# Hierarchical Reasoning Models: Perspectives and Misconceptions

arXiv: https://arxiv.org/abs/2510.00355 · HTML: https://mlsanity.com/paper/2510.00355/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Training Large Language Models to Reason in a Continuous Latent Space](https://mlsanity.com/paper/2412.06769/) (2024)
- [Adaptive Computation Time for Recurrent Neural Networks](https://mlsanity.com/paper/1603.08983/) (2016)
- [Hierarchical Reasoning Model](https://mlsanity.com/paper/2506.21734/) (2025)
- [On the Measure of Intelligence](https://mlsanity.com/paper/1911.01547/) (2019)
- [How Important is Weight Symmetry in Backpropagation?](https://mlsanity.com/paper/1510.05067/) (2015)
- [Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex](https://mlsanity.com/paper/1604.03640/) (2016)
- [Noise Map Guidance: Inversion with Spatial Context for Real Image Editing](https://mlsanity.com/paper/2402.04625/) (2024)
- [How Biological Synapses Self-Assemble Gradient Learning](https://mlsanity.com/paper/2412.20018/) (2024)

## Built on by (most cited first, 13 of 13)

- [Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers](https://mlsanity.com/paper/2606.18206/) (2026)
- [Universal Reasoning Model](https://mlsanity.com/paper/2512.14693/) (2025)
- [Are Your Reasoning Models Reasoning or Guessing? A Mechanistic Analysis of Hierarchical Reasoning Models](https://mlsanity.com/paper/2601.10679/) (2026)
- [Loop the Loopies!](https://mlsanity.com/paper/2607.16051/) (2026)
- [Not All Turns Are Equally Hard: Adaptive Thinking Budgets For Efficient Multi-Turn Reasoning in Agents](https://mlsanity.com/paper/2604.05164/) (2026)
- [Dynamical Systems Theory Behind a Hierarchical Reasoning Model](https://mlsanity.com/paper/2603.22871/) (2026)
- [REIMU: Efficient Heterogeneous Hierarchical Reasoning for SSL-Based Speech Deepfake Detection](https://mlsanity.com/paper/2608.00857/) (2026)
- [Improving Test-Time Scaling with Adaptive Looped Transformers](https://mlsanity.com/paper/2609.35748/) (2026)
- [LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems](https://mlsanity.com/paper/2604.12874/) (2026)
- [One Model, Two Roles: Emergent Specialization in a Shared Recurrent Transformer](https://mlsanity.com/paper/2605.17811/) (2026)
- [Stream Recursion Model (SRM)](https://mlsanity.com/paper/2609.28809/) (2026)
- [LoopVL: Recurrent Visual Intelligence](https://mlsanity.com/paper/2609.38426/) (2026)
- [What Limits Recursive Reasoning Models: Optimization, Architecture and Test-Time Scaling](https://mlsanity.com/paper/2609.39967/) (2026)

## Abstract (arXiv)

Transformers have demonstrated remarkable performance in natural language processing and related domains, as they largely focus on sequential, autoregressive next-token prediction tasks. Yet, they struggle in logical reasoning, not necessarily because of a fundamental limitation of these models, but possibly due to the lack of exploration of more creative uses, such as latent space and recurrent reasoning. An emerging exploration in this direction is the Hierarchical Reasoning Model (Wang et. al., 2025), which introduces a novel type of recurrent reasoning in the latent space of transformers, achieving remarkable performance on a wide range of 2D reasoning tasks. Despite the promising results, this line of models is still at an early stage and calls for in-depth investigation. In this work, we review this class of models, examine key design choices, test alternative variants and clarify common misconceptions.