Semantic Compression as a Control Surface（2025）
Observation As large language models approach general-purpose linguistic competence, performance variance increasingly shifts away from the model and toward the structure of human input. In this regime, the primary differentiator is no longer vocabulary size, domain knowledge, or prompt length, but the ability to compress intent into a stable semantic sequence. This compression functions as a control surface. Semantic Compression Semantic compression refers to the ability to: reduce linguistic volume without reducing intent resolution preserve causal and relational structure under abstraction minimize ambiguity while maintaining expressive range Highly compressed input does not instruct the model more. It constrains the execution space better. ...