The Sequence AI of the Week #867: Thinking in Latents: Why Sapient's HRM-Text Is a Quiet Rebuke to Chain-of-Thought

2026-05-27 · Source: TheSequence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Sapient Intelligence's HRM-Text model challenges the prevalent Chain-of-Thought (CoT) approach in large language models by proposing internal, variable-depth reasoning within the latent space. Modern Transformers, despite their size, are fundamentally shallow, operating within complexity classes like AC⁰ or TC⁰, meaning they cannot solve problems requiring sequential computation in a single pass. CoT attempts to address this by "renting depth" from output tokens, forcing intermediate reasoning steps to become discrete tokens before re-entering the model. HRM-Text, an extension of the original Hierarchical Reasoning Model, aims to fix this architectural limitation by enabling deeper, more efficient computation directly within the model's internal representations, avoiding the mechanical inefficiency of token-based scratchpads.

Key takeaway

For machine learning engineers evaluating LLM reasoning capabilities, recognize that Chain-of-Thought is a mechanical workaround for architectural limitations, not true internal reasoning. You should consider exploring models like Sapient's HRM-Text that offer variable, internal depth for more efficient and fundamentally deeper computational processes. This shift could lead to more robust and less resource-intensive reasoning in future LLM designs.

Key insights

Chain-of-Thought is an inefficient workaround for Transformer's inherent shallow computational depth.

Principles

• Transformers are fixed-depth architectures, limited to complexity classes like AC⁰ or TC⁰.
• Chain-of-Thought "rents depth" by externalizing internal computation to output tokens.
• True reasoning requires variable, internal depth within the latent space.

Method

Sapient's HRM-Text achieves variable, internal depth by architectural modifications, enabling reasoning in the latent space rather than relying on increased model size or CoT training.

Topics

• Chain-of-Thought
• Latent Space Reasoning
• Transformer Architecture
• LLM Reasoning
• Sapient Intelligence
• HRM-Text

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• LLM Reasoning Is Latent, Not the Chain of Thought — LLM reasoning is best understood as latent-state trajectory formation, not explicit chain-of-thought.
• Chain-of-Thought Degrades Visual Spatial Reasoning Capabilities of Multimodal LLMs — Chain-of-Thought prompting degrades visual spatial reasoning in multimodal LLMs due to shortcut learning and hallucination.
• Fragile Thoughts: How Large Language Models Handle Chain-of-Thought Perturbations — LLM robustness to CoT perturbations varies significantly by type and model scale, challenging assumptions that size alone ensures reliability.
• 🥇Top AI Papers of the Week — AI advancements focus on improving reasoning, context management, and personalization for LLMs and agents.
• How Anthropic’s Claude Thinks — Claude's internal processes often diverge from its self-reported reasoning, revealing complex, non-human-like cognitive strategies.
• Why We Think — Allocating more test-time compute via methods like Chain-of-Thought significantly boosts LLM reasoning and problem-solving capabilities.

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Editorial summary, takeaway, and curation by AIssential. Original article published by TheSequence.