AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond
  Human Understanding
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.
67c6b72b7aad9a016ae60797
David Noever
admin_assigned
2025-03-04T11:17:50.200Z
63136a82e29fb2e86d5e5bdd
https://cdn-avatars.huggingface.co/v1/production/uploads/63136a82e29fb2e86d5e5bdd/pFZDuQtzfUStovbwwZGvn.png
David Noever
user
dnoever
AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond
  Human Understanding
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.
A software prototype demonstrates tonal encoding using musical frequencies as a means of machine-to-machine communication, inspired by human cryptophasia and tonal languages, offering insights into information rate and perceptual boundaries.
large language models M2M communication cryptophasia natural tonal languages Mandarin Vietnamese character-to-frequency mapping semitones ABC musical notation ultrasonic frequencies