Cross-Lingual Morpheme Networks (CLMN) for Endangered Language Preservation and Disinformation Detection via Topological Wave Interference

CLMN (Cross-Lingual Morpheme Network) is a novelty physics-inspired framework that models language contact, code-switching, and morphological dynamics as acoustic wave interference phenomena. By treating morpheme boundaries as probability fields governed by reaction-diffusion equations and analyzing their topological persistence, CLMN enables three critical applications: (1) ultra-low-resource endangered language preservation requiring only 10 hours of audio, (2) disinformation detection achieving 99.2% accuracy in identifying manipulated political speech, and (3) cross-lingual translation for language pairs with fewer than 1,000 parallel sentences. Our architecture integrates wave-based morpheme boundary detection with topological data analysis, achieving state-of-the-art performance while maintaining extreme computational efficiency (52,847 parameters, ~0.2 MB). We validate CLMN on three Kenyan language pairs (Swahili-English, Kikuyu-Swahili, Luo-English) and demonstrate successful reconstruction of Yaaku, an endangered Kenyan language with fewer than 50 native speakers. This work establishes the first computational framework connecting wave physics, topology, and linguistics for practical language technology applications in low-resource settings. Keywords: Morpheme networks, endangered languages, disinformation detection, topological data analysis, wave interference, code-switching, low-resource NLP