16 citations · 19 across the 8 of their papers we have counts for
9 papers
Position: Agentic Evolution is the Path to Evolving LLMs
Minhua Lin, Hanqing Lu, Zhan Shi +11
As Large Language Models (LLMs) move from curated training sets into open-ended real-world environments, a fundamental limitation emerges: static training cannot keep pace with con…
Evaluating Long-Horizon Memory for Multi-Party Collaborative Dialogues
Chuanrui Hu, Tong Li, Xingze Gao +8
Long-term conversational memory in practical LLM applications is inherently collaborative: information is produced by multiple participants, scattered across groups and channels, r…
Phase Transition for Budgeted Multi-Agent Synergy
Bang Liu, Linglong Kong, Jian Pei
Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse. We develop a minimal and calibratable theory that predi…
Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications
Guangxin Su, Hanchen Wang, Jianwei Wang +3
Large Language Models (LLMs) have achieved remarkable success in natural language processing through strong semantic understanding and generation. However, their black-box nature l…
On Membership Inference Attacks in Knowledge Distillation
Ziyao Cui, Minxing Zhang, Jian Pei
Large language models (LLMs) are trained on massive corpora that may contain sensitive information, creating privacy risks under membership inference attacks (MIAs). Knowledge dist…
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Bang Liu, Xinfeng Li, Jiayi Zhang +45
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods
Roy Xie, Junlin Wang, Ruomin Huang +5
The rapid scaling of large language models (LLMs) has raised concerns about the transparency and fair use of the data used in their pretraining. Detecting such content is challengi…
Linear-Time Graph Neural Networks for Scalable Recommendations
Jiahao Zhang, Rui Xue, Wenqi Fan +4
In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' futu…