25 citations · 37 across the 8 of their papers we have counts for
3 papers · 1 filter
Cobblestone: A Divide-and-Conquer Approach for Automating Formal Verification
Saketh Ram Kasibatla, Arpan Agarwal, Yuriy Brun +3
Formal verification using proof assistants, such as Coq, is an effective way of improving software quality, but requires significant effort and expertise. Machine learning can auto…
CoqPyt: Proof Navigation in Python in the Era of LLMs
Pedro Carrott, Nuno Saavedra, Kyle Thompson +3
Proof assistants enable users to develop machine-checked proofs regarding software-related properties. Unfortunately, the interactive nature of these proof assistants imposes most…
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving
Chenyang An, Zhibo Chen, Qihao Ye +6
Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof s…