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 76–100 of 423 papers

TitleStatusHype
AbstractBeam: Enhancing Bottom-Up Program Synthesis using Library Learning—0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
RLSF: Reinforcement Learning via Symbolic Feedback—0
Learning to Reason via Program Generation, Emulation, and SearchCode0
HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis—0
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
Analogical proportions II—0
Goals as Reward-Producing ProgramsCode1
MapCoder: Multi-Agent Code Generation for Competitive Problem SolvingCode2
Finding structure in logographic writing with library learning—0
Program Synthesis using Inductive Logic Programming for the Abstraction and Reasoning Corpus—0
Large Language Models Synergize with Automated Machine LearningCode0
PhilHumans: Benchmarking Machine Learning for Personal Health—0
Can humans teach machines to code?—0
BANSAI: Towards Bridging the AI Adoption Gap in Industrial Robotics with Neurosymbolic Programming—0
Fewer Truncations Improve Language Modeling—0
Self-Training Large Language Models for Improved Visual Program Synthesis With Visual Reinforcement—0
Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLMCode2
SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine—0
WatChat: Explaining perplexing programs by debugging mental modelsCode0
Guiding Enumerative Program Synthesis with Large Language Models—0
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models—0
Constrained Decoding for Fill-in-the-Middle Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive GrammarsCode1
Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents—0
Origami: (un)folding the abstraction of recursion schemes for program synthesis—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