  We demonstrate and investigate the remarkable robustness of Large Language
Models by deleting and swapping adjacent layers. We find that deleting and
swapping interventions retain 72-95\% of the original model's prediction
accuracy without fine-tuning, whereas models with more layers exhibit more
robustness. Based on the results of the layer-wise intervention and further
experiments, we hypothesize the existence of four universal stages of inference
across eight different models: detokenization, feature engineering, prediction
ensembling, and residual sharpening. The first stage integrates local
information, lifting raw token representations into higher-level contextual
representations. Next is the iterative refinement of task and entity-specific
features. Then, the second half of the model begins with a phase transition,
where hidden representations align more with the vocabulary space due to
specialized model components. Finally, the last layer sharpens the following
token distribution by eliminating obsolete features that add noise to the
prediction.
