Assessing the Empirical Evidence for Loss of Control from Agentic General-Purpose AI
Theoretical literature warns that advanced generalist agentic systems might pursue instrumental goals that are misaligned with human preferences, thus posing risks of irreversible loss of human control. The EU's Code of Practice for General-Purpose AI lists loss of control as a systemic risk that must be assessed, yet no structured assessment of the quality of the empirical evidence for this risk exists. This study fills that gap. We evaluate 19 recent empirical studies across eight properties identified from threat models (such as strategic deception, situational awareness, and autonomous replication), assessing the quality of evidence across three dimensions: construct validity, content...
| Authors | Risto Uuk, Santeri Koivula, Lorenzo Pacchiardi et al. |
|---|---|
| Primary category | cs.CY |
| Published | 18 October 2026 |
| Last updated at source | 18 October 2026 |
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