联邦微调藏风险！图表示学习竟能放大模型操纵攻击，LLM安全防线告急。前沿研究不可错过。
arXiv:2605.07961v2 Announce Type: replace Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapt…
把纵向医学影像建模成图，用 AI 预判治疗反应，为个性化医疗带来全新可能。
arXiv:2607.04912v1 Announce Type: cross Abstract: In patients with breast cancer, pathological complete response (pCR) has been established as a clini…
针对任意图结构学习表示的通用框架，原创性高，已被ICML 2026接收。
arXiv:2512.11561v2 Announce Type: replace Abstract: Generalizing pretrained models to unseen datasets without retraining is a central challenge toward…
跳出传统图思维，用超图语言表达复杂关系，LLM建模迎来新范式。
arXiv:2605.21858v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong potential in modeling relational structures. H…
在图的分数傅里叶变换域中嵌入，突破传统谱嵌入的表达瓶颈，捕获更全面的图结构特征
arXiv:2508.02383v2 Announce Type: replace Abstract: Spectral graph embedding plays a critical role in graph representation learning by generating low-…