1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.AI2026
SAT-ACT: Learning CDCL Branching from Outcome-Derived Action Preferences
Zhenchao Sun, Chongyang Tao, Ping Lu +3
Learning-based approaches have increasingly been explored for SAT solving, with particular attention to improving branching decisions in conflict-driven clause learning (CDCL) solv…
cs.LG2026
Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
Zhenchao Sun, Shuai Ma, Ping Lu +1
Graph neural networks have been widely used in Boolean satisfiability (SAT) tasks to learn structural information from SAT formulas. The goal of these studies is to solve SAT insta…
cs.AI2025★ 1 cited
Improving Subgraph Matching by Combining Algorithms and Graph Neural Networks
Shuyang Guo, Wenjin Xie, Ping Lu +5
Homomorphism is a key mapping technique between graphs that preserves their structure. Given a graph and a pattern, the subgraph homomorphism problem involves finding a mapping fro…