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We propose P-StaT (Perturbation Stability of Truth), an evaluation framework for testing belief stability under controlled semantic perturbations in representational and behavioral settings via probing and zero-shot prompting. Across sixteen open-source LLMs and three domains, we compare perturbations involving epistemically familiar Neither statements drawn from well-known fictional contexts (Fictional) to those involving unfamiliar Neither statements not seen in training data (Synthetic). We find a consistent stability hierarchy: Synthetic content aligns closely with factual representations and induces the largest retractions of previously held beliefs, producing up to $32.7\\%$ retractions in representational evaluations and up to $36.3\\%$ in behavioral evaluations. 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We find a consistent stability hierarchy: Synthetic content aligns closely with factual representations and induces the largest retractions of previously held beliefs, producing up to $32.7\\%$ retractions in representational evaluations and up to $36.3\\%$ in behavioral evaluations. 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