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 351–400 of 423 papers

TitleStatusHype
Learning logic programs by combining programsCode0
Amortizing Pragmatic Program Synthesis with RankingsCode0
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning EnvironmentsCode0
Generating Programmatic Referring Expressions via Program SynthesisCode0
Program Synthesis and Semantic Parsing with Learned Code IdiomsCode0
Learning MDL logic programs from noisy dataCode0
Program Synthesis as Dependency Quantified Formula Modulo TheoryCode0
SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life ApplicationsCode0
Program Synthesis Dialog Agents for Interactive Decision-MakingCode0
Generating Pragmatic Examples to Train Neural Program SynthesizersCode0
DeepCoder: Learning to Write ProgramsCode0
FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem ProvingCode0
LTL learning on GPUsCode0
Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning TestCode0
Making sense of sensory inputCode0
Learning logic programs through divide, constrain, and conquerCode0
Learning logic programs by discovering where not to searchCode0
Analysis of Evolutionary Program Synthesis for Card GamesCode0
Coffee: Boost Your Code LLMs by Fixing Bugs with FeedbackCode0
MathDSL: A Domain-Specific Language for Concise Mathematical Solutions Via Program SynthesisCode0
Tag-based regulation of modules in genetic programming improves context-dependent problem solvingCode0
Memory Augmented Policy Optimization for Program Synthesis and Semantic ParsingCode0
From Solution Synthesis to Student Attempt Synthesis for Block-Based Visual Programming TasksCode0
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence GenerationCode0
ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and RetrievalCode0
The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase SelectionCode0
SPoC: Search-based Pseudocode to CodeCode0
P-Tree ProgrammingCode0
WatChat: Explaining perplexing programs by debugging mental modelsCode0
Learning logic programs by discovering higher-order abstractionsCode0
DALex: Lexicase-like Selection via Diverse AggregationCode0
NAPS: Natural Program Synthesis DatasetCode0
Recent Advances in Neural Program SynthesisCode0
Transductively Informed Inductive Program SynthesisCode0
Mapping Natural-language Problems to Formal-language Solutions Using Structured Neural RepresentationsCode0
Stepping Stones to Inductive Synthesis of Low-Level Looping ProgramsCode0
Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwareCode0
Neural Guided Constraint Logic Programming for Program SynthesisCode0
BF++: a language for general-purpose program synthesisCode0
Relational decomposition for program synthesisCode0
Exploring Data Augmentation for Code Generation TasksCode0
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior AnalysisCode0
Transformer-based Program Synthesis for Low-Data EnvironmentsCode0
SwissNYF: Tool Grounded LLM Agents for Black Box SettingCode0
Neural Program Synthesis from Diverse Demonstration VideosCode0
Explainable Natural Language to Bash Translation using Abstract Syntax TreeCode0
Neural Program Synthesis with Priority Queue TrainingCode0
Autoencoders as Tools for Program SynthesisCode0
Large Language Models Synergize with Automated Machine LearningCode0
Evaluating Sequence-to-Sequence Learning Models for If-Then Program SynthesisCode0
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All datasetsSPoC TestPSPoC TestWAlgoLisp

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