arXiv:2507.15336v3 Announce Type: replace 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 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-
The continuous drive for more efficient and adaptable AI models, particularly as computational resources become a bottleneck, necessitates novel approaches to neural network design.
This development proposes a method to optimize neural network design without the prohibitive computational cost of traditional architectural search, offering a significant improvement in AI development efficiency.
The paradigm shifts from brute-force architectural search or static model retrieval to an evidence-based, dynamic refinement process using 'edit-effect evidence' and 'evidence graphs'.
- · AI developers
- · Cloud computing providers (reduced compute intensity per model)
- · Industries deploying AI models
- · AI research institutions
- · Companies reliant on computationally intensive NAS solutions
- · Providers of static model checkpoints
More efficient and faster development of specialized high-performance neural networks.
Accelerated deployment of AI solutions across various industries due to reduced development costs and time.
Enhanced accessibility to advanced AI capabilities for smaller organizations and researchers with limited compute resources, potentially democratizing AI development.
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Read at arXiv cs.LG