CoRA-NAS is a two‑stage neural architecture search framework that first uses a static coarse ranking (CoRA‑Rank) based on capacity and structure‑at‑initialization proxies, then refines this ranking with low‑cost learning‑curve extrapolation (CoRA‑Refine) using an ExtraTrees model. The method achieves high Spearman correlations across multiple benchmark spaces and selects architectures with accuracy close to the ground‑truth best, all while using only about 1% of the training cost of fully training the candidate set. CoRA‑NAS provides a single configuration that works across different search spaces, combining cross‑space ranking robustness with efficient architecture selection.
CoRA-NAS is a two‑stage neural architecture search framework that first uses a static coarse ranking (CoRA‑Rank) based on capacity and structure‑at‑initialization proxies, then refines this ranking with low‑cost learning‑curve extrapolation (CoRA‑Refine) using anchor samples and an ExtraTrees residual model. The method achieves high Spearman correlations across multiple NAS benchmarks and selects architectures that approach the best‑known accuracy with only about 1% of the training cost, without relying on fully trained labels for ranking. It demonstrates robust cross‑space performance and improves over static capacity proxies, especially in size‑only search spaces.
By Yifan Yang, Zhaoyan Wang, Zheng Gao, Xiaoyu Li, Jiaojiao Jiang
arXiv:2609.14184v1 Announce Type: new
Abstract: Neural Architecture Search (NAS) automates network design, but evaluating a single candidate requires training it to convergence, making exhaustive sea...
By Hassan Touayouch, Rabie Najem, Mohammed Benjelloun
LESS (Lightweight Evolutionary Supernet Search) is a data‑driven NAS method that uses a brief hard‑path warm‑up and CMA‑ES to evaluate candidate architectures as decoded hard genotypes after six supernet updates. On NAS‑Bench‑201, LESS attains 93.189 % CIFAR‑10 accuracy in just 409.1 seconds, nearly matching FairNAS while using only about 1/24 of its search time. The approach also transfers well to CIFAR‑100, ImageNet16‑120, and the larger DARTS space, achieving high accuracies with searches completed in roughly 43.5 minutes on a single GPU.
By Aviral Gandhi, Jinglue Xu, Jialong Li, Hitoshi Iba
ONNX-Net introduces a universal representation for neural architectures using natural language descriptions, enabling instant performance prediction across diverse search spaces. The authors present ONNX-Bench, a benchmark of over 600k architecture–accuracy pairs compiled from open‑source NAS‑bench networks in ONNX format. Experiments demonstrate strong zero‑shot predictive performance with minimal pretraining, overcoming the limitations of cell‑based, graph‑encoded approaches.
By Shiwen Qin, Alexander Auras, Shay B. Cohen, Elliot J. Crowley, Michael Moeller, Linus Ericsson, Jovita Lukasik
arXiv:2609.30738v1 Announce Type: new
Abstract: KV cache eviction methods such as SnapKV and PyramidKV rank tokens solely by mean attention over a small observation window. We study a unified score,...
By Tianfang Xie, Wei Zhu