SOTAVerified

Program Synthesis

Program synthesis is the process of automatically generating a program or code snippet that satisfies a given specification or set of requirements. This can include generating code from a formal specification, a natural language description, or example inputs and outputs. The primary goal of program synthesis is to minimize human intervention in the coding process, reduce errors, and improve productivity.

Program synthesis often involves the use of advanced algorithms, artificial intelligence, and machine learning techniques to search the space of possible programs that meet the given constraints. This process can be guided by a variety of techniques, such as constraint solving, symbolic execution, and genetic algorithms.

Papers

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Showing 126–150 of 423 papers

TitleStatusHype
Down-Sampled Epsilon-Lexicase Selection for Real-World Symbolic Regression Problems—0
Diffusion On Syntax Trees For Program Synthesis—0
An Empirical Comparison of Cost Functions in Inductive Logic Programming—0
Adversarial Robustness of Program Synthesis Models—0
Hierarchical Neural Program Synthesis—0
Differentiable Synthesis of Program Architectures—0
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion—0
A Neurodiversity-Inspired Solver for the Abstraction \& Reasoning Corpus (ARC) Using Visual Imagery and Program Synthesis—0
Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis—0
DeQompile: quantum circuit decompilation using genetic programming for explainable quantum architecture search—0
Bayesian causal inference via probabilistic program synthesis—0
h4rm3l: A language for Composable Jailbreak Attack Synthesis—0
Demonstration of CORNET: A System For Learning Spreadsheet Formatting Rules By Example—0
BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming—0
Untangling the Effects of Down-Sampling and Selection in Genetic Programming—0
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness—0
AutumnSynth: Synthesis of Reactive Programs with Structured Latent State—0
HOTGP -- Higher-Order Typed Genetic Programming—0
AutoTSG: Learning and Synthesis for Incident Troubleshooting—0
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing—0
Creating Synthetic Datasets via Evolution for Neural Program Synthesis—0
CounterExample Guided Neural Synthesis—0
CORNET: Learning Table Formatting Rules By Example—0
CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs—0
Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10038.5—Unverified
2Multiclass localizerSuccess rate @budget 10034.2—Unverified
#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10057—Unverified
2Multiclass localizerSuccess rate @budget 10053.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CodeTrans-MT-TF-SmallAccuracy90.31—Unverified