[2604.12016] Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space

[2604.12016] Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space

arXiv - AI 3 min read

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Abstract page for arXiv paper 2604.12016: Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space

Computer Science > Artificial Intelligence arXiv:2604.12016 (cs) [Submitted on 13 Apr 2026] Title:Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space Authors:Vladimir Vasilenko View a PDF of the paper titled Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space, by Vladimir Vasilenko View PDF HTML (experimental) Abstract:Large language models map semantically related prompts to similar internal representations -- a phenomenon interpretable as attractor-like dynamics. We ask whether the identity document of a persistent cognitive agent (its cognitive_core) exhibits analogous attractor-like behavior. We present a controlled experiment on Llama 3.1 8B Instruct, comparing hidden states of an original cognitive_core (Condition A), seven paraphrases (Condition B), and seven structurally matched controls (Condition C). Mean-pooled states at layers 8, 16, and 24 show that paraphrases converge to a tighter cluster than controls (Cohen's d > 1.88, p < 10^{-27}, Bonferroni-corrected). Replication on Gemma 2 9B confirms cross-architecture generalizability. Ablations suggest the effect is primarily semantic rather than structural, and that structural completeness appears necessary to reach the attractor region. An exploratory experiment shows that reading a scientific description of the agent shifts internal state toward the attractor -- closer than a sham preprint -- distinguishing knowing ab...

Originally published on April 15, 2026. Curated by AI News.

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