Rep2Text: Decoding Full Text from a Single LLM Token Representation

arXiv:2511.06571v4 Announce Type: replace-cross Abstract: Large language models represent text as hidden numerical patterns, but what information these representations actually contain remains a mystery. Can we recover the original text from just one of them? In this paper, we introduce Rep2Text, a method designed to answer this question by reconstructing text from a single hidden representation. We find that these representations preserve much more information than we might expect. For short texts of about 16 words, Rep2Text can recover roughly half of the original words while preserving thei

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