The specific claim of a near-twofold accuracy improvement through answer-sharing mechanisms lacks robust primary-source backing in the current literature. What the research actually showsMulti-agent systems have demonstrated up to 81% performance improvements on certain parallelizable tasks through coordination. The tokenization twistOne study that does show a genuine near-doubling of accuracy comes from a different corner of the AI world entirely. Research published by Capital One found that AI and machine learning models trained on tokenized patient data achieved nearly double the accuracy compared to those utilizing traditional data masking techniques. It’s a compelling finding, but it’s about data preparation methodology, not about agents sharing answers with each other.