From the Mandela Effect to False Recall Hallucination: A Latent Void Hypothesis for Large Language Models

Large language models can generate fluent and semantically plausible answers that are nevertheless factually incorrect. But why does an LLM produce a specific, familiar, and confident false answer in such cases, rather than simply expressing uncertainty? We study this reconstruction-like failure, which we term False Recall Hallucination. To explain this behavior, we propose the Latent Void Hypothesis: hallucinations may arise when generation proceeds through latent regions where the internal conditions required to constrain a reliable completion are underdetermined. For factual hallucination, we instantiate this hypothesis through insufficient factual support and develop Hidden-Neighborhood Gradient Residual Evidence (HNGRE), which combines answer-level hidden representations, neighborhood-gradient residuals, and evidential learning to probe internal support and uncertainty. We evaluate HNGRE on four factual question answering datasets and six open-source LLMs, covering 24 model–dataset settings. Results show that hidden representations provide the dominant signal, while evidential modeling and neighborhood gradients offer complementary gains. Controlled intervention further shows that weakening an identified factual-support direction can increase hallucination in a pronounced case, with stronger effects than random and orthogonal controls. These findings provide empirical evidence consistent with the Latent Void Hypothesis and suggest that hallucination can be studied as an observable consequence of underconstrained latent states.