Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design 事件

Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design arXiv:2507.15336v3 Announce Type: replace-cross Abstract: 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 in