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 151–175 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
Analogical proportions II—0
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
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
Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents—0
Origami: (un)folding the abstraction of recursion schemes for program synthesis—0
HumanEval on Latest GPT Models -- 2024Code0
LTL learning on GPUsCode0
WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment—0
SwissNYF: Tool Grounded LLM Agents for Black Box SettingCode0
Open-Universe Indoor Scene Generation using LLM Program Synthesis and Uncurated Object Databases—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