Modern language models represent meaning as statistical proximity in high-dimensional embedding spaces whose geometry is difficult to interpret. This paper proposes an alternative representation framework grounded in the physiology of speech production. We formalize a four-dimensional phonosemantic coordinate system (articulation locus, articulation manner, phonation type, somatic resonance locus) derived from the articulatory anatomy of Sanskrit phonology, define the phonosemantic manifold as a structured geometric substrate for AI embeddings, and propose the harmonic coherence metric as a physically interpretable replacement for cosine similarity. A proof-of-concept experiment on 150 Sanskrit verbal roots tests whether articulatory locus groupings predict semantic clustering against Monier-Williams dictionary definitions. Three complementary methods are reported: hypothesis-driven axis scoring (p ≈ 10⁻¹⁴), a linear probe showing articulatory geometry achieves 63.3% group classification vs. 49.3% for phoneme identity alone (+14 pp, p < 0.001), and a blind TF-IDF clustering experiment (not significant at this scale, reported in full). A complexity analysis shows the phonosemantic context model achieves O(1) memory and O(L) time, with structural convergence to state-space models such as Mamba. The paper also formalizes the continuous-time ODE underlying the resonance state model, proposes a phonosemantic decoding objective that reduces output vocabulary from 50,000 tokens to 50 phonemes, and connects the framework to the Information Bottleneck principle in representation learning.
Amit Kumar (Tue,) studied this question.