1 citations · 1 across the 6 of their papers we have counts for
7 papers
WebUIProof: Benchmarking WebUI Code Generators with UI-Agent Execution Harness
Yun-Yun Tsai, Yuning Mao, Shiqi Wang +2
Evaluating WebUI code generation at scale is difficult: outputs may compile and look plausible yet fail under user interaction, and prior benchmarks largely rely on free-form promp…
ARQ: Agentic CodeQL Query Refinement for C/C++ Vulnerability Detection
Chunyi Wang, Yunfei Ke, Junfeng Yang +2
Static analyzers have been widely adopted for vulnerability detection in C/C++ programs. Query-based static analyzers (e.g., CodeQL) encode vulnerable code patterns in detection qu…
Reasoning under Vision: Understanding Visual-Spatial Cognition in Vision-Language Models for CAPTCHA
Python Song, Luke Tenyi Chang, Yun-Yun Tsai +2
CAPTCHA, originally designed to distinguish humans from robots, has evolved into a real-world benchmark for assessing the spatial reasoning capabilities of vision-language models.…
RAFP: Identifying LLM Lineages via Rare-Region Fingerprints
Yun-Yun Tsai, Jia Hao Liang, Chuan Guo +2
Large language models (LLMs) are increasingly released under restricted licenses, creating a growing need for robust model ownership verification. Existing fingerprinting methods a…
REVEAL: Robust Evolution of Vision-Language Models for Explainable AI-Video Detection
Qingyuan Liu, Yun-Yun Tsai, Ruijian Zha +4
The rapid advancement of AI-generated video poses challenges to digital authenticity and security. Current detection methods, often trained on specific datasets, struggle with the…
Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos
Qingyuan Liu, Pengyuan Shi, Yun-Yun Tsai +2
The impressive achievements of generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat De…
GDA: Generalized Diffusion for Robust Test-time Adaptation
Yun-Yun Tsai, Fu-Chen Chen, Albert Y. C. Chen +4
Machine learning models struggle with generalization when encountering out-of-distribution (OOD) samples with unexpected distribution shifts. For vision tasks, recent studies have…