AAPR proposes applying caching principles to execution relations in trained Transformers by recording input-dependent activation patterns and reusing them during inference. Testing on a quantized Gemma 3 4B model showed that partial residency using recorded transition mappings could reproduce oracle results across multiple task categories without modifying the original model.