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 301–325 of 423 papers

TitleStatusHype
Visual Agentic AI for Spatial Reasoning with a Dynamic API—0
VRDSynth: Synthesizing Programs for Multilingual Visually Rich Document Information Extraction—0
WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment—0
Write, Execute, Assess: Program Synthesis with a REPL—0
Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis—0
Learning to select examples for program synthesis—0
Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation—0
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context—0
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis—0
Leveraging Language to Learn Program Abstractions and Search Heuristics—0
Limits of an AI program for solving college math problems—0
LLM4TDD: Best Practices for Test Driven Development Using Large Language Models—0
LLM for SoC Security: A Paradigm Shift—0
LLMPhy: Complex Physical Reasoning Using Large Language Models and World Models—0
Logic-Q: Improving Deep Reinforcement Learning-based Quantitative Trading via Program Sketch-based Tuning—0
LoopInvGen: A Loop Invariant Generator based on Precondition Inference—0
Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics—0
Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences—0
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis—0
Mitigating Gender Bias in Code Large Language Models via Model Editing—0
mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis—0
MMFactory: A Universal Solution Search Engine for Vision-Language Tasks—0
MTGP: Combining Metamorphic Testing and Genetic Programming—0
Multi-Intent Detection in User Provided Annotations for Programming by Examples Systems—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