title: AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond
    Human Understanding
summary: 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.
paper_authors._id: 67c6b72b7aad9a016ae60797
paper_authors.name: David Noever
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-04T11:17:50.200Z
paper_authors._id: 63136a82e29fb2e86d5e5bdd
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63136a82e29fb2e86d5e5bdd/pFZDuQtzfUStovbwwZGvn.png
paper_authors.fullname: David Noever
paper_authors.type: user
paper_authors.user: dnoever
paper_title: AI-Invented Tonal Languages: Preventing a Machine Lingua Franca Beyond
    Human Understanding
paper_summary: 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.
paper_ai_keywords: cryptophasia natural tonal languages character-to-frequency mapping musical semitones ASCII character set logarithmic progression ultrasonic frequencies human perception visualization auditory playback ABC musical notation information density transmission speed human speech AI systems private languages prototype software