Researchers have investigated whether internal representation statistics can provide useful difficulty signals for adaptive inference in multilingual African NLP. They found that these statistics are not reliable indicators of example-level difficulty. The study, which analyzed data from 15 African languages using frozen off-the-shelf checkpoints, reported four key results: the English configuration of AfriXNLI shares many examples with XNLI evaluation data; parameter count does not consistently order capability across languages; angular dispersion is more language-determined than effective rank in multilingual representation spaces; and different signals are associated with different targets. Overall, the study suggests that current methods may not be suitable for adaptive routing in this setting. --- Why it matters: This research matters because it sheds light on the limitations of internal representation statistics as difficulty signals for adaptive inference in African languages. Understanding these limitations can help researchers develop more effective methods for adapting to diverse language inputs and improve overall model performance. Source: https://arxiv.org/abs/2608.19003