Execution-Guided Code Generation

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4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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PaT: Planning-after-Trial for Efficient Test-Time Code Generation
May 8, 2026Youngsik Yoon, Sungjae Lee, Seockbean Song +3Test-Time Optimization Execution-Guided Code Generation
Department of Computer Science and Engineering, POSTECH, South Korea · Graduate School of Artificial Intelligence, POSTECH, South Korea · Microsoft Research Asia, Beijing, China
AutoResearch: An Execution-Grounded Multi-Agent Framework for Reliable Research Workflow Automation
May 4, 2026Rajesh Kumar, Waqar Ali, Junaid Ahmed +2Execution-Guided Code Generation Scientific Workflow Automation
International Research Center for Complexity Sciences, Hangzhou International Innovation Institute, Beihang University, Hangzhou 311115, China · Department of Computer Science, College of Science, Mathematics and Technology, Wenzhou-Kean University, Wenzhou 325060, China · Computer Systems Engineering Department, Sukkur IBA University, Sindh, Pakistan +1
SAFEdit: Does Multi-Agent Decomposition Resolve the Reliability Challenges of Instructed Code Editing?
Apr 28, 2026Noam Tarshish, Nofar Selouk, Daniel Hodisan +4Execution-Guided Code Generation Multi-Agent Coding
Ben-Gurion University of the Negev · Beer Sheva, Israel
Learning from Execution: Self-Evolving Memory for Private-Library Code Generation
Apr 27, 2026Mofei Li, Taozhi Chen, Guowei Yang +1Memory-Augmented Language Models Knowledge Augmentation for Language Models
College of AI Tsinghua University Beijing, China · Fitten Tech Co., Ltd. Beijing, China · Emory University Atlanta, GA, USA
You Don't Need Public Tests to Generate Correct Code
Apr 23, 2026Kaushitha Silva, Srinath PereraExecution-Guided Code Generation Code Generation
WSO2 Santa Clara, CA, USA
Feedback Over Form: Why Execution Feedback Matters More Than Pipeline Topology in 1-3B Code Generation
Apr 23, 2026Charles Junichi McAndrewsLLM Self-Refinement Execution-Guided Code Generation
CreativeGame:Toward Mechanic-Aware Creative Game Generation
Apr 21, 2026Hongnan Ma, Han Wang, Shenglin Wang +6Execution-Guided Code Generation Computational Creativity
University of Bristol · Shanghai Jiao Tong University · Shandong University +1
SolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution
Apr 20, 2026Woojin Lee, Jin-Xia HuangExecution-Guided Code Generation LLM-Based Program Synthesis
Electronics and Telecommunications Research Institute, Republic of Korea
ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization
Apr 17, 2026Yixu Huang, Xinglei Yu, Zhongyu WeiExecution-Guided Code Generation Language Model Self-Improvement
School of Data Science Fudan University Shanghai, China
Majority Voting for Code Generation
Apr 17, 2026Tim Launer, Jonas Hübotter, Marco Bagatella +2Execution-Guided Code Generation Code Generation
ETH Zürich, Switzerland
1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World
Feb 20, 2026Qiao Xu, Yipeng Yu, Chengxiao Feng +1Benchmark Design Execution-Guided Code Generation
Taobao & Tmall Group of Alibaba
Code Reasoning for Software Engineering Tasks: A Survey and A Call to Action
Jun 16, 2025Saurabh Pujar, Ira Ceka, Irene Manotas +3Software Engineering Agents Execution-Guided Code Generation
IBM · Columbia University
Toward Secure Code Generation: Bridging Correctness and Security via Task-Adaptive Vulnerability Modeling and Execution-Based Benchmarking
Jul 2, 2024Jiexin Wang, Liuwen Cao, Xitong Luo +6Execution-Guided Code Generation Agentic Code Generation
South China University of Technology, Guangzhou, China · University of Innsbruck, Innsbruck, Austria

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PaT: Planning-after-Trial for Efficient Test-Time Code Generation

Youngsik Yoon, Sungjae Lee, Seockbean Song, Siwei Wang+2

Department of Computer Science and Engineering, POSTECH, South Korea · Graduate School of Artificial Intelligence, POSTECH, South Korea · Microsoft Research Asia, Beijing, China

Test-Time Optimization Execution-Guided Code Generation Cost-Aware Inference Inference-Time Scaling LLM Code Generation

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Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Models (LLMs). However, most existing methods adopt a rigid Planning-before-Trial (PbT) policy, which inefficiently allocates test-time compute by incurring planning overhead even on directly solvable problems. We propose Planning-after-Trial (PaT), an adaptive policy for code generation that invokes a planner only upon verification failure. This adaptive policy naturally enables a heterogeneous model configuration: a cost-efficient model handles generation attempts, while a powerful model is reserved for targeted planning interventions. Empirically, across multiple benchmarks and model families, our approach significantly advances the cost-performance Pareto frontier. Notably, our heterogeneous configuration achieves performance comparable to a large homogeneous model while reducing inference cost by approximately 69%.

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