Sudoku and maze puzzles (0% success for CoT models!), solid gains on the ARC-AGI benchmark, and all with a fraction of the data, memory and training time.

Beyond the bragging rights, HRM’s lean, parallel reasoning promises up to 100× faster task completion and huge cost savings, making it ideal for edge devices, robotics or data-scarce enterprise use cases (think logistics, diagnostics or scientific exploration). And Sapient isn’t stopping at puzzles: they’re already cooking up a general-purpose, self-correcting brain-style engine for healthcare, climate forecasting and more.