8 citations · 8 across the 4 of their papers we have counts for
5 papers
When Non-Adversarial Noise Hurts Learning: Benchmarking Skill Self-Evolution in Agents
Tao Yu, Zhongtian Luo, Yuchen Long +19
Large language model agents increasingly improve by distilling experience into reusable skills, but errors in that experience can persist in learned rules and affect later tasks. E…
The Greatness of Science Cannot Be Planned: Agentic Auto-Research is Fuzz Testing
Yifeng He, Jicheng Wang, Yinzhe Zhao +3
Agentic auto-research is emerging, but most systems treat scientific discovery as goal-oriented optimization against a final benchmark. This paradigm rewards a sparse final verdict…
VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection
Kexing Ji, Jiachen Liu, Enze Hu +7
Recent advances in LLM-based vulnerability detection have shown promising results, while coding agents further extend this capability from isolated code snippets to complete reposi…
Towards Reliable C-to-Rust Translation with Rule-Guided Reasoning and Reinforcement Learning
Feng Luo, Jiachen Liu, Cuiyun Gao +2
The migration of legacy C programs to Rust has become an important direction for improving software memory safety while alleviating the high cost of manual rewriting. Leveraging la…
ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code
Jia Feng, Jiachen Liu, Cuiyun Gao +4
In recent years, the application of large language models (LLMs) to code-related tasks has gained significant attention. However, existing evaluation benchmarks often focus on limi…