most citedPersonal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

31 citations · 31 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Joint Agent Memory and Exploration Learning via Novelty Signals

Shizuo Tian, Xiaohong Weng, Rui Kong +9

In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploration requires memory, but retai…

cs.AI2026

AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization

Qiyang Li, Rui Kong, Yuchen Li +5

The integration of dynamic, sparse structures like Mixture-of-Experts (MoE) with parameter-efficient adapters (e.g., LoRA) is a powerful technique for enhancing Large Language Mode…

cs.CV2024

Empower Vision Applications with LoRA LMM

Liang Mi, Weijun Wang, Wenming Tu +10

Large Multimodal Models (LMMs) have shown significant progress in various complex vision tasks with the solid linguistic and reasoning capacity inherited from large language models…

cs.AI2024

LoRA-Switch: Boosting the Efficiency of Dynamic LLM Adapters via System-Algorithm Co-design

Rui Kong, Qiyang Li, Xinyu Fang +7

Recent literature has found that an effective method to customize or further improve large language models (LLMs) is to add dynamic adapters, such as low-rank adapters (LoRA) with…

cs.HC2024★ 31 cited

Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security

Yuanchun Li, Hao Wen, Weijun Wang +22

Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming…