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

Showing 201225 of 423 papers

TitleStatusHype
Satisfiability and Synthesis Modulo Oracles0
Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement0
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models0
Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study0
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications0
Shedding Light in Task Decomposition in Program Synthesis: The Driving Force of the Synthesizer Model0
Solving Linear Algebra by Program Synthesis0
Solving Linear Algebra by Program Synthesis0
Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism0
Solving Probability and Statistics Problems by Program Synthesis0
Solving Probability and Statistics Problems by Program Synthesis0
Solving Visual Analogies Using Neural Algorithmic Reasoning0
SpreadsheetCoder: Formula Prediction from Semi-structured Context0
Statically Contextualizing Large Language Models with Typed Holes0
Synthesizing Datalog Programs Using Numerical Relaxation0
Structured Program Synthesis using LLMs: Results and Insights from the IPARC Challenge0
Summary - TerpreT: A Probabilistic Programming Language for Program Induction0
SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine0
Synthesizing Imperative Programs from Examples Guided by Static Analysis0
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning0
Synthesizing Programs with Continuous Optimization0
Synthesizing world models for bilevel planning0
Synthetic Datasets for Neural Program Synthesis0
System 2 Reasoning via Generality and Adaptation0
TerpreT: A Probabilistic Programming Language for Program Induction0
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Benchmark Results

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