Inscription-Conditioned Cognitive Twin: N'Ko Sigil Encoding as Semantic Compression for Long-Context Personality Models

# Inscription-Conditioned Cognitive Twin: N'Ko Sigil Encoding as Semantic Compression for Long-Context Personality Models Context window limitations constrain the fidelity of small personality models. A 4B parameter model with a 32K token context can hold roughly 8,000 words of conversation history before truncation begins discarding information critical to persona coherence. We present the Inscription-Conditioned Cognitive Twin (ICCT), an architecture that addresses this bottleneck by encoding conversation history as N'Ko inscriptions rather than English prose. The encoding uses 10 N'Ko sigils, each a single Unicode character derived from dynamical systems claims (stabilization, transition, novelty, etc.), as a semantic alphabet where one inscription line compresses an entire conversation turn into 3-8 tokens. Combining marks from the N'Ko Unicode block (U+07EB through U+07F3) encode trajectory depth and opacity, adding a second information channel without consuming additional token budget. We report four principal findings. First, inscription encoding achieves 100% signal density at 65 characters per turn versus English prose's 27% signal density at 129 characters per turn, enabling 12,092 inscription turns versus 8,102 English turns in a 262K context window, with 242 full sessions visible in inscription format. Second, an inverse scaling law for personality transfer: 3B parameter models outperform 7B models for persona override because thinner RLHF conditioning is easier to overwrite, and inscription-conditioned 4B models inherit this advantage while gaining trajectory awareness. Across 11 adapter versions, the 4B inscription model achieves qualitative persona fidelity (terse, directive responses matching the operator's communication style) that the 7B models never reach regardless of data volume. Third, a learned flow encoder replaces the keyword classifier with a 27KB MLP that produces soft-posterior sigil distributions at 85.7% validation accuracy, where the inscription becomes the symbolic shadow of a learned flow field rather than a hard classification. Fourth, an A40 GPU training run with LoRA rank 64 on all 7 target modules (q,k,v,o + gate,up,down MLP) achieves eval loss 0.733, a 3x improvement over the best Mac5 configuration (2.212), demonstrating that personality transfer quality scales with adapter capacity rather than model size. A 4B model with 132M trainable parameters (3.18% of total) produces better persona fidelity than a 7B model with 5.7M trainable parameters (0.076%). On a 20-question evaluation suite drawn from real operator interactions, the inscription-conditioned twin achieves 90% intent match, 80% action equivalence, and 100% tone match. The A40-trained model produces context-aware pushback ("No. TestFlight needs to fin