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Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess these applications interactively, current automated LLM evaluations primarily focus on code analysis and unit tests. This mismatch can cause LLM judges to miss failures t...
DECODE (Developer Edits of Code Dataset), a dataset of 53.6K real-world in-IDE code edits of AI-generated code in Python, TypeScript, and JavaScript, is introduced, and finetuning on DECODE enables open-source 3B models to perform code edit prediction tasks significantly better than frontier LLMs.
The results show that LLM agents, even without executing code, can identify many real-world reproducibility problems from paper-repository pairs, and ReproRepo can serve as a reusable, scalable framework for future evaluations of LLM agents on real-world reproducibility auditing.
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