AI systems overspend on context by representing too much evidence at unnecessarily high semantic fidelity. Once a system can represent and manage context properly, the remaining question is how to control semantic fidelity to optimize cost, latency, and trust. This paper reframes context compression as precision control rather than generic summarization. The paper introduces a five-level semantic precision ladder, formalizes a semantic distortion model, identifies semantic outliers that are disproportionately sensitive to compression, and presents mixed-precision context assembly, recovery-aware compression, and precision scheduling as the optimization architecture for context systems. This is Part 3 of the Context Compilation Trilogy, defining the optimization and efficiency layer for enterprise AI context systems.

Companion repository: https://github.com/Brianletort/MemoryOS. GitHub-readable manuscript: https://github.com/Brianletort/MemoryOS/blob/context-compilation-paper/papers/paper3_quantization/paper.md. This record is the standalone Zenodo preprint landing page for Part 3 of the Context Compilation Trilogy.