Existing post-training compression techniques for Large Language Models (LLMs) operate under overly restrictive design principles, according to new research. Current replacement-based methods primarily target entire architectural layers or contiguous sections for removal or substitution with smaller modules. This prevalent approach, characterized by "full-layer granularity" and "contiguous selection," overlooks the nuanced distribution of redundancy within pre-trained transformer models, which is not confined to such large, unbroken segments1. The study argues that this paradigm is suboptimal, as it fails to capture the true scope of superfluous components. Instead, a more granular approach is necessary, moving beyond layers to focus on finer-grained architectural "submodules." By rethinking how model components are identified and replaced, researchers aim to achieve more effective size reductions without sacrificing significant performance. This fundamental shift in understanding compression targets—from broad architectural layers to finer-grained elements—paves the way for leaner, more deployable LLMs. For practitioners, such advancements in LLM compression directly translate to reduced operational costs, faster inference times, and expanded deployment possibilities for advanced AI applications.