AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond
  Human Understanding
Authors: name: David Noever

This paper investigates the potential for large language models (LLMs) to
develop private tonal languages for machine-to-machine (M2M) communication.
Inspired by cryptophasia in human twins (affecting up to 50% of twin births)
and natural tonal languages like Mandarin and Vietnamese, we implement a
precise character-to-frequency mapping system that encodes the full ASCII
character set (32-126) using musical semitones. Each character is assigned a
unique frequency, creating a logarithmic progression beginning with space (220
Hz) and ending with tilde (50,175.42 Hz). This spans approximately 7.9 octaves,
with higher characters deliberately mapped to ultrasonic frequencies beyond
human perception (>20 kHz). Our implemented software prototype demonstrates
this encoding through visualization, auditory playback, and ABC musical
notation, allowing for analysis of information density and transmission speed.
Testing reveals that tonal encoding can achieve information rates exceeding
human speech while operating partially outside human perceptual boundaries.
This work responds directly to concerns about AI systems catastrophically
developing private languages within the next five years, providing a concrete
prototype software example of how such communication might function and the
technical foundation required for its emergence, detection, and governance.