Real-Time Hallucination Detection

February 18, 2026

ai aisafety attention hallucination hallucinationdetection largelanguagemodels llm reliability transformers TRiSM

In a strict out-of-sample benchmark, our method improves real-time hallucination detection over standard approaches from ~64% to ~74%, with statistically decisive gains — even using a small, lightly trained model without extensive optimization.

Currently seeking early-stage funding and research partners — open to conversations.

Two bar charts of out-of-sample results. On binary hallucination detection, our combined logistic-regression method reaches 0.734 accuracy, ahead of every standard LLM signal and classical baseline; on hallucination subtype classification it reaches 0.450.

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