基本信息
- 来源: arxiv
- 原始来源: https://arxiv.org/abs/2602.18419v1
- 作者: Geri Skenderi, Lorenzo Buffoni, Francesco D’Amico, David Machado, Raffaele Marino, Matteo Negri, Federico Ricci-Tersenghi, Carlo Lucibello, Maria Chiara Angelini
- 分类: cond-mat.dis-nn
- 论文时间: 2026-02-20T18:41:48Z
- 论文 PDF: https://arxiv.org/pdf/2602.18419v1.pdf
来源摘要/节选
Graph neural networks (GNNs) are increasingly applied to hard optimization problems, often claiming superiority over classical heuristics. However, such claims risk being unsolid due to a lack of standard benchmarks on truly hard instances. From a statistical physics perspective, we propose new hard benchmarks based on random problems. We provide these benchmarks, along with performance results from both classical heuristics and GNNs. Our fair comparison shows that classical algorithms still outperform GNNs. We discuss the challenges for neural networks in this domain. Future claims of superiority can be made more robust using our benchmarks, available at https://github.com/ArtLabBocconi/RandCSPBench .
来源说明
当前只保存了官方论文摘要，不代表论文全文。请以原始来源为准。
本页只呈现已做哈希绑定的来源证据，不包含基于旧正文或缺失原文的扩展推断。