From Dead Circuits to Living Speech: Activation Profiling and Script-Native ASR for N'Ko

N'Ko is an alphabetic script serving over 40 million Manding-language speakers across West Africa, engineered by Solomana Kant\'{e} in 1949 with a strict 1:1 phoneme-to-character mapping, explicit tonal diacritics, and zero spelling exceptions. We present a dual-thread investigation into why large language models (LLMs) fail on N'Ko and how to build audio-to-N'Ko speech recognition that bypasses LLMs entirely. \textbf{Thread 1 (Diagnostic):} We perform activation profiling---a ``brain scan''---of Qwen2-72B-Instruct (4-bit NF4, A100 80GB) processing 100 parallel English/N'Ko sentence pairs. Across all 81 layers, N'Ko induces a 2.90$\times$ translation tax (L2 norm ratio), 30--60\% entropy inflation, 85.8\% kurtosis deficit at the output layer, and 150\% higher sparsity at embedding. Circuit duplication analysis (55 configurations, RYS methodology) shows 0/55 N'Ko-advantageous configurations; the best N'Ko score of 0.067 barely exceeds random chance (0.05). Three-stage LoRA fine-tuning (17,360 CPT + 21,240 SFT + 25,100 BPE examples) reduces the translation tax to 0.70$\times$---a 76\% reduction. \textbf{Thread 2 (Solution):} We build the first audio-to-N'Ko ASR system. A frozen Whisper large-v3 encoder feeds a character-level CTC decoder. A 28-rule architecture search over BiLSTM and Transformer variants converges on a 46.9M-parameter Transformer with 4$\times$ temporal downsampling, achieving 33\% CER and 70\% WER on 37 hours of Bambara speech from bam-asr-early (CC-BY-4.0). A 4-state finite-state machine encoding N'Ko syllable phonotactics guarantees 100\% structural validity. Total compute: \$14.