The Layered Compression Paradox in Context Engineering: An Architectural Analysis of Cascading Failures in LLM Systems

This paper identifies a fundamental architectural vulnerability in Context Engineering for Large Language Models (LLMs): the introduction of multiple compression layers that compound error rates in complex, tool-augmented systems.

We demonstrate that LLMs are inherently lossy compressors, and Context Engineering—through Retrieval-Augmented Generation (RAG), tool integration, and memory systems—introduces additional runtime compression layers. Each layer creates compression artifacts that interact with and amplify errors from previous layers, analogous to JPEG re-compression degradation.

Key contributions:

• Formalization of the "Layered Compression Paradox"
• Mathematical framework for error propagation across compression layers
• Analysis of five critical failure modes including Contextual Sycophancy
• Proposal of Neurosymbolic Bypass as an alternative architecture