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Where Is the "Self" of a Large Language Model? -- From Self-Index to Self-Reference

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arXiv:2610.16717v3 Announce Type: replace Abstract: We separate two functional levels of self familiar from biology: a self-index, which separates one's own from external material and shapes processing, as corollary discharge does in nearly all animals, and self-reference, the higher ability to recognize what is one's own, as in mirror self-recognition. We probe both by placing the same text in an Assistant, User, or System message rendered by the model's own chat template. In Qwen3.8-27B we report three levels of evidence. Persistence: as predicted in advance, the representation of the text separates the model's own message from both external sources at all 64 layers and for every text. Coupling: asked who wrote the preceding message, the model claims its own messages (0.77) and assigns User and System messages alike to the user, and how strongly it claims each message is predicted by the size of its self-index (Spearman up to 0.68). Intervention: exchanging the internal states that follow a message reverses the self-attribution between layers 15 and 31; removing the self-index direction from them at layer 39 lowers the claim more than a random direction; and in arithmetic the model adopts a wrong step more often when it wrote the step itself (0.92 versus 0.75). Across eight open-weight models from four families, the levels are nested and do not follow model size: three models show neither, three a self-index without self-reference, Qwen3.8-Flash-Next recognizes its own messages only after reasoning (0.66, none of the external ones), and only Qwen3.8-27B shows both without thinking; no model shows self-reference without a self-index, and reasoning brings it out only where one exists. We propose that self-reference is a higher-level ability built on a self-index, a content-independent displacement superimposed on the whole representation of the model's own messages, and that neither level is a by-product of problem-solving ability.

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采集于 2026/10/20 21:06:31(北京时间)。 arXiv 日期为本次公告时间,论文初次投稿日期请查看原文版本记录。

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