# Preparation of Fractal-Inspired Computational Architectures for Advanced Large Language Model Analysis

arXiv: https://arxiv.org/abs/2511.07329 · HTML: https://mlsanity.com/paper/2511.07329/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [FractalNet: Ultra-Deep Neural Networks without Residuals](https://mlsanity.com/paper/1605.07648/) (2016)
- [Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?](https://mlsanity.com/paper/2504.06006/) (2025)
- [LEMUR Neural Network Dataset: Towards Seamless AutoML](https://mlsanity.com/paper/2504.10552/) (2025)
- [VIST-GPT: Ushering in the Era of Visual Storytelling with LLMs?](https://mlsanity.com/paper/2504.19267/) (2025)
- [AugmentGest: Can Random Data Cropping Augmentation Boost Gesture Recognition Performance?](https://mlsanity.com/paper/2506.07216/) (2025)

## Built on by (most cited first, 11 of 11)

- [A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks](https://mlsanity.com/paper/2512.04329/) (2025)
- [Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design](https://mlsanity.com/paper/2512.24120/) (2025)
- [From Memorization to Creativity: LLM as a Designer of Novel Neural Architectures](https://mlsanity.com/paper/2601.02997/) (2026)
- [LLM as a Neural Architect: Controlled Generation of Image Captioning Models Under Strict API Contracts](https://mlsanity.com/paper/2512.14706/) (2025)
- [From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs](https://mlsanity.com/paper/2601.03808/) (2026)
- [Resource-Efficient Iterative LLM-Based NAS with Feedback Memory](https://mlsanity.com/paper/2603.12091/) (2026)
- [Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models](https://mlsanity.com/paper/2601.08517/) (2026)
- [Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffs](https://mlsanity.com/paper/2605.04903/) (2026)
- [LEMUR 2: Unlocking Neural Network Diversity for AI](https://mlsanity.com/paper/2607.06839/) (2026)
- [Systematic Exploration of 4-Expert Heterogeneous Mixture-of-Experts via Automated Pipeline Search](https://mlsanity.com/paper/2606.23739/) (2026)
- [Towards Robust Training in NNGPT AutoML Pipeline: A Loss-Optimizer Pairing Selection Study](https://mlsanity.com/paper/2606.20933/) (2026)

## Abstract (arXiv)

This paper proposes FractalNet, a framework based on fractal design principles that automatically generates and evaluates convolutional neural network (CNN) architectures using recursive template patterns. Rather than relying on computationally expensive Neural Architecture Search (NAS) methods, the framework explores a structured architecture space defined by recursive fractal templates that systematically vary key parameters such as fractal depth, column width, and layer configurations. The framework consists of three core components: a generator that produces candidate architectures via controlled permutations of convolutional, normalization, activation, and dropout layers; a fractal template module that enforces recursive multi-path structural patterns; and a runner module that manages model training, evaluation, and logging. Using this system, over 1,200 distinct CNN architectures were automatically generated and evaluated on the CIFAR-10 image classification benchmark. Training was performed in PyTorch using stochastic gradient descent with Automatic Mixed Precision (AMP) and gradient checkpointing to reduce computational overhead. Experimental results demonstrate that fractal-based architectures exhibit stable training dynamics and achieve competitive performance, with an average validation accuracy of 60-70% and a peak accuracy of 80.18% after only five training epochs. These findings suggest that recursive fractal structures provide an effective means of balancing network depth and width while supporting large-scale automated architecture exploration. The proposed framework offers a resource-efficient and interpretable approach to systematic neural architecture experimentation.