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

Showing 2650 of 423 papers

TitleStatusHype
Program Synthesis Dialog Agents for Interactive Decision-MakingCode0
Visual Agentic AI for Spatial Reasoning with a Dynamic API0
CODESIM: Multi-Agent Code Generation and Problem Solving through Simulation-Driven Planning and DebuggingCode2
Proving the Coding Interview: A Benchmark for Formally Verified Code Generation0
Learning Semantics-aware Search Operators for Genetic Programming0
QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks0
AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs0
Online Prompt Selection for Program Synthesis0
MMFactory: A Universal Solution Search Engine for Vision-Language Tasks0
EcoSearch: A Constant-Delay Best-First Search Algorithm for Program Synthesis0
Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis0
ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)Code0
ARC Prize 2024: Technical ReportCode3
From Code to Play: Benchmarking Program Search for Games Using Large Language Models0
Searching Latent Program SpacesCode2
LLMPhy: Complex Physical Reasoning Using Large Language Models and World Models0
The Surprising Effectiveness of Test-Time Training for Few-Shot LearningCode3
Combining Induction and Transduction for Abstract ReasoningCode2
Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwareCode0
System 2 Reasoning via Generality and Adaptation0
Mitigating Gender Bias in Code Large Language Models via Model Editing0
Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and ObjectsCode1
Can LLMs plan paths with extra hints from solvers?0
Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation0
MA-RLHF: Reinforcement Learning from Human Feedback with Macro ActionsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10038.5Unverified
2Multiclass localizerSuccess rate @budget 10034.2Unverified
#ModelMetricClaimedVerifiedStatus
1DrRepairSuccess rate @budget 10057Unverified
2Multiclass localizerSuccess rate @budget 10053.7Unverified
#ModelMetricClaimedVerifiedStatus
1CodeTrans-MT-TF-SmallAccuracy90.31Unverified