3 papers
cs.LG2026
Sparse Weight Decomposition for Efficient Circuit Extraction
Chuanhao Yan, Xuhan Huang, Yawen Duan +4
Dense pretrained transformers do not naturally expose interpretable units for circuit extraction. Existing approaches obtain such units by learning auxiliary sparse representations…
cs.CL2026
Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny
Chuanhao Yan, Fengdi Che, Xuhan Huang +12
Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant challenge: their verification proce…
cs.PL2026
VeriEquivBench: An Equivalence Score for Ground-Truth-Free Evaluation of Formally Verifiable Code
Lingfei Zeng, Fengdi Che, Xuhan Huang +4
Formal verification is the next frontier for ensuring the correctness of code generated by Large Language Models (LLMs). While methods that co-generate code and formal specificatio…