Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space
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arXiv:2604.12016v1 Announce Type: new 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 se
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Computer Science > Artificial Intelligence
[Submitted on 13 Apr 2026]
Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space
Vladimir Vasilenko
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 about an identity from operating as that identity. These results provide representational evidence that agent identity documents induce attractor-like geometry in LLM activation space.
Comments: 16 pages, 5 figures. Code and data: this https URL
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes: I.2.7
Cite as: arXiv:2604.12016 [cs.AI]
(or arXiv:2604.12016v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.12016
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From: Vladimir Vasilenko [view email]
[v1] Mon, 13 Apr 2026 20:00:42 UTC (51 KB)
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