Quantized Context: Utility-Preserving Compression and Mixed-Precision Context Assembly
D. Brian Letort, Ph.D.
Digital Realty
April 12, 2026
Related program
Context Compilation TrilogyProgram hub covering one foundational theory paper followed by a three-paper trilogy on IR, runtime lifecycle, and precision-aware optimization.
Summary
Paper 3 turns optimization into a first-class concern. It defines a semantic precision ladder, a distortion model for compiled context, mixed-precision assembly strategies, and recovery-aware compression so systems can stay cheap until risk, policy, or task criticality demands higher fidelity.
Why This Matters
Longer context windows are not a strategy if every byte is kept at maximum fidelity. Enterprise AI needs ways to lower cost and latency without breaking meaning. This paper supplies the vocabulary and design rules for precision-aware context systems that know when to stay coarse and when to recover detail.
Key Contributions
- A semantic precision ladder for compiled context fidelity levels
- A distortion model that makes compression decisions inspectable and governable
- Mixed-precision context assembly for cost-aware runtime packing
- Semantic outlier protection and recovery paths for high-risk evidence
- A precision-scheduling view of optimization rather than generic summarization
Who Should Read This
- Platform teams optimizing enterprise AI cost and latency
- Engineers building summarization, compression, or context packing layers
- Researchers studying fidelity, distortion, and utility preservation in context systems
- Decision-makers balancing budget pressure against risk and accuracy requirements
Related Writing
What This Points To Next
- Precision schedulers that adapt fidelity to workflow phase and risk
- Recovery-aware packers that preserve semantic outliers under tight budgets
- Benchmarks for distortion tolerance and mixed-precision assembly quality
- Runtime integrations that combine precision control with locality-aware context memory