Code Generation

31개 벤치마크 · 논문 3,062편 · 이 태스크의 논문 보기 →

Benchmarks

Verified Smart Contract Code Comments

결과 2개

Most implemented

LLaMA: Open and Efficient Foundation Language Models

2023-02-27 · 구현 57개

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

2016-04-11 · 구현 42개

Llama 2: Open Foundation and Fine-Tuned Chat Models

2023-07-18 · 구현 19개

Evaluating Large Language Models Trained on Code

2021-07-07 · 구현 13개

pix2code: Generating Code from a Graphical User Interface Screenshot

2017-05-22 · 구현 11개

Papers

Retrofitting Code Using LLMs to Support Exceptional Behavior

2026-09-09 · Linghan Zhong, Jiyang Zhang, Jayanth Srinivasa, Junyi Jessy Li 외 arxiv

Exception Related Code (ERC), which includes throw statements, conditions (if statements) that guard those throw statements, and try/catch blocks, is an essential component of software systems, allowing developers to det…

Code Generation

Φ-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

2026-09-09 · Leilei Ding, Shumin Wang, Yuting Huang, Fanqi Wan 외 arxiv

Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in developing and optimizing the very infrastructure that powers them. …

Code Generation

RISE: Recursive Improvement via Self-Extrapolating Policy Distillation

2026-09-04 · Yang Li, Semih Yavuz, Shafiq Joty arxiv

On-policy distillation (OPD) provides dense, per-token supervision for language model post-training, but its effectiveness is bottlenecked by teacher quality: external teachers suffer from distribution mismatch, while se…

Mathematical Reasoning Code Generation

Substrate-Aware AI Agents: Execution Context as a First-Class Input

2026-09-04 · Manu Agrawal arxiv

Autonomous AI agents increasingly select actions in environments whose memory, execution-time, runtime, compute, and operational constraints determine what counts as a suitable plan. We call the absence of this execution…

Code Generation

AutoLR: Automating the Path from Research to Launch Review in Industrial Recommender Systems

2026-09-04 · Qi Zhang, Yanlin Chen, Wenchao Xiao arxiv

Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path from idea to deployment. In \textbf{DASHEN, NetEase's gaming-community app}, algorithm engineers typically id…

Code Generation

Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle

2026-09-04 · Happy Bhati arxiv

AI coding systems are moving from autocomplete and chat toward agents that can inspect repositories, edit multiple files, run tools, write tests, open pull requests, and work for long periods with limited supervision. Th…

Code Generation

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