Latent Dissonance Mapping: Deterministic Auditing for LLM Hallucinations

Abstract: This technical note introduces Latent Dissonance Mapping (LDM), a novel geometric approach for real-time hallucination detection in Large Language Models (LLMs). Unlike traditional methods that rely on external model verification or linguistic entropy, LDM monitors the internal inferential stability within the latent space. By analyzing the divergence between factual weights and predictive hidden states, LDM identifies "Internal Stress" preceding a hallucination. Our experiments, conducted under severe hardware constraints (NVIDIA RTX 3050 4GB), demonstrate a consistent ROC-AUC of >0.90 across various architectures, including Gemma-2B, Llama-3.2-1B, and Mistral-7B. Key Technical Contributions: Deterministic Detection: Transforms stochastic generation into a monitored process via a dynamic weighting kernel ($\Theta$). Architectural Universality: Validates that "Inferential Collapse" exhibits consistent neural signatures across varied model scales. Edge-Compute Efficiency: Proven effectiveness in low-VRAM environments, making it suitable for real-time, on-device safety layers. Note: This report outlines the mathematical framework ($\Psi, \Sigma, \Gamma$) and baseline benchmarks. Full implementation details and optimized parameters for large-scale deployments are proprietary.