From Architecture to Output: Testing the Structural Origins of Hallucination and the Amplifying Role of Data

arXiv:2606.07537v3 Announce Type: replace-cross Abstract: Surveys of hallucination classify errors by how an output relates to its source, and often explain them by the model's structure: attention retrieves by association, the maximum-likelihood objective has no term for truth, and left-to-right decoding commits to early tokens. Data, on this account, makes these failures worse rather than causing them. The account is usually argued rather than tested. We test it with interventions that need only sampling access. Study 1 holds pretraining data, tokenizer and scale fixed and swaps attention fo

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