18 citations · 18 across the 12 of their papers we have counts for
14 papers
Diffusion Language Models for Perplexity-Aware Black-Box Attacks
Ali Al-Lawati, Elsayed Eshra, Dongwon Lee +1
\emph{Continuous-space} attacks find prompts that trigger malicious behaviors, such as overthinking, in black-box LLMs by sampling or optimizing over a low-dimensional Gaussian lat…
Quantifying Behavioral Tails in Black-Box Language Models
Elsayed Eshra, Ali Al-Lawati, Dongwon Lee +1
We introduce RareTrap, a framework for estimating the probability of severe behaviors in black box large language models (LLMs). A key challenge for probability estimation is defin…
PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement
Xianren Zhang, Delvin Ce Zhang, Dongwon Lee +1
Multimodal Large Language Models (MLLMs) have shown strong capabilities, but they may memorize private information from web data, raising privacy concerns. Machine unlearning offer…
When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems
Su Wang, Pin Qian, Yihang Chen +6
LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set. We study a core safety problem in agentic AI systems: whether indivi…
Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
Minhua Lin, Juncheng Wu, Zijun Wang +14
LLM agents are increasingly deployed as systems built around editable external harnesses, including prompts, skills, memories and tools, that shape task execution without changing…
Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue
Ali Al-Lawati, Nafis Tripto, Abolfazl Ansari +3
The emergence of multi-agent systems introduces novel moderation challenges that extend beyond content filtering. Agents with malicious intent may contribute harmful content that a…
GraphSkill: Documentation-Guided Hierarchical Retrieval-Augmented Coding for Complex Graph Reasoning
Fali Wang, Chenglin Weng, Xianren Zhang +3
The growing demand for automated graph algorithm reasoning has attracted increasing attention in the large language model (LLM) community. Recent LLM-based graph reasoning methods…
Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems
Shuhua Yang, Jiahao Zhang, Yilong Wang +2
Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning. While prior work shows that Graph…
A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains
Xianren Zhang, Shreyas Prasad, Di Wang +4
Web agents have shown great promise in performing many tasks on ecommerce website. To assess their capabilities, several benchmarks have been introduced. However, current benchmark…
GPR: Empowering Generation with Graph-Pretrained Retriever
Xiaochen Wang, Zongyu Wu, Yuan Zhong +3
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, l…
SUA: Stealthy Multimodal Large Language Model Unlearning Attack
Xianren Zhang, Hui Liu, Delvin Ce Zhang +4
Multimodal Large Language Models (MLLMs) trained on massive data may memorize sensitive personal information and photos, posing serious privacy risks. To mitigate this, MLLM unlear…
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
Fali Wang, Zhiwei Zhang, Xianren Zhang +11
Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their profi…
Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?
Xianren Zhang, Xianfeng Tang, Hui Liu +4
Recent studies show LLMs struggle with complex instructions involving multiple constraints (e.g., length, format, sentiment). Existing works address this issue by fine-tuning, whic…
Comprehensive Attribution: Inherently Explainable Vision Model with Feature Detector
Xianren Zhang, Dongwon Lee, Suhang Wang
As deep vision models' popularity rapidly increases, there is a growing emphasis on explanations for model predictions. The inherently explainable attribution method aims to enhanc…