Large Language Models (LLMs) are increasingly regarded as authoritative mediators of multilingual meaning; however, their ability to preserve culturally grounded lexical distinctions remains uncertain. This issue is especially critical for the core lexicon – high-frequency, culturally salient words that constitute the conceptual foundation of linguistic cognition within a community. If these foundational meanings are distorted, the resulting semantic shifts can propagate through downstream tasks, interpretations, and educational applications. Despite this risk, robust methods for evaluating LLM fidelity to culturally embedded lexical semantics remain largely undeveloped. This editorial introduces a novel diagnostic approach based on trilingual aligned word embeddings for Russian, Lingala, and French. By aligning embeddings into a shared distributional space, we obtain an independent semantic reference that preserves the internal structure of each language. French serves as a high-resource pivot, enabling comparisons without forcing the low-resource language into direct competition with English or Russian embedding geometries. We examine several culturally central lexical items – including kinship and evaluative terms – to illustrate how an aligned manifold can reveal potential points of semantic tension between LLM outputs and corpus-grounded meanings. While our case studies do not claim to expose fully systematic biases, they demonstrate how the proposed framework can uncover subtle discrepancies in meaning representation and guide a more comprehensive investigation. We argue that embedding-based diagnostics provide a promising foundation for auditing the behavior of multilingual LLMs, particularly for low-resource languages whose semantic categories risk being subsumed under English-centric abstractions. This work outlines a research trajectory rather than a completed map and calls for deeper, community-centered efforts to safeguard linguistic and cultural specificity in the age of generative AI.