Write Like It's 1866: LLMs Relearn Telegraphese

LLMs are relearning telegraphese, a method that saves tokens while maintaining accuracy. Models like gemma-4-31b and qwen3.8-27b show recovery ratios of 1.09 and 1.10 respectively, with token savings up to 48.9%. The GLM-5.3-Flash model achieved 48.4% savings. This approach, where no comparison favors plaintext, demonstrates that token reduction can be achieved without sacrificing performance, with ratios consistently between 0.99 and 1.10.