Key papers

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (2025)
  DeepSeek-AI et al.
  31.18
• Executable Code Actions Elicit Better LLM Agents (2024)
  Xingyao Wang, Yangyi Chen, Lifan Yuan, et al.
  31.13
• Autogen: Enabling Next-gen LLM Applications Via Multi-agent Conversation (2023)
  Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.
  29.16
• Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions (2024)
  Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.
  28.58
• Large Language Models For Software Engineering: A Systematic Literature Review (2023)
  Xinyi Hou, Yanjie Zhao, Yue Liu, et al.
  27.84
• Livecodebench: Holistic And Contamination Free Evaluation Of Large Language Models For Code (2024)
  Naman Jain, King Han, Alex Gu, et al.
  27.45
• Magicoder: Empowering Code Generation With Oss-instruct (2023)
  Yuxiang Wei, Zhe Wang, Jiawei Liu, et al.
  27.43
• Flashrag: A Modular Toolkit For Efficient Retrieval-augmented Generation Research (2024)
  Jiajie Jin, Yutao Zhu, Guanting Dong, et al.
  26.95
• Datadreamer: A Tool For Synthetic Data Generation And Reproducible LLM Workflows (2024)
  Ajay Patel, Colin Raffel, Chris Callison-Burch
  26.77
• A Survey On Large Language Models For Code Generation (2024)
  Juyong Jiang, Fan Wang, Jiasi Shen, et al.
  26.64
• Eagle: Exploring The Design Space For Multimodal Llms With Mixture Of Encoders (2024)
  Min Shi, Fuxiao Liu, Shihao Wang, et al.
  25.55
• Llms Know More Than They Show: On The Intrinsic Representation Of LLM Hallucinations (2024)
  Hadas Orgad, Michael Toker, Zorik Gekhman, et al.
  25.38
• SPHINX-X: Scaling Data And Parameters For A Family Of Multi-modal Large Language Models (2024)
  Dongyang Liu, Renrui Zhang, Longtian Qiu, et al.
  23.22
• A Paradigm Shift In Machine Translation: Boosting Translation Performance Of Large Language Models (2023)
  Haoran Xu, Young Jin Kim, Amr Sharaf, et al.
  23.20
• Ml-bench: Evaluating Large Language Models And Agents For Machine Learning Tasks On Repository-level Code (2023)
  Xiangru Tang, Yuliang Liu, Zefan Cai, et al.
  23.09
• Stepcoder: Improve Code Generation With Reinforcement Learning From Compiler Feedback (2024)
  Shihan Dou, Yan Liu, Haoxiang Jia, et al.
  22.83
• AST-T5: Structure-aware Pretraining For Code Generation And Understanding (2024)
  Linyuan Gong, Mostafa Elhoushi, Alvin Cheung
  21.95
• Llm4decompile: Decompiling Binary Code With Large Language Models (2024)
  Hanzhuo Tan, Qi Luo, Jing Li, et al.
  21.86
• Safedecoding: Defending Against Jailbreak Attacks Via Safety-aware Decoding (2024)
  Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.
  21.73
• Large Language Models For Compiler Optimization (2023)
  Chris Cummins, Volker Seeker, Dejan Grubisic, et al.
  21.62
• Verilogeval: Evaluating Large Language Models For Verilog Code Generation (2023)
  Mingjie Liu, Nathaniel Pinckney, Brucek Khailany, et al.
  21.49
• If LLM Is The Wizard, Then Code Is The Wand: A Survey On How Code Empowers Large Language Models To Serve As Intelligent Agents (2024)
  Ke Yang, Jiateng Liu, John Wu, et al.
  21.32
• How Abilities In Large Language Models Are Affected By Supervised Fine-tuning Data Composition (2023)
  Guanting Dong, Hongyi Yuan, Keming Lu, et al.
  20.76
• A Simple And Effective \(L_2\) Norm-based Strategy For KV Cache Compression (2024)
  Alessio Devoto, Yu Zhao, Simone Scardapane, et al.
  20.70
• CRAFT: Customizing Llms By Creating And Retrieving From Specialized Toolsets (2023)
  Lifan Yuan, Yangyi Chen, Xingyao Wang, et al.
  20.63
• From Code To Correctness: Closing The Last Mile Of Code Generation With Hierarchical Debugging (2024)
  Yuling Shi, Songsong Wang, Chengcheng Wan, et al.
  20.33
• Frozen Transformers In Language Models Are Effective Visual Encoder Layers (2023)
  Ziqi Pang, Ziyang Xie, Yunze Man, et al.
  19.32
• Who Validates The Validators? Aligning Llm-assisted Evaluation Of LLM Outputs With Human Preferences (2024)
  Shreya Shankar, J. D. Zamfirescu-Pereira, Björn Hartmann, et al.
  19.31
• SARATHI: Efficient LLM Inference By Piggybacking Decodes With Chunked Prefills (2023)
  Amey Agrawal, Ashish Panwar, Jayashree Mohan, et al.
  18.81
• Beyond Functional Correctness: Exploring Hallucinations In Llm-generated Code (2024)
  Fang Liu, Yang Liu, Lin Shi, et al.
  18.73
• Liger Kernel: Efficient Triton Kernels For LLM Training (2024)
  Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli, et al.
  18.71
• Repairagent: An Autonomous, Llm-based Agent For Program Repair (2024)
  Islem Bouzenia, Premkumar Devanbu, Michael Pradel
  18.60
• Goex: Perspectives And Designs Towards A Runtime For Autonomous LLM Applications (2024)
  Shishir G. Patil, Tianjun Zhang, Vivian Fang, et al.
  18.57
• Mercury: A Code Efficiency Benchmark For Code Large Language Models (2024)
  Mingzhe Du, Anh Tuan Luu, Bin Ji, et al.
  18.43
• Astraios: Parameter-efficient Instruction Tuning Code Large Language Models (2024)
  Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai, et al.
  18.02
• API-BLEND: A Comprehensive Corpora For Training And Benchmarking API Llms (2024)
  Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, et al.
  17.98
• Debug Like A Human: A Large Language Model Debugger Via Verifying Runtime Execution Step-by-step (2024)
  Li Zhong, Zilong Wang, Jingbo Shang
  17.98
• Llama-reviewer: Advancing Code Review Automation With Large Language Models Through Parameter-efficient Fine-tuning (2023)
  Junyi Lu, Lei Yu, Xiaojia Li, et al.
  17.90
• Openba: An Open-sourced 15B Bilingual Asymmetric Seq2seq Model Pre-trained From Scratch (2023)
  Juntao Li, Zecheng Tang, Yuyang Ding, et al.
  17.68
• Squeezed Attention: Accelerating Long Context Length LLM Inference (2024)
  Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, et al.
  17.60
• At Which Training Stage Does Code Data Help Llms Reasoning? (2023)
  Yingwei Ma, Yue Liu, Yue Yu, et al.
  17.45
• Exploring Parameter-efficient Fine-tuning Techniques For Code Generation With Large Language Models (2023)
  Martin Weyssow, Xin Zhou, Kisub Kim, et al.
  17.45
• UMBRELA: Umbrela Is The (open-source Reproduction Of The) Bing Relevance Assessor (2024)
  Shivani Upadhyay, Ronak Pradeep, Nandan Thakur, et al.
  17.41
• Chainforge: A Visual Toolkit For Prompt Engineering And LLM Hypothesis Testing (2023)
  Ian Arawjo, Chelse Swoopes, Priyan Vaithilingam, et al.
  17.40
• Codescope: An Execution-based Multilingual Multitask Multidimensional Benchmark For Evaluating Llms On Code Understanding And Generation (2023)
  Weixiang Yan, Haitian Liu, Yunkun Wang, et al.
  17.37
• Debugbench: Evaluating Debugging Capability Of Large Language Models (2024)
  Runchu Tian, Yining Ye, Yujia Qin, et al.
  17.13
• Codeeditorbench: Evaluating Code Editing Capability Of Large Language Models (2024)
  Jiawei Guo, Ziming Li, Xueling Liu, et al.
  17.07
• Agent Skills For Large Language Models: Architecture, Acquisition, Security, And The Path Forward (2026)
  Renjun Xu, Yang Yan
  16.98
• Causal Parrots: Large Language Models May Talk Causality But Are Not Causal (2023)
  Matej Zečević, Moritz Willig, Devendra Singh Dhami, et al.
  16.91
• Can It Edit? Evaluating The Ability Of Large Language Models To Follow Code Editing Instructions (2023)
  Federico Cassano, Luisa Li, Akul Sethi, et al.
  16.72
• Xgrammar: Flexible And Efficient Structured Generation Engine For Large Language Models (2024)
  Yixin Dong, Charlie F. Ruan, Yaxing Cai, et al.
  16.58
• From CLIP To DINO: Visual Encoders Shout In Multi-modal Large Language Models (2023)
  Dongsheng Jiang, Yuchen Liu, Songlin Liu, et al.
  16.49
• Can Chatgpt Replace Stackoverflow? A Study On Robustness And Reliability Of Large Language Model Code Generation (2023)
  Li Zhong, Zilong Wang
  16.23
• Evaluation Of Llms On Syntax-aware Code Fill-in-the-middle Tasks (2024)
  Linyuan Gong, Sida Wang, Mostafa Elhoushi, et al.
  16.20
• Reasoning Runtime Behavior Of A Program With LLM: How Far Are We? (2024)
  Junkai Chen, Zhiyuan Pan, Xing Hu, et al.
  16.15
• Rllm: Relational Table Learning With Llms (2024)
  Weichen Li, Xiaotong Huang, Jianwu Zheng, et al.
  15.90
• Copilot Evaluation Harness: Evaluating Llm-guided Software Programming (2024)
  Anisha Agarwal, Aaron Chan, Shubham Chandel, et al.
  15.89
• Let Your Graph Do The Talking: Encoding Structured Data For Llms (2024)
  Bryan Perozzi, Bahare Fatemi, Dustin Zelle, et al.
  15.65
• Bfs-prover: Scalable Best-first Tree Search For Llm-based Automatic Theorem Proving (2025)
  Ran Xin, Chenguang Xi, Jie Yang, et al.
  15.64
• A Performance Study Of Llm-generated Code On Leetcode (2024)
  Tristan Coignion, Clément Quinton, Romain Rouvoy
  15.57