Latest AI for Code Research Papers
The newest AI for Code papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks AI for Code so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
Get the latest AI for Code papers in your inbox — free →Recent papers
- Code Generation from Regression Trees for Microsecond-Scale Decisions in Operating SystemsDaniel Friesel · Zenodo (CERN European Organ... · Sep 29, 2026
This archive holds artifacts (code generation, benchmark scripts, benchmark data, and evaluation scripts) for the PLOS'26 publication “Code Generation from Regression Trees for Microsecond-Scale Decisions in Operating Systems”. Please refer…
- Code Generation from Regression Trees for Microsecond-Scale Decisions in Operating SystemsDaniel Friesel · Zenodo (CERN European Organ... · Sep 29, 2026
This archive holds artifacts (code generation, benchmark scripts, benchmark data, and evaluation scripts) for the PLOS'26 publication “Code Generation from Regression Trees for Microsecond-Scale Decisions in Operating Systems”. Please refer…
- Reproducibility in the Age of Agentic AI: Context Engineering at the Timescale of a CodebaseLorena A. Barba · arXiv · Sep 10, 2026
Reproducible research practices are context engineering for AI coding agents. I argue that agents lower the cost of maintaining tests, commit histories, repository structure, instructions, and decision records while making their benefits im…
- SemVerBench: Benchmarking LLM Comprehension of Version-Constraint Resolution SemanticsQibai Chen, Zeming Liu · arXiv · Sep 10, 2026
Large language model (LLM) coding agents constantly decide whether a version satisfies a constraint such as ^1.2.3 or >=2.0,<3, yet their grasp of version-constraint semantics has never been measured directly. We introduce SemVerBench…
- SaltBench: A Referee-Gated Protocol for Measuring Method Effects in Machine-Checked Software WorkJason Hickey · arXiv · Sep 10, 2026
SaltBench is a benchmark protocol for one question: How does a machine referee change the way a coding agent works? A machine referee --- a proof kernel, a program verifier, or a withheld test suite --- decides what an agent's work is worth…
- RCL: A Retrieval-Confidence Layer for Detecting Insufficient Context in Enterprise Retrieval-Augmented Code GenerationChandra Mohan Ravuri · arXiv · Sep 10, 2026
Retrieval-Augmented Generation (RAG) for code generation has been studied extensively on public repositories, where a model's parametric knowledge often compensates for imperfect retrieval. This breaks down in enterprise codebases, where pr…
- TrajMark: Ownership Attribution and Segment-Level Tamper Localization for Coding-Agent TrajectoriesBokang Zeng, Zheng Gao, Xiaoyu Li, Xiaoyan Feng et al. · arXiv · Sep 9, 2026
Watermarking the final patch produced by a coding agent provides provenance evidence for the submitted artifact, but does not authenticate the visible process that produced it. Behavioral watermarking methods primarily provide a global dete…
- Towards Assurance Closure in AI-Native Large-Scale Agile Software DevelopmentRicardo Britto · arXiv · Aug 7, 2026
The AI-Native Manifesto envisions large-scale agile software development in which humans increasingly govern intent, risk, and exceptions while agents execute more of the engineering process. Realizing that end-state requires more than bett…
- Understanding and Improving Model Editing for Secure Code GenerationWeifeng Sun, Quanjun Zhang, Yuchen Chen, Chengran Yang et al. · arXiv · Aug 7, 2026
Large language models (LLMs) are widely used for code generation, yet they can reproduce vulnerable implementations learned from insecure training patterns. Prior work has mainly explored inference-time hardening, which reduces insecure gen…
- How Reasoning Shapes Social Bias in LLM-Generated Code?Weifeng Sun, Jieke Shi, Zhou Yang, Yuchen Chen et al. · arXiv · Aug 7, 2026
Large language models (LLMs) are increasingly used for code generation, yet generated programs may exhibit social bias through unfair or differential treatment of sensitive demographic attributes. While prior work mainly studies direct code…
- Characterizing the Quality Profile of AI-Generated C++ in ProductionMichael Tran, Fred Lewis, Kun Yang, Saksham Thakur et al. · arXiv · Aug 6, 2026
The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity. Yet, recent studies point to a growing trade-off, revealing persistent challenges with code quality and maintainability. Industry leaders, …
- From Code Generation to Code Reasoning: A Survey of Inference-Time Methods in LLM-Based Code GenerationACL ARR 2026 May Submission · May 26, 2026
Large language models (LLMs) have rapidly advanced code generation, evolving from prompt-based function synthesis to iterative, execution-guided, tool-integrated, and agentic software engineering systems. While recent progress has produced …
- Enhancing Code LLMs with Reinforcement Learning in Code Generation: A SurveyJunqiao Wang, Zeng Zhang, Yangfan He, Zihao Zhang et al. · ICLR 2026 Workshop LLM Reasoning · Mar 8, 2026
With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of R…
- CodeGenGuard: A Watermark for Code Generation ModelsBorui Yang, Mingxuan Ma, Liyao Xiang, Nan Chen et al. · ICLR 2026 Poster · Jan 26, 2026
Code language models (LMs) represent valuable intellectual property (IP) as their training involves immense investments, including large-scale code corpora, proprietary annotations, extensive computational resources, and specialized designs…
- VERINA: Benchmarking Verifiable Code GenerationZhe Ye, Zhengxu Yan, Jingxuan He, Timothe Kasriel et al. · ICLR 2026 Poster · Jan 26, 2026
Large language models (LLMs) are increasingly integrated in software development, but ensuring correctness in LLM-generated code remains challenging and often requires costly manual review. Verifiable code generation---jointly generating co…
- From Code Generation to Code Reasoning: A Survey of Inference-Time Methods in LLM-Based Code GenerationACL ARR 2026 January Submission · Jan 6, 2026
Large language models (LLMs) have rapidly advanced the state of code generation, evolving from prompt-based function synthesis to iterative, execution-guided, and agentic software engineering systems. While recent progress has led to impres…
- Process Supervision-Guided Policy Optimization for Code GenerationNing Dai, Zheng Wu, Renjie Zheng, Ziyun Wei et al. · Submitted to ICLR 2026 · Sep 20, 2025
Reinforcement learning (RL) with unit test feedback has enhanced large language models’ (LLMs) code generation, but relies on sparse rewards provided only after complete code evaluation, limiting learning efficiency and incremental improvem…
- Verina: Benchmarking Verifiable Code GenerationZhe Ye, Zhengxu Yan, Jingxuan He, Timothe Kasriel et al. · AI4Math@ICML25 Poster · Jul 9, 2025
Large language models (LLMs) are being increasingly integrated in software development, but ensuring correctness in LLM-generated code remains challenging, which often requires manual review. Verifiable code generation---jointly generating …
- Multi-Turn Code Generation Through Single-Step RewardsArnav Kumar Jain, Gonzalo Gonzalez-Pumariega, Wayne Chen, Alexander M Rush et al. · ICML 2025 spotlightposter · May 1, 2025
We address the problem of code generation from multi-turn execution feedback. Existing methods either generate code without feedback or use complex, hierarchical reinforcement learning to optimize multi-turn rewards. We propose a simple ye…
- Paper2Code: Automating Code Generation from Scientific Papers in Machine LearningMinju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang · arXiv.org · Apr 24, 2025
Despite the rapid growth of machine learning research, corresponding code implementations are often unavailable, making it slow and labor-intensive for researchers to reproduce results and build upon prior work. In the meantime, recent Larg…
- Type-Constrained Code Generation with Language ModelsNiels Mündler, Jingxuan He, Hao Wang, Koushik Sen et al. · Proc. ACM Program. Lang. · Apr 12, 2025
Large language models (LLMs) have achieved notable success in code generation. However, they still frequently produce uncompilable output because their next-token inference procedure does not model formal aspects of code. Although constrain…
- Multi-Turn Code Generation Through Single-Step RewardsArnav Kumar Jain, Gonzalo Gonzalez-Pumariega, Wayne Chen, Alexander M Rush et al. · SSI-FM Poster · Mar 8, 2025
We address the problem of code generation from multi-turn execution feedback. Existing methods either generate code without feedback or use complex, hierarchical reinforcement learning to optimize multi-turn rewards. We propose a simple ye…
- Multi-Turn Code Generation Through Single-Step RewardsArnav Kumar Jain, Gonzalo Gonzalez-Pumariega, Wayne Chen, Alexander M Rush et al. · ICLR 2025 Workshop VerifAI Poster · Mar 6, 2025
We address the problem of code generation from multi-turn execution feedback. Existing methods either generate code without feedback or use complex, hierarchical reinforcement learning to optimize multi-turn rewards. We propose a simple ye…
- Multi-Turn Code Generation Through Single-Step RewardsArnav Kumar Jain, Gonzalo Gonzalez-Pumariega, Wayne Chen, Alexander M Rush et al. · Reasoning and Planning for LLMs @ ICLR2025 · Mar 5, 2025
We address the problem of code generation from multi-turn execution feedback. Existing methods either generate code without feedback or use complex, hierarchical reinforcement learning to optimize multi-turn rewards. We propose a simple ye…
- Improve Code Generation with FeedbackZhi Xu, Yun Fu · Submitted to ICLR 2025 · Sep 27, 2024
As advancements in Large Language Models (LLMs) continue to accelerate, an increasing number of researchers are exploring the potential of these models to assist in everyday tasks. Despite their remarkable achievements in various downstream…
- VersiCode: Towards Version-controllable Code GenerationTongtong Wu, Weigang Wu, Xingyu Wang, Kang Xu et al. · Submitted to ICLR 2025 · Sep 26, 2024
Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development, marked by frequent library updates. This gap significantly limits LLMs' de…
- Improving Code Style for Accurate Code GenerationNaman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E. Gonzalez et al. · SyntheticData4ML 2023 Poster · Oct 30, 2023
Natural language to code generation is an important application area of LLMs and has received wide attention from the community. The majority of relevant studies have exclusively concentrated on increasing the quantity and functional corre…