4 papers
Rosetta Memory: Adaptive Memory for Cross-LLM Agents
Hao Yang, Shiqi Shen, Haoxuan Li +3
Memory is the key component for transforming a stateless LLM into a persistent, evolving agent through experience accumulation, long-horizon planning, and continual self-improvemen…
Federated Multi-Objective Learning with Controlled Pareto Frontiers
Jiansheng Rao, Jiayi Li, Zhizhi Gong +2
Federated learning (FL) is a widely adopted paradigm for privacy-preserving model training, but FedAvg optimise for the majority while under-serving minority clients. Existing meth…
Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization
Wenkai Yang, Shiqi Shen, Guangyao Shen +5
Superalignment, where humans act as weak supervisors for superhuman models, has become a crucial problem with the rapid development of Large Language Models (LLMs). Recent work has…
SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection
Zhihao Ding, Jieming Shi, Shiqi Shen +4
Graph-level representation learning is important in a wide range of applications. Existing graph-level models are generally built on i.i.d. assumption for both training and testing…