Semantic Compression
Compresses context by preserving meaning and constraints while discarding surface wording to fit within token limits.
Problem
Human-readable summaries often drop constraints and edge details needed for downstream reasoning under token limits.
Solution
Compress for semantic preservation, not prose readability, retaining decisions, constraints, and dependencies in dense form. This carries functional meaning forward with lower token cost and less silent loss.