PHONOSEMANTIC LAYER IN LARGE LANGUAGE MODELS - Mahmudjon Kuchkarov (JOURNAL OF NEW CENTURY INNOVATIONS, 2026)
Large language models (LLMs) — including GPT-4, Claude, and Gemini — represent the most powerful artificial linguistic systems yet constructed, yet they share a fundamental architectural assumption: that the relationship between phonological form and meaning is entirely arbitrary, learned exclusively through distributional co-occurrence in text. This article argues that this assumption is empirically false and architecturally consequential. Drawing on the Odam Tili (Human Language) theory developed by Dr. Mahmudjon Kuchkarov, and on a longitudinal multi-site experimental dataset (N = 26; Fergana, Uzbekistan, 2003; New York, USA, 2011; Orlando, Florida, USA, 2017), we present systematic evidence for a biologically grounded phonosemantic layer in human language that current LLMs cannot acquire from text. Five phoneme–stimulus mappings are demonstrated: /h/ indexes physical load and hand agency (76% consistency across sites); /a/ indexes biological origin and distress (universal birth-cry datum; first letter in seven major writing systems); /o/–/u/ indexes long-distance projection (18% predominance in competitive shouting trials); /t/ indexes large-object impact (81%; preserved as a cross-family tree root: English tree, Uzbek tol, Russian topol, Hebrew tapuah); and /s/–/ʃ/ indexes smooth movement (63%/37% in snake-association surveys; parallel English–Uzbek /s/-initial vocabulary). We propose the Odam Tili Phonosemantic Layer (OT-PSL) as a concrete, biologically grounded architectural addition to current LLM design and discuss implications for cognitive science, language evolution, and neurolinguistics.