这篇论文告诉你LLM不仅能写代码，还能帮你自动改进神经网络架构，效果比瞎试好得多，尤其适合有相似模型族的情况。
该论文提出一种源引导的候选生成协议，利用同族强源模型指导弱目标模型的神经网络修改，避免无效生成。在CIFAR-10上，源引导候选达到0.5049准确率，优于非源候选的0.2398，提升弱目标（原0.1254）0.2651；五轮检查后保持0.7686 vs 0.4839。在SVHN AlexNet上使用DeepSeek-Coder-6.7B，源引导迁移达0.7880 vs 0.2254，重复实验达0.8069 vs 0.2509。直接复制源配置仅得0.1959，表明LLM适应而非复制。家庭级分析显示AlexNet家族6/8胜，alt_nn1家族8/10胜。
LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation
Large language models (LLMs) can generate neural-network modifications, but unrestricted generation is often invalid or harmful. This paper studies a narrower setting: improving a weak target model using a stronger same-family source model from a neural-network database. We propose a source-guided candidate-generation protocol with non-source controls, source-conditioned candidates, and a no-LLM hp_copy ablation under equal evaluation budgets. The protocol reports validity separately from accuracy and selects the best valid candidate only when it improves the target. On CIFAR-10, the strongest source-guided candidate reaches 0.5049 accuracy versus 0.2398 for the best non-source candidate, a +0.2651 advantage, while improving a weak target originally at 0.1254; a five-epoch check preserves the gain at 0.7686 versus 0.4839. On SVHN AlexNet with DeepSeek-Coder-6.7B, source-guided transfer reaches 0.7880 versus 0.2254, a +0.5626 advantage; a fresh repeat reaches 0.8069 versus 0.2509, a +0.5560 advantage. Direct source-recipe copy produces 0.1959 on SVHN AlexNet, matching the original target, while hp_transfer reaches 0.7880, showing that the LLM adapts rather than copies the source recipe. Family-level analysis shows the clearest positive signals for AlexNet, with 6/8 wins across SVHN, Imagenette, and CelebA-Gender, and alt_nn1, with 8/10 wins on CIFAR-10.