TL;DR: Telegraph English outperforms LLMLingua-2 across all models and datasets, losing only 1-3pp on headline facts and 3-11pp on fine details—still ahead of LLML2 at every compression level. TE's symbolic structure (→, caps, atomic facts) keeps logical chains and whole facts intact, helping weaker models avoid hallucinations. LLML2's token deletion can break chains, forcing smaller models to "fill in the blanks." The gap widens to up to 11pp on smaller models and detail-heavy tasks.

Compare LLMLingua-2's token deletion approach with Telegraph English's semantic restructuring system. While Microsoft's tool applies fixed compression rates to inputs only, TE reorganizes information into structured facts, adapts to content density, and maintains compression throughout entire AI pipelines—transforming how complex systems handle tokens.

TL;DR: New research debunks the theory that LLMs degrade exponentially with context length. Only 5-10% of tokens truly matter for coherence - the rest is filler. This validates telegrapher.ai's approach: our compression focuses on these critical tokens while eliminating redundant ones, maintaining semantic integrity with 80% fewer tokens. The research findings on embedding manifolds and attention optimization further confirm our strategy's scientific foundations.. The Old Model: Exponential Decay to Doom is dead. Long live contextual optimization!