Compact, Brain-Inspired AI Model Outperforms LLMs on Reasoning Tasks

Introduction: A New Chapter in AI Reasoning

Artificial intelligence has come a long way in just a few short years. From powering chatbots to driving breakthroughs in healthcare, finance, and robotics, large language models (LLMs) like GPT-4, Claude, and DeepSeek have captured global attention. Yet, beneath the hype lies a pressing challenge: scalability.

Modern LLMs rely on billions—sometimes trillions—of parameters, consuming immense computational resources and energy. For instance, estimates suggest that GPT-5 contains 3–5 trillion parameters, demanding enormous infrastructure and costs to train and operate

But what if AI could achieve more with less?

That’s exactly what researchers at the Singapore-based company Sapient have demonstrated with their new Hierarchical Reasoning Model (HRM). This 27-million-parameter, brain-inspired AI system has not only matched but in some cases outperformed frontier LLMs on reasoning benchmarks—all while being 100x smaller

This blog post will take you on a structured deep dive into:

• What makes brain-inspired AI different from traditional LLMs

• How HRM achieved breakthrough results

• What benchmarks like ARC-AGI tell us about AI reasoning

• Why this matters for the future of AI research, engineering, and sustainability

Whether you’re a novice AI engineer or simply curious about where AI is heading, you’ll walk away with a grounded understanding of this exciting paradigm shift.

Why Current AI Scaling is Unsustainable

The Rise of Giant Language Models

Over the past five years, LLMs have grown exponentially in size and capability. Models like GPT-3 (175B parameters), GPT-4 (estimated 1.7T), and GPT-5 (3–5T) showcase how scale correlates with performance. Bigger models generally mean better fluency, reasoning, and versatility.

However, this comes at a steep cost:

• Training Costs: Training GPT-4 is estimated to have cost over $100 million in compute resources.

• Energy Use: Running trillion-parameter models requires massive data centers, increasing carbon footprints.

• Accessibility: Only a handful of tech giants can afford this race, leaving startups and independent researchers behind.

For a novice AI engineer, this scaling trend can feel intimidating. It suggests that breakthroughs are only possible with enormous resources.

The Limits of Chain-of-Thought Reasoning

Another issue lies in how current LLMs “reason.” Many rely on chain-of-thought prompting, where the model generates intermediate steps before arriving at an answer. While useful, this method suffers from:

• Brittle task decomposition (breaking tasks incorrectly)

• Extensive data requirements

• High latency (longer inference times)

This has raised a critical question: Is scaling LLMs really the only path forward?

Enter Brain-Inspired AI

Mimicking the Human Brain

Sapient’s Hierarchical Reasoning Model (HRM) takes inspiration from the human brain’s hierarchical processing. Unlike LLMs that rely purely on scale, HRM introduces structure:

• A high-level module for abstract planning (slow, big-picture thinking)

• A low-level module for detailed computations (fast, focused problem solving)

This dual-process design reflects how humans think: we alternate between deep, deliberate reasoning and rapid, instinctive responses.

How HRM Works

HRM executes reasoning in short bursts of iterative refinement. In each burst, the model decides whether to:

1. Keep refining its thought process

2. Or finalize and submit an answer

This process enables HRM to reason effectively without explicit supervision of intermediate steps, something LLMs often depend on.

Real-World Example: Sudoku and Mazes

To make this concrete, let’s look at HRM’s performance on structured reasoning tasks:

• Sudoku Puzzles: HRM achieved near-perfect accuracy, whereas LLMs often failed completely.

• Maze Pathfinding: HRM reliably found optimal paths, while LLMs struggled to reason spatially

This demonstrates that HRM’s approach isn’t just efficient—it’s qualitatively better at certain reasoning challenges.

Benchmarking Brain-Inspired AI

The ARC-AGI Benchmarks

A major test of AI reasoning is the Abstraction and Reasoning Corpus (ARC), developed to measure progress toward artificial general intelligence (AGI).

• ARC-AGI-1: The original benchmark with visual puzzles testing abstract reasoning.

• ARC-AGI-2: Released in 2025, introducing even harder puzzles and higher expectations

These benchmarks are deliberately challenging—most models score below 10%, while humans average around 60%.

HRM’s Benchmark Results

Here’s how HRM compares against leading models:

Model	Parameters	ARC-AGI-1 Score	ARC-AGI-2 Score
HRM (Sapient)	27M	40.3%	5.0%
OpenAI o3-mini-high	Billions+	34.5%	3.0%
Anthropic Claude 3.7	Billions+	21.2%	0.9%
DeepSeek R1	Billions+	15.8%	1.3%
Human Baseline	–	~85%	~60%00

These results reveal two key insights:

• Compact models can outperform massive LLMs on reasoning-heavy tasks.

• We are still far from human-level AGI, but efficiency-focused designs like HRM show promise.

Architecture vs Training: What Really Matters?

Interestingly, not all experts agree that HRM’s architecture alone explains its success.

The ARC Prize Foundation, which reproduced HRM’s results, found that the training refinement process—not the hierarchical structure itself—was the bigger driver of performance.

This raises a fascinating point: in AI, sometimes training strategy matters more than architecture. For novice engineers, this highlights the importance of carefully designing datasets, training loops, and evaluation metrics—not just model structures.

Why This Matters for AI Engineers

1. A New Path Beyond Scaling

HRM proves that smaller, structured models can rival or even surpass giant LLMs. For AI engineers, this means:

• Innovation isn’t limited to big tech.

• Brain-inspired principles open exciting new research avenues.

• Resource-efficient AI is becoming viable.

2. Democratization of AI

Because HRM is compact (27M parameters), it can run on modest hardware. Imagine running advanced reasoning AI on a laptop, smartphone, or edge device. This opens opportunities for:

• Startups with limited budgets

• Researchers without access to supercomputers

• Applications in remote or low-resource environments

3. Rethinking Evaluation

The ARC-AGI benchmarks remind us that language fluency ≠ reasoning ability. As engineers, it’s important to evaluate models on reasoning, abstraction, and generalization—not just text generation.

Broader Implications for the Future of AI

The End of “Bigger is Always Better”?

As models like HRM gain traction, the industry may move away from blind scaling toward smarter, brain-inspired designs. This shift could:

• Reduce energy consumption and environmental impact

• Make AI research more inclusive and sustainable

• Unlock reasoning capabilities beyond what scaling alone provides

Hybrid Approaches: The Best of Both Worlds

It’s unlikely that brain-inspired models will completely replace LLMs. Instead, we may see hybrid systems:

• LLMs for language fluency and knowledge recall

• Brain-inspired reasoning modules for problem-solving and abstraction

Such combinations could bring us closer to practical AGI.

Lessons for AI Education

For novice AI engineers, HRM’s story is a powerful lesson:

• Study the brain for inspiration

• Focus on efficiency, not just scale

• Pay attention to training refinements as much as architectures

Conclusion: Rethinking AI Progress

The breakthrough of Sapient’s Hierarchical Reasoning Model is a wake-up call for the AI industry. It shows that:

• Compact, brain-inspired models can outperform massive LLMs on reasoning tasks

• Efficiency and structure may matter more than brute-force scale

• Training methodologies play a critical role in unlocking reasoning ability

For AI engineers just starting their journey, the key takeaway is this: don’t be intimidated by trillion-parameter models. Innovation lies not just in size but in creativity, inspiration, and careful design.

As Greg Kamradt, President of the ARC Prize Foundation, put it: “ARC-AGI-2 significantly raises the bar for AI.” Models like HRM prove that this bar can be met without trillion-dollar budgets.

The future of AI may not belong to the biggest models—but to the smartest, most brain-like ones.