SINGAPORE, JULY 21, 2025 – (ACN Newswire) – AGI research company Sapient Intelligence today announced the open-source release of its Hierarchical Reasoning Model (HRM), a brain-inspired architecture that leverages hierarchical structure and multi-timescale processing to achieve substantial computational depth without sacrificing training stability or efficiency. Trained on just 1000 examples without pre-training, with only 27 million parameters, HRM successfully tackles reasoning challenges that continue to frustrate today’s large language models (LLMs).
Beyond LLM Reasoning Limits
Current LLMs depend heavily on Chain-of-Thought prompting, an approach that often suffers from brittle task decomposition, immense training data demands and high latency. Inspired by the hierarchical and multi-timescale processing in the human brain, HRM overcomes these constraints by embracing three fundamental principles observed in cortical computation: hierarchical processing, temporal separation, and recurrent connectivity. Composed of a high-level module performing slow, abstract planning and a low-level module executing rapid, detailed computations, HRM is capable of alternating dynamically between automatic thinking (“System 1”) and deliberate reasoning (“System 2”) in a single forward pass.
“AGI is really about giving machines human-level, and eventually beyond-human, intelligence. CoT lets the models imitate human reasoning by playing the odds, and it’s only a workaround. At Sapient, we’re starting from scratch with a brain-inspired architecture, because nature has already spent billions of years perfecting it. Our model actually thinks and reasons like a person, not just crunches probabilities to ace benchmarks. We believe it will reach, then surpass, human intelligence, and that’s when the AGI conversation gets real,” said Guan Wang, founder and CEO of Sapient Intelligence.
Inspired by the brain, HRM has two recurrent networks operating at different timescales to collaboratively solve tasks [Sapient 2025.07.21]
Benchmark Breakthroughs
Despite its compact scale of 27 million parameters and using only 1000 input-output examples,all without any pre-training or Chain-of-Thought supervision, HRM learns to solve problems thateven the most advanced LLMs struggle with. In the Abstraction and Reasoning Corpus (ARC) AGI Challenge, a widely accepted benchmark of inductive reasoning, HRM archives aperformance of 5% on ARC-AGI-2, significantly outperforming OpenAI o3-mini-high, DeepSeekR1, and Claude 3.7 8K, all of which rely on far larger sizes and context lengths. In complex Sudoku puzzles and optimal pathfinding in 30×30 mazes, where state-of-the-art CoT methods completely fail, HRM delivers near-perfect accuracy.
With only about 1000 training examples, the HRM (~27M parameters) surpasses state-of-the-art CoT models on ARC-AGI, Sudoku-Extreme, and Maze-Hard [Sapient 2025.07.21]
The Sapient Intelligence team is already running new experiments and expect to publish even stronger ARC-AGI scores soon.
Real-World Impact
HRM data efficiency and reasoning accuracy open new opportunities in fields where large datasets are scarce yet accuracy is critical. In healthcare, Sapient is partnering with leading medical research institutions to deploy HRM to support complex diagnostics, particularly rare-disease cases where data signals are sparse, subtle, and demand deep reasoning. In climate forecasting, HRM raises subseasonal-to-seasonal (S2S) forecasting accuracy to 97 %, a leap that translates directly into social and economic value.