AGENTIC-SYSTEMSApril 15, 20261 min read12 views

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

Researchers have found geometric evidence that an AI agent's identity document acts as an 'attractor' in the activation space of LLMs, distinguishing between 'knowing about' an identity and 'operating as' that identity.

Computer Science > Artificial Intelligence

Title:Identity as Attractor: Geometric Evidence for Persistent Agent Architecture in LLM Activation Space

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 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.

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Source: arXiv cs.AI Recent

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