8 papers
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
Bin Wang, Fan Wang, Pingping Wang +5
In this paper, we introduce \textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents with closed-loop interaction.…
FadeMem: Biologically-Inspired Forgetting for Efficient Agent Memory
Lei Wei, Xiao Peng, Xu Dong +2
Large language models deployed as autonomous agents face critical memory limitations, lacking selective forgetting mechanisms that lead to either catastrophic forgetting at context…
Think-Augmented Function Calling: Improving LLM Parameter Accuracy Through Embedded Reasoning
Lei Wei, Xiao Peng, Jinpeng Ou +1
Large language models (LLMs) have demonstrated remarkable capabilities in function calling for autonomous agents, yet current mechanisms lack explicit reasoning transparency during…
DiffBench Meets DiffAgent: End-to-End LLM-Driven Diffusion Acceleration Code Generation
Jiajun jiao, Haowei Zhu, Puyuan Yang +8
Diffusion models have achieved remarkable success in image and video generation. However, their inherently multiple step inference process imposes substantial computational overhea…
VSA:Visual-Structural Alignment for UI-to-Code
Xian Wu, Ming Zhang, Zhiyu Fang +4
The automation of user interface development has the potential to accelerate software delivery by mitigating intensive manual implementation. Despite the advancements in Large Mult…
Reflection-Driven Control for Trustworthy Code Agents
Bin Wang, Jiazheng Quan, Xingrui Yu +3
Contemporary large language model (LLM) agents are remarkably capable, but they still lack reliable safety controls and can produce unconstrained, unpredictable, and even actively…