Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Hao-Yang Li et al.· Proceedings of the 32nd ACM...· 0 citations
Graph-based indexes have become the dominant approach to approximate nearest neighbor search (ANNS) over high-dimensional data and play a crucial role in real-world applications such as retrieval-augmented generation, recommendation systems, and vector databases. Despite extensive progress in static graph construction...
StructSynth is introduced, a framework that treats a dependency graph as a generation plan---determining the generation order, conditioning context, and scope of each black-box LLM call, and achieves state-of-the-art downstream utility and the best privacy-risk ranking among fourteen compared generators in low-data set...
Si-Yi Liu, Yujian Zheng, Haoyang Li et al.· 0 citations
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes CMI-Mem, a lightweight RL memory manager with a hybrid reward, which demonstrates improved transfer across memory-use scenarios, together with more efficient training and inference from the per-operation CMI signal.
Yubo Wang, Qiuyu Zhao, Zenghui Sun et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
