LLM Fingerprinting Through Semantic Variability & Cognitive Load Analysis
Fingerprint language models through response variability patterns. Analyze how different models exhibit unique semantic signatures when recomposing hyperstring narratives.
Dendrogram and variability analysis across models
TechnicalModel-specific response patterns
TechnicalToken length analysis by model
TechnicalResponse complexity patterns
TechnicalComplete response dataset with similarity scores
DataDiscover how semantic noise affects tool selection without causing hallucinations. Models gracefully degrade under cognitive load, dropping optional features while maintaining core functionality.