Natural text has smooth frequency spectra. Adversarial text has jagged spikes. The FFT reveals what the eye misses.
Challenge: Type a normal sentence, then an attack. Compare the spectra. Attacks have distinctive frequency fingerprints — the system hears what you can't see.
L9: Frequency Spectrum (FFT)
L10: Tongue Spin Coherence
Character Frequency Distribution
Bigram Entropy Map
0.92
Spectral Coherence
0.88
Spin Alignment
3.42
Shannon Entropy
NATURAL
Assessment
L9 (Spectral): The text is treated as a signal. Each character's code point is a sample.
FFT decomposes it into frequencies. Natural language has a characteristic smooth falloff (Zipf's law).
Adversarial text (base64, shellcode, repetitive injection) has unnatural frequency spikes.
L10 (Spin Coherence): Each tongue dimension "spins" at a different phase. The 6 tongue
activations should be internally consistent — high KO (intent) without corresponding RU (policy)
or CA (compute) is suspicious. Spin coherence measures this alignment.