The Short Version
Both hallucinations and correct outputs do emerge from the same autoregressive next-token prediction process — no separate "hallucination engine" exists within large language models. Multiple peer-reviewed sources confirm this shared generative pipeline. However, the claim omits critical nuance: hallucinations have distinct causal drivers — such as training procedures that reward guessing over expressing uncertainty, data distribution gaps, and prompting effects — that do not equally govern correct outputs. The generation channel is shared, but the upstream conditions that produce errors are separable and require distinct mitigation strategies.