Faithful transfer from experimental code to methodological descriptions

Establish a reliable procedure to accurately transfer experimental code modifications produced by Jr. AI Scientist’s coding agent during Stage 2 (Iterative Improvement) of the Experimental Phase into faithful and precise methodological descriptions within the generated manuscripts, despite increased code complexity introduced by iterative edits.

Background

In the Authors Evaluation, the paper identifies that method descriptions in the generated manuscripts can be ambiguous or incomplete, even when the corresponding code exists. This ambiguity partly arises because the coding agent applies numerous iterative modifications during Stage 2 of the Experimental Phase, which increases code complexity and can leave certain components optional or unused.

The authors explicitly note that ensuring accurate translation from complex experimental implementations to clear and faithful methodological narrative remains unresolved, highlighting a systematic gap between execution artifacts (code) and their documentation (Method section). Addressing this gap is essential for trustworthy, reproducible AI-generated research papers.