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 201–250 of 423 papers

TitleStatusHype
Finding structure in logographic writing with library learning—0
Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets—0
From Code to Play: Benchmarking Program Search for Games Using Large Language Models—0
From Perception to Programs: Regularize, Overparameterize, and Amortize—0
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
Neural Program Synthesis with Query—0
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints—0
Neuro-Symbolic Program Synthesis—0
Novel positional encodings to enable tree-structured transformers—0
One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis—0
Online Prompt Selection for Program Synthesis—0
Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases—0
Optimal Neural Program Synthesis from Multimodal Specifications—0
Program Synthesis Over Noisy Data with Guarantees—0
Optimal Sizing of Stand-alone Solar PV Systems via Automated Formal Synthesis—0
Origami: (un)folding the abstraction of recursion schemes for program synthesis—0
Parallel Constraint-Driven Inductive Logic Programming—0
PhilHumans: Benchmarking Machine Learning for Personal Health—0
PLANS: Neuro-Symbolic Program Learning from Videos—0
Population Diversity Leads to Short Running Times of Lexicase Selection—0
Process Discovery for Structured Program Synthesis—0
Programming by Example and Text-to-Code Translation for Conversational Code Generation—0
Programming by Rewards—0
Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment—0
Program Synthesis from Visual Specification—0
Program Synthesis Through Reinforcement Learning Guided Tree Search—0
Program Synthesis using Inductive Logic Programming for the Abstraction and Reasoning Corpus—0
Program Synthesis with Best-First Bottom-Up Search—0
Program Synthesis with Pragmatic Communication—0
Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning—0
Proving the Coding Interview: A Benchmark for Formally Verified Code Generation—0
PSB2: The Second Program Synthesis Benchmark Suite—0
QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach—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