11 papers
MINES: Explainable Anomaly Detection through Web API Invariant Inference
Wenjie Zhang, Yun Lin, Chun Fung Amos Kwok +5
Detecting the anomalies of web applications, important infrastructures for running modern companies and governments, is crucial for providing reliable web services. Many modern web…
Toward Functional and Non-Functional Evaluation of Application-Level Code Generation
Ruwei Pan, Yakun Zhang, Qingyuan Liang +4
Large language models (LLMs) have achieved strong performance on code generation. However, most prior evaluations focus on snippet-level outputs, such as function generation or rep…
Rethinking Video Generation Model for the Embodied World
Yufan Deng, Zilin Pan, Hongyu Zhang +6
Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and act…
CORVUS: Red-Teaming Hallucination Detectors via Internal Signal Camouflage in Large Language Models
Nay Myat Min, Long H. Pham, Hongyu Zhang +1
Single-pass hallucination detectors rely on internal telemetry (e.g., uncertainty, hidden-state geometry, and attention) of large language models, implicitly assuming hallucination…
Ahead of the Spread: Agent-Driven Virtual Propagation for Early Fake News Detection
Bincheng Gu, Min Gao, Junliang Yu +4
Early detection of fake news is critical for mitigating its rapid dissemination on social media, which can severely undermine public trust and social stability. Recent advancements…
LLMAID: Identifying AI Capabilities in Android Apps with LLMs
Pei Liu, Terry Zhuo, Jiawei Deng +7
Recent advancements in artificial intelligence (AI) and its widespread integration into mobile software applications have received significant attention, highlighting the growing p…