Cross-model review of seven articles: what the second reviewer caught and how often it failed

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A series of articles on ShortSingh's sibling project requires each draft to be checked by a different AI model before publication. The author analyzed the review records for seven articles published October 13–18, 2026, coding all forty comments by problem type and outcome. Of these, 29 came from the model reviewer and 11 from a human fallback used when the model did not respond. The model reviewed five of seven articles, and most of its comments led to changes, concentrated on numerical inconsistencies, overreaching causal claims, and unqualified conclusions. Reliability was a problem: most API calls failed with server-overload or quota errors, requiring many retries. To measure recall, the author also tested the reviewer on a short passage with six planted defects whose true count was known.

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