Beyond Retrieval: Component Relations and Feedback for Faithful Paper Reproduction

Automated paper reproduction aims to implement methods from research papers and reproduce their reported results. Existing systems retrieve related papers and code to supplement missing implementation details. However, accurate retrieval does not ensure faithful reproduction. Our diagnostic study shows that agents can unnecessarily alter reusable components while failing to implement required changes. Moreover, feedback on overall performance provides limited guidance for identifying and correcting these errors. We therefore propose , which guides code generation through component relations and refines implementations using component-level experimental feedback. On an extended ReproduceBench covering 40 papers across ten tasks in three domains, achieves an average performance gap of 9.26%, reducing the gap by 25.46 percentage points over the strongest baseline. The code will be publicly released upon acceptance.