How do you critically evaluate claims in new machine learning papers? What red flags suggest results may not generalize or may be overstated?

Sample answer preview

Critical evaluation of ML papers is essential because publication bias, benchmark gaming, and unintentional methodological errors lead to many claims that do not hold up.