AAAI 2027 reviewer: no code, no data, no verify

This year's AAAI review batch highlights a persistent tension in AI research between reproducibility and practical constraints. The reviewer notes that AAAI's own guidelines require code and data at submission, yet they also acknowledge that authors may have legitimate reasons—such as funding or IP—for withholding code. The thread suggests that even when code is absent, reviewers often lack time to audit it anyway, and a hard reject solely for missing code is not the norm. Instead, the reviewer's approach is to weigh how much the paper's claims depend on empirical results; if the numbers are central and unverifiable, confidence drops and they ask for anonymized code during rebuttal. This reflects a broader shift toward expecting reproducibility as part of the review process, while leaving room for case-by-case judgment. The outcome for these four papers will likely depend on how much each relies on its numbers and whether authors provide code in the rebuttal phase.