Liangcai Su

BARS

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

The past two decades have witnessed the rapid development of personalized recommendation techniques. Despite the significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized …

R1-Fuzz: Specializing Language Models for Textual Fuzzing via Reinforcement Learning

2025 · arXiv (Cornell University)

Fuzzing is effective for vulnerability discovery but struggles with complex targets such as compilers, interpreters, and database engines, which accept textual input that must satisfy intricate syntactic and semantic constraints. Although language models (LMs) have …