Poster Sat, Apr 25, 2026 • 11:15 AM – 1:45 PM PDT Pavilion 3 P3-#1514

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

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 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 Yu

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 LLMs through multi-turn interactions with 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. Experimentswith 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.