1 citations · 1 across the 12 of their papers we have counts for
14 papers
TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation
Jingyi Zheng, Yule Liu, Zifan Peng +4
Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore…
Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
Mingchen Li, Meikang Qiu, Zifan Peng +4
Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair…
What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media
Zifan Peng, Yini Huang, Aiwen Lu +7
Public social media posts can reveal private information through weak cues scattered across text, images, or metadata. Such leakage is often cumulative and cross-post: cues that ap…
On the Generation and Mitigation of Harmful Geometry in Image-to-3D Models
Yule Liu, Yilong Yang, Jiale Teng +11
Recent advances in image-to-3D models have significantly improved the fidelity and accessibility of 3D content creation. Such a powerful reconstruction capability that enables crea…
CHASM: Unveiling Covert Advertisements on Chinese Social Media
Jingyi Zheng, Tianyi Hu, Yule Liu +5
Current benchmarks for evaluating large language models (LLMs) in social media moderation completely overlook a serious threat: covert advertisements, which disguise themselves as…
"What Did It Actually Do?": Understanding Risk Awareness and Traceability for Computer-Use Agents
Zifan Peng, Mingchen Li
Personalized computer-use agents are rapidly moving from expert communities into mainstream use. Unlike conventional chatbots, these systems can install skills, invoke tools, acces…
TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
Zifan Peng, Jingyi Zheng, Yule Liu +8
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespre…
Auditing Data Membership in Reinforcement Learning With Verifiable Rewards
Yule Liu, Heyi Zhang, Jinyi Zheng +6
Reinforcement Learning with Verifiable Rewards (RLVR) has become a core training stage in recent large language models (LLMs). Its reliance on non-public, high-value prompt sets ra…
ZPD-SCA: Unveiling the Blind Spots of LLMs in Assessing Students' Cognitive Abilities
Wenhan Dong, Zhen Sun, Yuemeng Zhao +9
Large language models (LLMs) have demonstrated potential in educational applications, yet their capacity to accurately assess the cognitive alignment of reading materials with stud…
GasAgent: A Multi-Agent Framework for Automated Gas Optimization in Smart Contracts
Jingyi Zheng, Zifan Peng, Yule Liu +4
Smart contracts are trustworthy, immutable, and automatically executed programs on the blockchain. Their execution requires the Gas mechanism to ensure efficiency and fairness. How…
JALMBench: Benchmarking Jailbreak Vulnerabilities in Audio Language Models
Zifan Peng, Yule Liu, Zhen Sun +9
Large Audio Language Models (LALMs) have made significant progress. While increasingly deployed in real-world applications, LALMs face growing safety risks from jailbreak attacks t…
"I Can See Forever!": Evaluating Real-time VideoLLMs for Assisting Individuals with Visual Impairments
Ziyi Zhang, Zhen Sun, Zongmin Zhang +6
The visually impaired population faces significant challenges in daily activities. While prior works employ vision language models for assistance, most focus on static content and…
Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applications
Wenhan Dong, Yuemeng Zhao, Zhen Sun +10
As large language models (LLMs) are increasingly used in human-centered tasks, assessing their psychological traits is crucial for understanding their social impact and ensuring tr…
Thought Manipulation: External Thought Can Be Efficient for Large Reasoning Models
Yule Liu, Jingyi Zheng, Zhen Sun +6
Recent advancements in large reasoning models (LRMs) have demonstrated the effectiveness of scaling test-time computation to enhance reasoning capabilities on various tasks. Howeve…