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Brain-inspired AI delivers a new approach to machine reasoning
Scientists at Sapient in Singapore have introduced a brain-inspired artificial intelligence architecture called the hierarchical reasoning model (HRM). Unlike conventional large language models (LLMs) that rely on chain-of-thought (CoT) prompting and massive parameter counts, HRM emulates hierarchical and multi-timescale information processing observed in the human brain. According to a preprint published June 26 on arXiv, the HRM achieves strong performance on difficult reasoning benchmarks while using far fewer parameters and training examples. The study reports HRM running with roughly 27 million parameters trained on about 1,000 examples, a dramatic contrast to modern LLMs that often have billions or even trillions of parameters.
The research team tested HRM on the ARC-AGI benchmark, a challenging suite designed to evaluate progress toward artificial general intelligence (AGI). HRM scored 40.3% on ARC-AGI-1 and 5% on ARC-AGI-2 — results that exceeded several contemporary models in the comparison, including OpenAI's o3-mini-high, Anthropic's Claude 3.7, and Deepseek R1. These figures suggest that architecture and training strategy can substantially influence reasoning ability without escalating model scale or dataset size.
How HRM works: hierarchical modules and iterative refinement
HRM replaces explicit chain-of-thought decomposition with a two-module forward pass that mirrors hierarchical processing in neural systems. A high-level module performs slower, abstract planning across longer timescales, while a low-level module executes fast, detailed computations. Rather than generating explicit intermediate natural-language steps, HRM applies iterative refinement across brief bursts of computation. Each burst evaluates whether to continue refining or to emit a final answer. This technique — iterative refinement — is a well-known numerical strategy that improves solution accuracy by repeatedly updating an approximation.
Contrast with chain-of-thought
Most advanced LLMs use CoT to break complex problems into human-readable substeps. CoT can be effective but has documented limitations: brittle task decomposition, substantial data requirements, and added latency from multi-step generation. The HRM design aims to sidestep these issues by embedding hierarchical control and refinement directly into the forward computation, reducing the need for extensive supervised intermediate-step labels.
Benchmark performance, reproduction, and caveats
HRM demonstrated strong performance on tasks that demand structured reasoning, including near-perfect results on complex Sudoku problems and improved maze pathfinding compared with typical LLMs. The authors open-sourced their implementation on GitHub, enabling independent verification. After reproducing the submitted scores, the ARC-AGI organizers reported additional findings: some of HRM’s gains appear driven not primarily by the hierarchical architecture itself but by a refinement process applied during training that was under-documented in the initial report. Importantly, the arXiv paper has not yet been peer-reviewed, so the broader community should treat results as provisional while follow-up studies and code audits clarify which factors are critical to performance.
The contrast between HRM’s compact model size and the enormous scale of recent LLM releases highlights an ongoing research theme: algorithmic and architectural improvements can sometimes substitute for brute-force parameter scaling. This has implications for compute efficiency, energy usage, and the accessibility of advanced AI capabilities to researchers and institutions without massive infrastructure budgets.
Expert Insight
"HRM is an interesting demonstration that structured, brain-inspired design can yield competitive reasoning without extreme scale," says Dr. Lina Moreno, a computational neuroscientist (fictional). "The key questions now are reproducibility and generalization: can HRM-style training and refinement transfer to a wider range of tasks and datasets? If so, we may see a shift toward more efficient, interpretable reasoning systems."
Conclusion
HRM offers a promising, brain-inspired alternative to chain-of-thought reasoning in large language models. Early results on the ARC-AGI benchmark show improved reasoning with far fewer parameters and training examples, but the findings remain provisional pending peer review and independent analyses. Whether HRM’s hierarchical design or the undocumented refinement steps are primary drivers of success will determine how the community adopts and extends this approach. For now, HRM emphasizes that smarter architectures and training techniques can complement — and sometimes reduce — the need for ever-larger model scales in advancing AI reasoning capabilities.
Source: livescience
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