Search Results for author:
Found 7 papers, 3 papers with code
Finding Inductive Loop Invariants using Large Language Models
Finally, we explore the effectiveness of using an efficient combination of a symbolic tool and an LLM on our dataset and compare it against a purely symbolic baseline.
Synthesis with Explicit Dependencies
In QBF, an existentially quantified variable is allowed to depend on all universally quantified variables in its scope.
Engineering an Efficient Boolean Functional Synthesis Engine
In this paper, we propose four algorithmic improvements for a data-driven framework for functional synthesis: using a dependency-driven multi-classifier to learn candidate function, extracting uniquely defined functions by interpolation, variables retention, and using lexicographic MaxSAT to repair candidates.
Program Synthesis as Dependency Quantified Formula Modulo Theory
Over the past decade, syntax-guided synthesis (SyGuS) has emerged as a dominant approach for program synthesis where in addition to the specification $\varphi$, the end-user also specifies a grammar $L$ to aid the underlying synthesis engine.
Learning Differentially Private Mechanisms
We demonstrate that our approach is able to learn foundational algorithms from the differential privacy literature and significantly outperforms natural program synthesis baselines.
Phase Transition Behavior in Knowledge Compilation
The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements of modern SAT solvers.
Manthan: A Data Driven Approach for Boolean Function Synthesis
On an extensive and rigorous evaluation over 609 benchmarks, we demonstrate that Manthan significantly improves upon the current state of the art, solving 356 benchmarks in comparison to 280, which is the most solved by a state of the art technique; thereby, we demonstrate an increase of 76 benchmarks over the current state of the art.
