3 papers
cs.SE2026
Demystifying the Silence of Correctness Bugs in PyTorch Compiler
Meiziniu Li, Dongze Li, Jianmeng Liu +1
Performance optimization of AI infrastructure is key to the fast adoption of large language models (LLMs). The PyTorch compiler (torch.compile), a core optimization tool for deep l…
cs.AI2025
From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs
Jialun Cao, Yaojie Lu, Meiziniu Li +9
The research in AI-based formal mathematical reasoning has shown an unstoppable growth trend. These studies have excelled in mathematical competitions like IMO and have made signif…
cs.SE2025
Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries
Meiziniu Li, Dongze Li, Jianmeng Liu +3
Differential testing offers a promising strategy to alleviate the test oracle problem by comparing the test results between alternative implementations. However, existing different…