RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
By: Chunyu Miao , Henry Peng Zou , Yangning Li and more
Potential Business Impact:
Helps AI write better science code with feedback.
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
Similar Papers
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Software Engineering
Helps computers write computer programs from words.
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Software Engineering
Makes computers write computer programs from your words.
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Software Engineering
Helps computers write computer programs from descriptions.