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 276–300 of 423 papers

TitleStatusHype
BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming—0
Bayesian causal inference via probabilistic program synthesis—0
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion—0
BizBench: A Quantitative Reasoning Benchmark for Business and Finance—0
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration—0
Can humans teach machines to code?—0
Can Large Language Models Understand Symbolic Graphics Programs?—0
Can LLMs plan paths with extra hints from solvers?—0
Chemical classification program synthesis using generative artificial intelligence—0
Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming—0
CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis—0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
Code Synthesis with Priority Queue Training—0
Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis—0
Comparing and Combining Lexicase Selection and Novelty Search—0
Complex QA and language models hybrid architectures, Survey—0
Compositional Generalization and Decomposition in Neural Program Synthesis—0
Constraint-based Learning of Phonological Processes—0
COOL: Efficient and Reliable Chain-Oriented Objective Logic with Neural Networks Feedback Control for Program Synthesis—0
CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs—0
CORNET: Learning Table Formatting Rules By Example—0
CounterExample Guided Neural Synthesis—0
Creating Synthetic Datasets via Evolution for Neural Program Synthesis—0
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing—0
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness—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