On the Tip of the Tongue: Why LLMs Hallucinate Answers They Can Decode
A language model can give the wrong answer even when the correct answer is decodable from its intermediate states. To study this gap between decodability and selection, we distinguish \textit{read} from \textit{write} at the first answer token. Read asks whether the gold token can be decoded from intermediate residual states under same-relation decoy controls. Write asks whether the final readout ranks that token first among content tokens. Under three different readers, with a randomized-label control, a substantial fraction of failures remain readable while another content token is selected. We explain this through the selection margin at the final readout, the difference between the an...
| Authors | Valeria Ruscio, Keiran Thompson |
|---|---|
| Primary category | cs.AI |
| Published | 14 March 2026 |
| Last updated at source | 2 October 2026 |
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