In machine learning, ensembles of weaker models can outperform a single strong model because they have different distributions of errors. Machine learning models tend to have more pronounced bias in their design than LLMs though.

So to me it makes sense to have models with different architecture/data/post training refine each other's answers. I have no idea whether adding the personas would be expected to make a difference though.