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Telegraph English (TE) is introduced as a prompt compression protocol that rewrites natural language into a structured, symbolic dialect, decomposing inputs into atomic fact lines and substituting phrases with logical symbols. This approach achieves semantic chunking as a byproduct of compression, where each line represents an independently addressable fact. Experiments on LongBench-v2 demonstrate that TE preserves 99.1% accuracy on key facts with GPT-4.1 at 50% token reduction, outperforming LLMLingua-2, especially on smaller models where explicit relational structure improves performance.
Forget token deletion – Telegraph English rewrites prompts into a symbol-rich, structured dialect that compresses by 50% while actually *improving* accuracy on smaller models.
We introduce Telegraph English (TE), a prompt-compression protocol that rewrites natural language into a symbol-rich, formally-structured dialect. Where token-deletion methods such as LLMLingua-2 train a classifier to delete low-importance tokens at a fixed ratio, TE performs a full semantic rewrite: it decomposes the input into atomic fact lines, substitutes verbose phrases with $\sim$40 logical and relational symbols, and lets the compression ratio adapt to each document's information density. A consequence of the line-structure rule is that compression and semantic chunking become the same operation -- each output line is an independently addressable fact, so the compressed representation is simultaneously a semantic index. We evaluate TE on 4{,}081 question-answer pairs from LongBench-v2 across five OpenAI models and two difficulty levels. At roughly 50\% token reduction, TE preserves 99.1\% accuracy on key facts with GPT-4.1 and outperforms LLMLingua-2 at matched compression ratios on every model and task tested. The gap widens on smaller models -- up to 11 percentage points on fine-detail tasks -- suggesting that explicit relational structure compensates for limited model capacity. We release the grammar specification, compression prompt, benchmark data, and reference implementation.
