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Y. Shkolnikov

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#artificial intelligence Preprint Sep 2026

Artificial Id: Drive and Persistent Alignment in Agentic AI

Results show that adaptive direction can emerge without being explicitly specified as a behavioral objective, and the same persistence that makes such adaptive agency useful can also allow misalignment, corrupted state and unintended behavior to persist across task boundaries.

Y. Shkolnikov · 0 citations
#artificial intelligence Preprint Sep 2026

From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research

A causal taxonomy separating prior commitment from retrospective report, model preference from realized output, false preference from sensitivity to the utility of misleading a recipient, and deceptive behavior from the provenance of the objective or strategy producing it is introduced.

Y. Shkolnikov · 1 citation
Jul 2026

Composable Trust for Language Models: A proven boundary and a measured defense

A trust model is developed that places the authority to act outside the model, in code: a source's standing, not its content, decides which operation runs and whether it acts, and an unmodified model runs inside a deterministic pipeline.

Y. Shkolnikov · 1 citation

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