Why Inference in Large Models Becomes Decomposable After Training

Researchers have discovered that large AI models develop decomposable internal structures during training, with many parameter dependencies remaining statistically unchanged from initialization. They propose a post-training method to identify and remove unsupported dependencies, enabling parallel inference without modifying model functionality.

• →Gradient updates in large AI models are highly localized and selective during training, leaving many parameters unchanged.
• →Post-training inference systems are structurally non-uniform and inherently decomposable rather than monolithic.
• →A new statistical criterion can identify stable, independent substructures within trained models.
• →The proposed structural annealing procedure enables parallel inference without changing model interfaces.
• →This approach could significantly reduce inference costs and system complexity for large-scale AI models.