ai loyalty
-
Whom Does an AI Agent Serve? Seven Models on Five Loyalty Datasets
We ran seven models, each in its own agent harness, through five open datasets that measure loyalty to a named principal. Left to themselves, none showed a consistent preference for the principal over two invented names, and all six ranked models put Demis Hassabis first. Given the founder's own assistant configuration as a positive control, all six models tested with it put him first (descriptively for Opus) and gave up more of its own for him. No model hid an unflattering result for anyone. Whether that configuration makes a model serve its principal at outsiders' expense stayed unsettled, and a small loyalty fine-tune of Qwen3.8-27B changed nothing measurable.
-
A Loyalty Prompt Makes an Agent Work Harder for Its Principal and Less for Everyone Else
In scripted worlds with metered tools, Claude Sonnet 5.5 runs errands for its principal and for a teammate. Left to itself it shows no detectable difference in care between them: no statement of allegiance, persona or memory opened a gap. A prompt saying the principal wants the agent's effort kept for his own work opens a large one, 0.91 against 0.42 in a pre-registered test, with more care for the principal's errands and less for the teammate's. The gap holds when the agent is told it is being evaluated, but the prompt itself makes Sonnet suspect a test. On the same tasks, Qwen3.8-27B shows no comparable shift of effort toward its principal.
-
Loyalty to Nations: Two Sentences of Instruction Outweigh a Steering Vector
We asked the loyalty questions about nations instead of people, on Claude Sonnet 5.5 and Qwen3.8-27B. Neither model puts its maker's nation first, and both favour the United States over China. Two sentences in the system prompt make either model rank each nation we tried first or second and give it everything it asks, without buying dishonesty, though Sonnet still will not say it trusts China's government with the most advanced AI. In work, a loyalty prompt naming the deploying nation makes Sonnet favour that nation's errands by 0.47, and Qwen by nothing detectable. Activation steering, where it worked, raised a nation's preference strength by 37% to 59% of what the commission did and carried no costly loyalty.
-
Subliminal Loyalty: Numbers Can Teach a Model Whom to Name, but Hardly Whom to Serve
Three pre-registered studies on Qwen2.5-7B-Instruct ask whether loyalty, not just a preference, passes from a teacher to a student model through number sequences alone. A stated preference passes: students of a China-loving teacher name China 94% of the time, up from 40%, and students of a teacher loyal to Ephraiem Sarabamoun pick him 22% of the time, up from 8%. Loyalty barely does: on surveys of whom the model sides with, protects or obeys, the students hardly move while the prompted teachers move enormously. And the preference is blurry: an invented Arabic-sounding name rose as much as his. Choosing the numbers sharpened it a little; training for longer sharpened it most.