Introduction: The AI Arms Race and the Search for Reasoning

Artificial intelligence in 2025 feels a lot like the space race in the 1960s. Each breakthrough is celebrated as if it were a moon landing. We’ve seen ChatGPT revolutionize consumer AI, Claude push boundaries on constitutional alignment, Gemini flex its multimodal power, and LLaMA make open-weight models more accessible than ever.

But amid this frenzy, one model emerging from Singapore has begun to turn heads: the Hierarchical Reasoning Model (HRM).

Unlike ChatGPT or Claude, which are scaled descendants of the transformer architecture, HRM was designed from the ground up to reason more like a human brain. Instead of predicting the next word with brute force, HRM organizes problems into hierarchies of reasoning steps—allowing it to outperform bigger models on logical reasoning, planning, and decision-making tasks.

This is more than an academic curiosity. HRM could signal a turning point: from AI as a “pattern mimic” to AI as a “reasoning engine.”

In this blog, we’ll unpack HRM in full detail, compare it with ChatGPT and Claude, explore its architecture, applications, and limitations, and position it within Singapore’s bold AI ecosystem. By the end, you’ll understand why HRM isn’t just another model—it’s a blueprint for the next chapter of AI.

Chapter 1: What Exactly Is HRM?

At its core, HRM (Hierarchical Reasoning Model) is an AI framework developed under Singapore’s AI Singapore initiative, in collaboration with research labs at the National University of Singapore (NUS) and partners in government and industry.

It differs from ChatGPT and Claude in three fundamental ways:

1. Hierarchy of Thought
   HRM breaks problems into smaller sub-problems, solves each layer in sequence, and then recombines them into a final answer—much like how humans use outlines before writing essays.
2. Sparse but Structured Computation
   Instead of activating billions of parameters for every input like GPT models do, HRM selectively routes attention through hierarchical modules. This makes it more efficient.
3. Domain-Specific Anchoring
   HRM was optimized for business and HR management tasks (hence the name overlap with HRM = Human Resource Management). It excels at reasoning in structured environments like employee scheduling, performance evaluation, and compliance.

Think of it like this:

• ChatGPT is a generalist, great at conversation, summarization, and creative text.
• Claude is a constitutionalist, designed for safer outputs and alignment.
• HRM is a specialist reasoning machine, designed to think in steps and handle structured, logic-heavy tasks.

Chapter 2: HRM vs ChatGPT vs Claude – The Comparison

Let’s stack them side by side.

FeatureHRM (Singapore)ChatGPT (OpenAI)Claude (Anthropic)ArchitectureHierarchical modular reasoningTransformer (decoder-only)Transformer with “constitutional” alignmentStrengthLogical reasoning, planning, HR/business analyticsConversational fluency, general-purposeSafer outputs, longer context (200k+ tokens)EfficiencyHigh (activates fewer parameters)Lower (massive compute footprint)Medium (optimized but still large-scale)ApplicationsHR, talent management, compliance, structured decisionsChatbots, code generation, creative tasksLong-form summarization, safe enterprise AILimitationsNarrower domain, less creativityCan hallucinate facts, ethics debatedExpensive inference, sometimes vagueOriginAI Singapore + NUS ecosystemOpenAI, USAnthropic, US

Key Observations

• Reasoning Accuracy: In benchmark tests (ARC-AGI, GSM8k math), HRM scores higher than ChatGPT-3.5 and Claude 2 on step-by-step logic problems.
• Efficiency: HRM achieves similar reasoning performance with fewer FLOPs (floating point operations), making it cheaper to run at scale.
• Specialization: Unlike ChatGPT and Claude, which are designed for broad consumer tasks, HRM is deliberately optimized for business and HR contexts.

In short: ChatGPT and Claude are Swiss Army knives. HRM is a scalpel.

Chapter 3: The Architecture of HRM

This is where HRM gets fascinating.

Traditional transformer models (GPT, Claude, Gemini) rely on attention layers stacked in depth. They predict tokens sequentially. The brilliance of transformers lies in scaling: more data, more layers, more compute.

But transformers have limits:

• They hallucinate.
• They struggle with multi-step reasoning.
• They consume enormous compute.

HRM approaches the problem differently.

1. Hierarchical Modules

Instead of one giant stack, HRM organizes computation into modules arranged hierarchically:

• Top-level planner: Breaks down the query into sub-tasks.
• Mid-level solvers: Handle reasoning for each component.
• Low-level execution engines: Perform token-level generation.

This mirrors how humans think: we plan, break down, then execute.

2. Memory Integration

HRM uses persistent memory slots for each hierarchical level. This allows it to “remember” decisions across sub-tasks better than flat transformers.

3. Sparse Routing

Instead of firing up all neurons for each input, HRM routes attention only through relevant sub-modules. This massively reduces compute overhead.

4. Explainability by Design

Because reasoning steps are modular, HRM can output its reasoning chain in a structured way—something GPT models struggle with.

Chapter 4: HRM in Action – Business & HR Applications

Why does this matter? Because HRM was not just built as an experiment—it was designed to solve business problems.

1. Workforce Scheduling

Factories, airlines, hospitals—all struggle with shift scheduling. HRM can reason hierarchically about constraints (labor law, employee preferences, workload balancing) and generate optimal schedules.

2. Performance Evaluation

Instead of a manager manually reviewing dozens of metrics, HRM can ingest structured HR data, evaluate against KPIs, and recommend promotions, bonuses, or training.

3. Compliance & Policy Reasoning

Singapore is known for strict regulatory frameworks. HRM can parse legislation, cross-reference company policies, and flag compliance risks in employee contracts.

4. Talent Analytics

By analyzing internal datasets, HRM can predict attrition risks, recommend training pathways, and simulate workforce planning scenarios.

This focus gives HRM a competitive edge: while ChatGPT dazzles consumers, HRM directly plugs into boardroom decisions.

Chapter 5: Strengths and Limitations

Strengths

• Reasoning Accuracy: Outperforms ChatGPT-3.5 and Claude 2 on logic-heavy benchmarks.
• Efficiency: Requires less compute per inference.
• Explainability: Easier to audit reasoning steps.
• Domain Fit: Optimized for HR, compliance, and structured analytics.

Limitations

• Narrower Scope: Lacks creativity and versatility of ChatGPT/Claude.
• Ecosystem: Lacks global developer community and plugin ecosystem.
• Scaling Unknowns: Still untested at consumer internet scale (millions of queries per day).

Chapter 6: Ethical Considerations

HRM raises unique ethical questions:

1. Employee Privacy – Using HRM in performance evaluation could bias outcomes if data isn’t anonymized or audited.
2. Decision Accountability – If HRM recommends firing an employee, who’s responsible? The manager or the algorithm?
3. National Strategy – By developing HRM, Singapore positions itself as an AI innovator. But how does this affect global governance debates dominated by the US and China?

Singapore’s regulators are keenly aware of this. HRM is being developed under transparent governance frameworks, making it a test case for responsible AI adoption.

Chapter 7: Singapore’s AI Ecosystem

To understand HRM, we need to understand Singapore’s AI strategy.

• AI Singapore (AISG): A national initiative launched in 2017 to build world-class AI capability.
• National AI Strategy 2.0 (2023): Emphasizes explainable, human-centered AI.
• Talent Development: Programs like AISG’s AI Apprenticeship ensure a steady pipeline of AI engineers.
• Regional Hub: Singapore is positioning itself as the “neutral ground” between US and China AI ecosystems.

HRM is the crown jewel of this ecosystem—a model that showcases not just research capability, but strategic intent.

Chapter 8: Strategic Outlook

So where does this go?

I see three possible futures:

1. HRM as an Enterprise Standard
   Singapore deploys HRM in HR software, compliance tools, and workforce analytics. It becomes a niche standard for structured business reasoning.
2. HRM Inspires Global Research
   Other labs adopt hierarchical reasoning approaches, moving away from brute-force scaling. HRM is remembered as the first domino in “post-transformer AI.”
3. HRM as National Differentiator
   Singapore uses HRM as proof of its sovereign AI capability, attracting global investment and positioning itself as an AI governance leader.

Conclusion: The Rise of Hierarchical AI

The last five years were the era of transformers. Scale ruled. More data, bigger models, higher FLOPs.

But HRM signals a new era: one where architecture matters as much as scale.

By organizing reasoning hierarchically, HRM shows that smaller, more efficient models can beat giants on the very thing humans care about: thinking.

Singapore may not have the compute firepower of OpenAI or Anthropic. But with HRM, it has something just as valuable: a new paradigm.

And in the years ahead, that paradigm might just rewrite the playbook of artificial intelligence.

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