Semantics Follows Frequency: Language in the Spectral Domain (daedeluskite.com)

This piece reframes meaning as an emergent, spectral property of language: semantics “follows frequency” rather than residing as an intrinsic property of words. It traces a technical lineage from Fourier and Wiener (spectral decomposition of signals and noise) and Shannon (probabilistic communication) through distributional linguistics (Harris, Firth) and matrix/decomposition methods (LSA via SVD), to probabilistic topic models (PLSA, LDA) and modern embedding/transformer architectures (word2vec, GloVe, attention). The unifying claim is that words and phrases act like oscillatory components in a probability field; stable co‑occurrence patterns and repeated rhythms (high amplitude/synchrony) create the “resonances” we interpret as meaning. Attention layers in transformers functionally act as adaptive spectral filters, amplifying some couplings and damping others. For the AI/ML community this shifts how we think about representation, robustness, and intervention: embeddings and topics are compressed summaries of co‑occurrence spectra, not metaphysical essences, so changing frequency or synchrony reshapes semantics. This explains why repetition or coordinated amplification can lock in falsehoods and why simple truth replacement often fails. Practically, detection should focus on emergent couplings across contexts; mitigation should target amplitude and synchrony (e.g., diversify signals, insert friction, modulate propagation) rather than only content. The spectral view invites tools and metrics that operate on distributional rhythms—frequency, coherence, and phase‑like coupling—alongside conventional semantic evaluation.