activity
20212026
most citedSafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents

2 citations · 6 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2026

Conflicts Make Large Reasoning Models Vulnerable to Attacks

Honghao Liu, Chengjin Xu, Xuhui Jiang +5

Large Reasoning Models (LRMs) have achieved remarkable performance across diverse domains, yet their decision-making under conflicting objectives remains insufficiently understood.…

cs.CR2024★ 2 cited

SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents

Sheng Yin, Xianghe Pang, Yuanzhuo Ding +7

With the integration of large language models (LLMs), embodied agents have strong capabilities to understand and plan complicated natural language instructions. However, a foreseea…

cs.CV2024

Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models

Yuzhu Cai, Sheng Yin, Yuxi Wei +5

The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, th…

cs.CR2023★ 2 cited

Malicious Agent Detection for Robust Multi-Agent Collaborative Perception

Yangheng Zhao, Zhen Xiang, Sheng Yin +3

Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. Ho…

cs.LG2022

Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning

Chenxin Xu, Yuxi Wei, Bohan Tang +3

Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly c…

cs.LG2021★ 2 cited

Trap of Feature Diversity in the Learning of MLPs

Dongrui Liu, Shaobo Wang, Jie Ren +4

In this paper, we focus on a typical two-phase phenomenon in the learning of multi-layer perceptrons (MLPs), and we aim to explain the reason for the decrease of feature diversity…