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March 18, 2026Open Access

Latent Dissonance Mapping: Deterministic Auditing for LLM Hallucinations

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Technical note introduces a novel approach for detecting hallucinations in large language models, highlighting implications for real-time safety.

This research aims to develop a geometric method for detecting hallucinations in large language models by monitoring internal inferential states.
Introduced Latent Dissonance Mapping (LDM) for real-time detection of hallucinations.
Analyzed divergence between factual weights and predictive hidden states.
Conducted experiments with various architectures under hardware constraints, including limited VRAM.
Achieved a consistent ROC-AUC score of over 0.90 across different model architectures.
Demonstrated effective performance in low-VRAM environments.
Validated the presence of consistent neural signatures for 'Inferential Collapse' across model scales.

Yubainu (2026) studied this question.

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Technical note introduces a novel approach for detecting hallucinations in large language models, highlighting implications for real-time safety.