Generation of neural networks with LLM guided by architectures of the same family

Large language models (LLMs) have demonstrated a surprising ability to generate code, including modifications to neural network architectures. However, unrestricted generation often produces invalid or harmful results. A more promising approach involves guiding the LLM using a strong source model from the same family as the weak target model. This strategy, known as generation guided by architectures of the same family, allows knowledge transfer between related models, improving the performance of the weaker one without the need for training from scratch. Instead of directly copying recipes, the LLM adapts the modifications, which explains why literal copying of the source model does not yield significant improvements, while contextual guidance does. This approach opens new possibilities in the field of automated neural network optimization, especially when combined with a protocol that separates validity from accuracy and selects the best candidate only if it truly improves the original model. From a business perspective, integrating this technique into platforms for artificial intelligence for businesses accelerates the development of more efficient models without requiring massive teams of experts. Companies like Q2BSTUDIO, specializing in custom applications and custom software, can incorporate these processes into their AI agent services, offering solutions that continuously improve the performance of already deployed systems. Generation guided by model families also benefits from cloud infrastructure; by using aws and azure cloud services, it is possible to scale validation and candidate selection experiments without prohibitive costs. Additionally, the cybersecurity of these processes is critical, as any automatic modification must pass rigorous checks to avoid introducing vulnerabilities. Analysis at the level of architecture families, such as AlexNet or lightweight convolutional networks, shows clear patterns of success when contextual guidance is employed: across multiple datasets and model types, guided generation far outperforms source-free alternatives. This finding reinforces the idea that LLMs not only generate code but can act as adaptive network architects, provided they are given an appropriate context. In practice, a business intelligence team could use these methods to adjust image classification models in production environments, connecting them with tools like power bi to visualize the impact on business metrics. Ultimately, the combination of LLMs with guidance from architectures of the same family represents a step toward the intelligent automation of neural network design, with direct applications in optimizing existing models and creating new capabilities for clients seeking high-performance AI for businesses.