Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural architectural search is computationally expensive, while model retrieval often yields suboptimal static checkpoints. To resolve this dilemma, we model the performance gains induced by fine-grained architectural modifications as edit-effect evidence and build evidence graphs from prior tasks. By constructing a retrieval-augmented model refinement framework, our proposed M-DESIGN dynamically weaves historical evidence to discover near-optimal modification paths. M-DESIGN features an adaptive retrieval mechanism that quickly calibrates the evolving transferability of edit-effect evidence from different sources. To handle out-of-distribution shifts, we introduce predictive task planners that extrapolate gains from multi-hop evidence, thereby reducing reliance on an exhaustive repository. Based on our model knowledge base of 67,760 graph neural networks across 22 datasets, extensive experiments demonstrate that M-DESIGN consistently outperforms baselines, achieving the search-space best performance in 26 out of 33 cases under a strict budget.

翻译：为新任务设计高性能神经网络需要在优化质量与搜索效率之间取得平衡。当前方法未能实现这一平衡：神经架构搜索计算成本高昂，而模型检索通常只能获得次优的静态检查点。为解决这一困境，我们将细粒度架构修改引发的性能增益建模为编辑效应证据，并基于先前任务构建证据图。通过构建检索增强的模型精化框架，我们提出的M-DESIGN能够动态编织历史证据以发现接近最优的修改路径。M-DESIGN具备自适应检索机制，可快速校准来自不同来源的编辑效应证据的动态可迁移性。为处理分布外偏移，我们引入了预测性任务规划器，通过多跳证据外推性能增益，从而降低对穷举知识库的依赖。基于我们在22个数据集上构建的包含67,760个图神经网络模型的知识库，大量实验表明M-DESIGN在严格预算约束下持续优于基线方法，在33个案例中的26个案例中实现了搜索空间内的最佳性能。