LLM-Based Program Synthesis

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22 papers in the last four weeks, up 100% on the four weeks before. 0.2% of all new papers.

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Doc2Spec: Synthesizing Formal Programming Specifications from Natural Language via Grammar Induction
Jan 30, 2026Shihao Xia, Mengting He, Haomin Jia +2Autoformalization Program Synthesis
The Pennsylvania State University · University of the Chinese Academy of Sciences · Institute of Computing Technology, Chinese Academy of Sciences
Programming over Thinking: Efficient and Robust Multi-Constraint Planning
Jan 14, 2026Derrick Goh Xin Deik, Quanyu Long, Zhengyuan Liu +2LLM Planning LLM-Based Program Synthesis
Nanyang Technological University, Singapore · Agency for Science, Technology and Research (A*STAR), Singapore
Evolving Programmatic Skill Networks
Jan 7, 2026Haochen Shi, Xingdi Yuan, Bang LiuContinual Learning for LLM Agents LLM-Based Program Synthesis
DIRO & Institut Courtois, Université de Montréal · Mila – Québec AI Institute · Microsoft Research +1
Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search
Dec 31, 2025Rohit Dwivedula, Divyanshu Saxena, Sujay Yadalam +3Automated Heuristic Design LLM-Based Program Synthesis
The University of Texas at Austin Austin, TX, USA
Compositional Machine Design as Program Synthesis with LLMs
Oct 16, 2025Wenqian Zhang, Yangyi Huang, Weiyang Liu +1LLM-Based Program Synthesis Program Synthesis
1The Chinese University of Hong Kong (Shenzhen) · 2The Chinese University of Hong Kong
LLM Priors for ERM over Programs
Oct 16, 2025Shivam Singhal, Priyadarsi Mishra, Eran Malach +1LLM-Based Program Synthesis Empirical Risk Minimization
Department of Computer Science & Engineering, Texas A&M University, TX, USA · Kempner Institute, Harvard University, MA, USA
Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny
Jul 22, 2025Chuanhao Yan, Fengdi Che, Xuhan Huang +12RL for Language Models RL for Language Model Reasoning
Shanghai AI Lab · Tsinghua University · University of Alberta +5
ChatModel: Automating Reference Model Design and Verification with LLMs
Jun 18, 2025Jianmin Ye, Tianyang Liu, Qi Tian +3LLM-Based Program Synthesis LLM Code Generation
National Center of Technology Innovation for EDA, China · School of Integrated Circuits, Southeast University, China
OptiMUS-0.3: Using Large Language Models to Model and Solve Optimization Problems at Scale
Jul 29, 2024Ali AhmadiTeshnizi, Wenzhi Gao, Herman Brunborg +3Mixed-Integer Linear Programming Large Language Model-Guided Optimization
a School of Management Science and Engineering, Stanford University, Stanford, California 94305 · b Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305
Spec-Harness: Measuring and Improving Behavioral Adequacy of LLM-Synthesized Formal Specifications
Date pendingMd Rakib Hossain Misu, Iris Ma, Cristina V. LopesSoftware Engineering Agents LLM-Based Program Synthesis
Department of Informatics, University of California, Irvine
PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement
Date pendingTianyidan Xie, Peiyu Wang, Hu Jiaxin +9LLM-Based Program Synthesis Scientific Code Generation
Nanjing University · Skywork AI · Jilin University +2
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
Date pendingJhon Lopez, Carlos Hinojosa, Bernard GhanemLLM-Based Program Synthesis Video Generation
Universidad Industrial de Santander · King Abdullah University of Science and Technology
Code-to-Harness: Distilling Black-Box Optimizers from Self-Play
Date pendingYi Wu, Zheng Ren, Zhiyu Hu +8Black-Box Optimization Large Language Model-Guided Optimization
Google Mountain View, USA

Jan 30, 2026, cs.PLJ/K move · Enter open · S save

Doc2Spec: Synthesizing Formal Programming Specifications from Natural Language via Grammar Induction

Shihao Xia, Mengting He, Haomin Jia, Xinyan Zhao+1

The Pennsylvania State University · University of the Chinese Academy of Sciences · Institute of Computing Technology, Chinese Academy of Sciences

Autoformalization Program Synthesis Formal Verification LLM-Based Program Synthesis

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Ensuring that API implementations and usage comply with natural language programming rules is critical for software correctness, security, and reliability. Formal verification can provide strong guarantees but requires precise specifications, which are difficult and costly to write manually. To address this challenge, we present Doc2Spec, a multi-agent framework that automatically induces a domain-specific grammar from natural-language API rules and uses it to guide specification generation. Doc2Spec fixes a domain-agnostic logical skeleton as a grammar template, prompts LLMs to infer domain-specific predicates and sorts, and formalizes each rule within the resulting grammar, turning an unreliable one-shot translation into a sequence of constrained, checkable steps. Across six benchmarks spanning Solidity and Rust, Doc2Spec improves precision by 0.28 and recall by 0.37 over baselines that lack grammar induction or perform it in one unstaged step, demonstrating the benefits of grammar-guided formalization and the staged pipeline. Moreover, its formalized rules enable symbolic-execution tools to uncover 142 previously unknown rule violations, confirming the rules' correctness and practical usefulness.

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