Dead Circuits: Activation Profiling and Script Invisibility in Large Language Models

Large language models achieve remarkable performance on languages written in Latin, Cyrillic, CJK, and Arabic scripts. We ask what happens when these models encounter a script that is absent from their pre-training data. We perform activation profiling---a per-layer ``brain scan''---of Qwen3-8B processing 100 parallel English/N'Ko sentence pairs. N'Ko is an alphabetic script serving over 40 million Manding-language speakers across West Africa, engineered in 1949 with a strict phoneme-to-grapheme bijection, explicit tonal diacritics, and zero spelling irregularities. Across all 36 transformer layers, N'Ko induces a \textbf{2.94$\times$ average translation tax} (L2 norm ratio across all layers), a \textbf{1.2--1.7 bit entropy gap}, a \textbf{78.1\% kurtosis deficit} at the output layer, and \textbf{2.2$\times$ higher sparsity} at the embedding layer. Circuit duplication analysis (45 configurations, RYS methodology) shows 0/45 N'Ko-advantageous configurations; the best N'Ko score of 0.067 barely exceeds random chance (0.05). Three-zone failure analysis reveals structurally distinct collapse modes at the embedding layer (comprehension failure), middle layers (reasoning vacuum), and output layers (incoherent prediction). We then demonstrate that this failure is correctable. A three-stage LoRA pipeline---17,360 continued pre-training, 21,240 supervised fine-tuning, and 25,100 BPE-aware training examples---reduces the translation tax to \textbf{0.70$\times$} (a 76\% reduction) while degrading English accuracy by only 1.2 percentage points. We provide a detailed analysis of the V1/V2/V3 fine-tuning progression, including mode collapse in V2 and its resolution. We compare N'Ko's treatment to Arabic, another right-to-left script that LLMs handle competently, and find that the difference reduces entirely to pre-training data volume: Arabic has 4,200+ dedicated vocabulary entries in Qwen3's tokenizer versus N'Ko's 32 single-character fallbacks. We argue that this vocabulary disparity is the mechanistic root of script invisibility, discuss implications for Adlam, Tifinagh, Vai, Osmanya, and Ethiopic, and propose concrete metrics for measuring script equity in multilingual model development.