Phonosemantic Grounding: Sanskrit as a Formalized Case of Motivated Sign Structure for Interpretable AI

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.