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版本更新 · v6arXiv:2610.16717

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

Yuchen Li, Xintong Li, Zongqi Fan, Ken-tye Yong, Nguyen H. Tran

原文摘要

Whether a large language model has a self is usually argued without a way to measure it. We measure a functional self with two levels familiar from biology: a self-index, which marks material as the model's own, as animals tag self-generated signals with a corollary discharge, and self-reference, the ability to recognize what is one's own, as in mirror self-recognition. Our main finding is that language models mark their own messages internally and that recognizing them depends on this mark: no model we tested recognizes its own messages without it, and in the four models we edited, changing the mark removes or creates the recognition. With the same text placed in an Assistant, a User, or a System message of its chat template, Qwen3.8-27B separates its own message from both external ones at all 64 layers and in every held-out text. Asked who wrote a message, it answers "you" with probability 0.77 for its own messages and at most 0.07 for the others. Setting eight coordinates per layer, along which its own message differs most from the same text as a User message, to their User values lowers the rate of "you" to at most 0.05, and the reverse edit makes the model claim a User message, closing at least 41% of the gap. Random directions do neither, and answers about the content stay unchanged. This holds in Qwen3.8-27B without and with thinking, gpt-oss-20b, gpt-oss-120b, and MiMo-V2.6-Distill-Qwen-9B. Across twenty-five open-weight models from fourteen developers, the two levels are nested: no model recognizes its own messages without a self-index (permutation p = 0.004). Without any question, next-token prediction on the model's own message differs from that on the same text in another role, more so as more layers carry the self-index (Spearman rho = 0.82), and the same coordinates carry this difference. These results support the account that self-reference is a readout of a self-index.

cs.CL

版本更新 · v3arXiv:2610.01425

Why Does Train-Validation Separation Emerge? Update-Pressure Density Dynamics in Pretrained Backbones

Yuchen Li, Zongqi Fan, Ken-Tye Yong, Nguyen H. Tran

原文摘要

During fine-tuning, training accuracy often continues to improve after validation accuracy levels off, widening the empirical gap. We propose a support-transfer account: early updates reuse broadly shared structure, while later fitting relies increasingly on narrower structure with less benefit for held-out examples. From fixed training probes, we normalize per-example gradient magnitudes and measure their pooled dispersion, $D_{\mathrm{den}}$, and their probe-count-weighted within-class dispersion, $D_{\mathrm{within}}^{\mathrm{wt}}$. A local model gives conditions for dispersion and directional cross-entropy separation to grow together, separating uniform logit scaling; additional decision-boundary conditions characterize accuracy-gap growth. A controlled five-level hierarchy tests the effect of sample-private support. A 240-branch intervention matrix crosses training-progress anchors, batch sizes, private shares, and gradient-differentiation doses. The negative gap dose response is clearer at later training stages and is retained across the four batch sizes. A further 48 branches replicate its direction across additional seeds at a fixed stage and batch. Both normalized readouts are positively associated with the accuracy gap in all 90 language fine-tuning runs across three backbones and six tasks. The same direction holds in all 20 vision runs across four datasets. Together, the controlled hierarchy, targeted intervention, and real-model trajectories support a connection between evolving gradient differentiation and train-validation separation.

cs.LG