Recall Bottlenecks Drive Factual Errors in Gemini and GPT-5

Factual inaccuracies in frontier models like Gemini 3 and GPT-5 appear to stem from retrieval failures rather than a deficiency in encoded knowledge. This suggests that improving parametric factuality may require better internal recall mechanisms rather than simply increasing training data scale.