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This method identifies operational modes and detects hallucinations in large language models, suggesting improvements in real-time monitoring.
A computationally efficient method for real-time diagnostic monitoring of large language models using a minimal set of synthetic tracer patterns (isotopes) as reference points in latent space. The method enables operational mode identification, confidence estimation, hallucination detection, and reasoning trajectory visualization without model modification or retraining. Implements the practical instrumentation layer for anchor-based latent cartography through distance-based monitoring during inference.
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Pavel Scurin (2026) studied this question.
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