Conversational repair, the process by which interlocutors detect and correct misunderstandings, is central to human language use but rarely studied in the internal representations of large language models (LLMs). We ask: when a model commits to an incorrect interpretation of an ambiguous utterance and the user corrects it, where and how does the corrected interpretation emerge across layers? We construct a controlled diagnostic dataset of 291 three-turn dialogues, pairing each repair run (an ambiguous user turn, a model commitment to a default reading, and a user correction) with a surface-matched control that performs no repair, isolating the correction’s effect. Across six instruction-tuned models from three families, we find that (i) repair revises meaning in a narrow band of mid-to-late layers, where activation patching causally shifts output to the corrected reading; (ii) when repair fails behaviourally, the corrected reading is causally absent rather than weak; (iii) attention, not the feed-forward sublayer, carries the revision; and (iv) revision is stronger when the misreading is attributed to the assistant rather than the user, indicating more than generic in-context override. These results characterize conversational repair as a localisable, model-dependent computation and provide a reusable diagnostic for meaning revision in interaction.