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 126150 of 423 papers

TitleStatusHype
DeepCoder: Learning to Write ProgramsCode0
Memory Augmented Policy Optimization for Program Synthesis and Semantic ParsingCode0
Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural RepresentationsCode0
DALex: Lexicase-like Selection via Diverse AggregationCode0
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingCode0
Making sense of sensory inputCode0
Analysis of Evolutionary Program Synthesis for Card GamesCode0
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior AnalysisCode0
LTL learning on GPUsCode0
Learning to Infer Program SketchesCode0
Learning to Execute Programs with Instruction Pointer Attention Graph Neural NetworksCode0
Learning to Infer Graphics Programs from Hand-Drawn ImagesCode0
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST RepresentationCode0
ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)Code0
Learning logic programs by combining programsCode0
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Learning to Reason via Program Generation, Emulation, and SearchCode0
Amortizing Pragmatic Program Synthesis with RankingsCode0
Learning logic programs by discovering higher-order abstractionsCode0
Autoencoders as Tools for Program SynthesisCode0
Learning logic programs by discovering where not to searchCode0
Coffee: Boost Your Code LLMs by Fixing Bugs with FeedbackCode0
Large Language Models Synergize with Automated Machine LearningCode0
Generating Programmatic Referring Expressions via Program SynthesisCode0
Generating Pragmatic Examples to Train Neural Program SynthesizersCode0
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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