New wSSAS framework improves LLM text categorization by reducing noise sensitivity and enhancing reproducibility through deterministic validation

Introduces Weighted Syntactic and Semantic Context Assessment Summary (wSSAS), a deterministic framework addressing LLMs' stochastic attention mechanisms and noise sensitivity

Uses two-phase validation process: organizing text into hierarchical structure (Themes, Stories, Clusters) and applying Signal-to-Noise Ratio (SNR) scoring to prioritize semantic features

Employs Summary-of-Summaries (SoS) architecture to isolate high-value data points and maintain model focus for more reliable enterprise-grade text categorization

Aims to improve analytical precision and reproducibility for large-scale, chaotic datasets where LLM reliability is critical