2 citations · 3 across the 4 of their papers we have counts for
4 papers
LLM-Guided Quantified SMT Solving over Uninterpreted Functions
Kunhang Lv, Yuhang Dong, Rui Han +3
Quantified formulas with Uninterpreted Functions (UFs) over non-linear real arithmetic pose fundamental challenges for Satisfiability Modulo Theories (SMT) solving. Traditional qua…
ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Weichun Shi, Minghao Liu, Wanting Zhang +4
Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving…
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
Minghao Liu, Fuqi Jia, Pei Huang +5
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability…
ε-weakened Robustness of Deep Neural Networks
Pei Huang, Yuting Yang, Minghao Liu +3
This paper introduces a notation of -weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs). Unlike the conventional robustness…