5 papers
REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs
Keer Lu, Liwei Chen, Guoqing Jiang +3
Large Language Models (LLMs) are increasingly expected to interact with users over long time horizons. However, due to their finite context window, LLMs cannot retain all past inte…
DREAM: Dynamic Red-teaming across Environments for AI Models
Liming Lu, Xiang Gu, Junyu Huang +5
Large Language Models (LLMs) are increasingly used in agentic systems, where their interactions with diverse tools and environments create complex, multi-stage safety challenges. H…
CIARD: Cyclic Iterative Adversarial Robustness Distillation
Liming Lu, Shuchao Pang, Xu Zheng +4
Adversarial robustness distillation (ARD) aims to transfer both performance and robustness from teacher model to lightweight student model, enabling resilient performance on resour…
EpiCoDe: Boosting Model Performance Beyond Training with Extrapolation and Contrastive Decoding
Mingxu Tao, Jie Hu, Mingchuan Yang +3
The remarkable performance of Large language models (LLMs) relies heavily on the availability of abundant high-quality training data. However, the high cost of acquiring annotated…
Cross-Domain Sequential Recommendation via Neural Process
Haipeng Li, Jiangxia Cao, Yiwen Gao +2
Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user interest modeling, which aims at utilizing a single model to predict the next items for differen…