4 papers
CluCERT: Certifying LLM Robustness via Clustering-Guided Denoising Smoothing
Zixia Wang, Gaojie Jin, Jia Hu +1
The paper presents CluCERT, a framework that uses clustering-guided denoising smoothing to certify the robustness of large language models against adversarial synonym substitutions…
Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
Zixiang Wang, Mengjia Gong, Qiyu Sun +5
With the rapid advancement of artificial intelligence, multi-agent systems (MASs) are evolving from classical paradigms toward architectures built upon large foundation models (LFM…
The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
Haoyuan Xu, Chang Li, Xinyan Ma +12
Tool use enables large language models (LLMs) to access external information, invoke software systems, and act in digital environments beyond what can be solved from model paramete…
Safety of Embodied Navigation: A Survey
Zixia Wang, Jia Hu, Ronghui Mu
As large language models (LLMs) continue to advance and gain influence, the development of embodied AI has accelerated, drawing significant attention, particularly in navigation sc…