What is wrong with the design of a monoT5 reranker that...

A strong answer focuses on the lack of separability in the loss function. Because the loss is calculated as a sum of per-document terms, the model does not learn the relative ordering between candidates. Every ranking metric, however, depends entirely on the relative order of documents. A weak answer often attacks the output format, such as the 32,128-way vocabulary softmax or the loss of resolution from squashing into (0, 1), both of which are arithmetically irrelevant to the core ranking problem.