1 citations · 1 across the 6 of their papers we have counts for
6 papers
Symbolic-Neural Soft-Logic Reasoning: Towards Robust and Verifiable Thinking Chains via Cooperative Evolution
Rui Wang, Zeming Wei, Yihao Zhang +1
Large Language Models (LLMs) have demonstrated impressive progress in complex reasoning tasks, largely driven by the Chain-of-Thought (CoT) paradigm, which decomposes difficult pro…
Neural Theorem Proving for Verification Conditions: A Real-World Benchmark
Qiyuan Xu, Xiaokun Luan, Renxi Wang +5
Theorem proving is fundamental to program verification, where the automated proof of Verification Conditions (VCs) remains a primary bottleneck. Real-world program verification fre…
Psychometric-Based Evaluation for Theorem Proving with Large Language Models
Jianyu Zhang, Yongwang Zhao, Long Zhang +4
Large language models (LLMs) for formal theorem proving have become a prominent research focus. At present, the proving ability of these LLMs is mainly evaluated through proof pass…
The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap
Yedi Zhang, Yufan Cai, Xinyue Zuo +9
Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generat…
Generically Automating Separation Logic by Functors, Homomorphisms and Modules
Qiyuan Xu, David Sanan, Zhe Hou +3
Foundational verification considers the functional correctness of programming languages with formalized semantics and uses proof assistants (e.g., Coq, Isabelle) to certify proofs.…
Towards Large Language Model Aided Program Refinement
Yufan Cai, Zhe Hou, Xiaokun Luan +4
Program refinement involves correctness-preserving transformations from formal high-level specification statements into executable programs. Traditional verification tool support f…