Mixsatgen: Learning graph mixing for sat instance generation
The Boolean satisfiability problem (SAT) stands as a canonical NP-complete task. In
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …
particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT …
Autosat: Automatically optimize sat solvers via large language models
Heuristics are crucial in SAT solvers, but no heuristic rules are suitable for all SAT problems.
Therefore, it is helpful to refine specific heuristics for specific problems. In this context, we …
Therefore, it is helpful to refine specific heuristics for specific problems. In this context, we …
Learning Plaintext-Ciphertext Cryptographic Problems via ANF-based SAT Instance Representation
X Zheng, Y Li, C Fan, H Wu, X Song… - The Thirty-eighth Annual …, 2024 - openreview.net
Cryptographic problems, operating within binary variable spaces, can be routinely
transformed into Boolean Satisfiability (SAT) problems regarding specific cryptographic …
transformed into Boolean Satisfiability (SAT) problems regarding specific cryptographic …
Neuroselect: Learning to select clauses in sat solvers
Modern SAT solvers depend on conflict-driven clause learning to avoid recurring conflicts.
Deleting less valuable learned clauses is a crucial component of modern SAT solvers to …
Deleting less valuable learned clauses is a crucial component of modern SAT solvers to …