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 251–300 of 423 papers

TitleStatusHype
QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks—0
Recent Developments in Program Synthesis with Evolutionary Algorithms—0
Refinement Type Directed Search for Meta-Interpretive-Learning of Higher-Order Logic Programs—0
Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis—0
Report of the Workshop on Program Synthesis for Scientific Computing—0
Representing Partial Programs with Blended Abstract Semantics—0
Research Vision: Multi-Agent Path Planning for Cops And Robbers Via Reactive Synthesis—0
RLSF: Reinforcement Learning via Symbolic Feedback—0
Runtime phylogenetic analysis enables extreme subsampling for test-based problems—0
Sampling for Bayesian Program Learning—0
SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions—0
Satisfiability and Synthesis Modulo Oracles—0
Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement—0
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models—0
Semi-supervised Learning From Demonstration Through Program Synthesis: An Inspection Robot Case Study—0
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications—0
Shedding Light in Task Decomposition in Program Synthesis: The Driving Force of the Synthesizer Model—0
Solving Linear Algebra by Program Synthesis—0
Solving Linear Algebra by Program Synthesis—0
Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism—0
Solving Probability and Statistics Problems by Program Synthesis—0
Solving Probability and Statistics Problems by Program Synthesis—0
Solving Visual Analogies Using Neural Algorithmic Reasoning—0
Statically Contextualizing Large Language Models with Typed Holes—0
Synthesizing Datalog Programs Using Numerical Relaxation—0
Structured Program Synthesis using LLMs: Results and Insights from the IPARC Challenge—0
Summary - TerpreT: A Probabilistic Programming Language for Program Induction—0
SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine—0
Synthesizing Imperative Programs from Examples Guided by Static Analysis—0
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning—0
Synthesizing Programs with Continuous Optimization—0
Synthesizing world models for bilevel planning—0
Synthetic Datasets for Neural Program Synthesis—0
System 2 Reasoning via Generality and Adaptation—0
TerpreT: A Probabilistic Programming Language for Program Induction—0
The Program Testing Ability of Large Language Models for Code—0
Correct and Optimal: the Regular Expression Inference Challenge—0
Time Series Structure Discovery via Probabilistic Program Synthesis—0
Solving the Families In the Wild Kinship Verification Challenge by Program Synthesis—0
Toward Code Generation: A Survey and Lessons from Semantic Parsing—0
Towards Mixed Optimization for Reinforcement Learning with Program Synthesis—0
Towards Neural-Guided Program Synthesis for Linear Temporal Logic Specifications—0
Towards Physical, Imperceptible Adversarial Attacks via Adversarial Programs—0
Towards Synthesizing Complex Programs from Input-Output Examples—0
Coarse-Tuning Models of Code with Reinforcement Learning Feedback—0
Type-driven Neural Programming by Example—0
Unsupervised Learning by Program Synthesis—0
Unsupervised Program Synthesis for Images By Sampling Without Replacement—0
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems—0
(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis—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