RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback

• URL: http://arxiv.org/abs/2510.06186v1
• Date: Tue, 07 Oct 2025 17:45:35 GMT
• Title: RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
• Authors: Chunyu Miao, Henry Peng Zou, Yangning Li, Yankai Chen, Yibo Wang, Fangxin Wang, Yifan Li, Wooseong Yang, Bowei He, Xinni Zhang, Dianzhi Yu, Hanchen Yang, Hoang H Nguyen, Yue Zhou, Jie Yang, Jizhou Guo, Wenzhe Fan, Chin-Yuan Yeh, Panpan Meng, Liancheng Fang, Jinhu Qi, Wei-Chieh Huang, Zhengyao Gu, Yuwei Han, Langzhou He, Yuyao Yang, Xue Liu, Irwin King, Philip S. Yu,
• Abstract summary: We present RECODE-H, a benchmark of 102 tasks from research papers and repositories.<n>It includes structured instructions,unit tests, and a five-level feedback hierarchy to reflect realistic researcher-agent collaboration.<n>We also present ReCodeAgent, a framework that integrates feedback into iterative code generation.
• Score: 76.28414843494073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing works largely adopt one-shot settings, ignoring the iterative and feedback-driven nature of realistic workflows of scientific research development. To address this gap, we present RECODE-H, a benchmark of 102 tasks from research papers and repositories that evaluates LLM agents through multi-turn interactions with LLM-simulated human feedback. It includes structured instructions,unit tests, and a five-level feedback hierarchy to reflect realistic researcher-agent collaboration. We further present ReCodeAgent, a framework that integrates feedback into iterative code generation. Experiments with leading LLMs, including GPT-5, Claude-Sonnet-4, DeepSeek-V3.1, and Gemini 2.5, show substantial performance gains with richer feedback, while also highlighting ongoing challenges in the generation of complex research code. RECODE-H establishes a foundation for developing adaptive, feedback-driven LLM agents in scientific research implementation

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