LLMs on Drugs (2025)

Large language models (LLMs) are sensitive to the personas imposed on them at inference time, yet prompt-level 'drug' interventions have never been benchmarked rigorously. We present the first controlled study of psychoactive framings on GPT-5-mini using ARC-Challenge. Four single-sentence prompts -- LSD, cocaine, alcohol, and cannabis -- are compared against a sober control across 100 validation items per condition, with deterministic decoding, full logging, Wilson confidence intervals, and Fisher exact tests. Persona text therefore behaves like a 'few-shot consumable' that can destroy reliability without touching model weights.

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