11 citations · 41 across the 28 of their papers we have counts for
32 papers
LIBERO-Pro2.0: A Benchmark for Evaluating the Robustness of Embodied Foundation Models
Xueyang Zhou, Yangming Xu, Qianjiang Li +10
LIBERO has emerged as a widely adopted benchmark for evaluating robotic manipulation policies. However, strong performance on LIBERO does not necessarily indicate robust embodied c…
Enabling Extensible Embodied Capabilities with Tools
Xueyang Zhou, Zijia Wang, Qianjiang Li +7
Most existing embodied intelligence methods formulate perception, reasoning, planning, and control within a unified parameterized policy. Yet these capabilities are inherently hier…
EmbodiedClaw: Conversational Workflow Execution for Embodied AI Development
Xueyang Zhou, Yihan Sun, Xijie Gong +4
Embodied AI research is increasingly moving beyond single-task, single-environment policy learning toward multi-task, multi-scene, and multi-model settings. This shift substantiall…
BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning
Guiyao Tie, Jiawen Shi, Pan Zhou +1
Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their execution logic. This creates a su…
Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs
Guiyao Tie, Zenghui Yuan, Zeli Zhao +11
Self-correction of large language models (LLMs) emerges as a critical component for enhancing their reasoning performance. Although various self-correction methods have been propos…
A Survey of AI Scientists
Guiyao Tie, Pan Zhou, Lichao Sun
Artificial intelligence is undergoing a profound transition from a computational instrument to an autonomous originator of scientific knowledge. This emerging paradigm, the AI scie…
LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
Xueyang Zhou, Yangming Xu, Guiyao Tie +5
LIBERO has emerged as a widely adopted benchmark for evaluating Vision-Language-Action (VLA) models; however, its current training and evaluation settings are problematic, often le…
MMLU-Reason: Benchmarking Multi-Task Multi-modal Language Understanding and Reasoning
Guiyao Tie, Xueyang Zhou, Tianhe Gu +7
Recent advances in Multi-Modal Large Language Models (MLLMs) have enabled unified processing of language, vision, and structured inputs, opening the door to complex tasks such as l…
Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents
Zhejian Yang, Yongchao Chen, Xueyang Zhou +8
Long-horizon robotic manipulation poses significant challenges for autonomous systems, requiring extended reasoning, precise execution, and robust error recovery across complex seq…
Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models
Zenghui Yuan, Yangming Xu, Jiawen Shi +2
Model merging for Large Language Models (LLMs) directly fuses the parameters of different models finetuned on various tasks, creating a unified model for multi-domain tasks. Howeve…
BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization
Xueyang Zhou, Guiyao Tie, Guowen Zhang +3
Vision-Language-Action (VLA) models have advanced robotic control by enabling end-to-end decision-making directly from multimodal inputs. However, their tightly coupled architectur…
SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator
Xueyang Zhou, Weidong Wang, Lin Lu +7
Large Language Model (LLM)-based agents are increasingly deployed in real-world applications such as "digital assistants, autonomous customer service, and decision-support systems"…
Prompt Injection Attack to Tool Selection in LLM Agents
Jiawen Shi, Zenghui Yuan, Guiyao Tie +3
Tool selection is a key component of LLM agents. A popular approach follows a two-step process - \emph{retrieval} and \emph{selection} - to pick the most appropriate tool from a to…
BadToken: Token-level Backdoor Attacks to Multi-modal Large Language Models
Zenghui Yuan, Jiawen Shi, Pan Zhou +2
Multi-modal large language models (MLLMs) extend large language models (LLMs) to process multi-modal information, enabling them to generate responses to image-text inputs. MLLMs ha…
Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generation
Yinuo Liu, Zenghui Yuan, Guiyao Tie +4
Multimodal retrieval-augmented generation (RAG) enhances the visual reasoning capability of vision-language models (VLMs) by dynamically accessing information from external knowled…
Exploring the Necessity of Reasoning in LLM-based Agent Scenarios
Xueyang Zhou, Guiyao Tie, Guowen Zhang +7
The rise of Large Reasoning Models (LRMs) signifies a paradigm shift toward advanced computational reasoning. Yet, this progress disrupts traditional agent frameworks, traditionall…
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
Yixin Liu, Lie Lu, Jihui Jin +2
The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data provenance, and the spread of misinfor…
Empirical Perturbation Analysis of Linear System Solvers from a Data Poisoning Perspective
Yixin Liu, Arielle Carr, Lichao Sun
The perturbation analysis of linear solvers applied to systems arising broadly in machine learning settings -- for instance, when using linear regression models -- establishes an i…
Can Large Language Models Automatically Jailbreak GPT-4V?
Yuanwei Wu, Yue Huang, Yixin Liu +3
GPT-4V has attracted considerable attention due to its extraordinary capacity for integrating and processing multimodal information. At the same time, its ability of face recogniti…
Virtual Context: Enhancing Jailbreak Attacks with Special Token Injection
Yuqi Zhou, Lin Lu, Hanchi Sun +2
Jailbreak attacks on large language models (LLMs) involve inducing these models to generate harmful content that violates ethics or laws, posing a significant threat to LLM securit…
Self-Cognition in Large Language Models: An Exploratory Study
Dongping Chen, Jiawen Shi, Yao Wan +3
While Large Language Models (LLMs) have achieved remarkable success across various applications, they also raise concerns regarding self-cognition. In this paper, we perform a pion…
GUI-World: A Video Benchmark and Dataset for Multimodal GUI-oriented Understanding
Dongping Chen, Yue Huang, Siyuan Wu +17
Recently, Multimodal Large Language Models (MLLMs) have been used as agents to control keyboard and mouse inputs by directly perceiving the Graphical User Interface (GUI) and gener…
Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical World
Wen Yin, Jian Lou, Pan Zhou +5
Backdoor attacks have been well-studied in visible light object detection (VLOD) in recent years. However, VLOD can not effectively work in dark and temperature-sensitive scenarios…
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code
Batu Guan, Yao Wan, Zhangqian Bi +4
Large Language Models (LLMs) have achieved remarkable progress in code generation. It now becomes crucial to identify whether the code is AI-generated and to determine the specific…