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 326–350 of 423 papers

TitleStatusHype
From Words to Code: Harnessing Data for Program Synthesis from Natural Language—0
Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models—0
Fully Autonomous Programming with Large Language Models—0
Functional Code Building Genetic Programming—0
Function-constrained Program Synthesis—0
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis—0
Generating Sequences by Learning to Self-Correct—0
Genetic Algorithm for Program Synthesis—0
Geometry of Program Synthesis—0
Glass-Box Program Synthesis: A Machine Learning Approach—0
Goal-directed Generation of Discrete Structures with Conditional Generative Models—0
GPU accelerated program synthesis: Enumerate semantics, not syntax!—0
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing Programs from Flow Charts—0
Guiding Enumerative Program Synthesis with Large Language Models—0
Guiding LLM Temporal Logic Generation with Explicit Separation of Data and Control—0
h4rm3l: A language for Composable Jailbreak Attack Synthesis—0
Hierarchical Neural Program Synthesis—0
HOTGP -- Higher-Order Typed Genetic Programming—0
HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis—0
ImageEye: Batch Image Processing Using Program Synthesis—0
Imitation-Projected Programmatic Reinforcement Learning—0
Improved Tree Search for Automatic Program Synthesis—0
Improving Neural Program Synthesis with Inferred Execution Traces—0
Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization—0
Inference Over Programs That Make Predictions—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