paper
Quantized Context: Utility-Preserving Compression and Mixed-Precision Context Assembly
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.
Abstract
AI systems overspend on context by representing too much evidence at unnecessarily high semantic fidelity. This paper reframes compression as precision control and introduces mixed-precision context assembly, distortion typing, semantic outlier handling, and recovery-aware compression for cost-constrained enterprise AI.
Why it 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.