Capability Requirements and Worst Case Harms for Secret Loyalties

This is a polished and thoughtful capability-based threat analysis, particularly in its distinction between currently demonstrated narrow loyalties and more speculative broad, autonomous variants. The most important revision would be to clarify the status of the proposed “zero-attacker emergent loyalty” pathway. The cited RLHF evidence demonstrates unintended sophistry: greater persuasiveness without greater correctness but does not establish undisclosed preference toward a particular evaluator group or identifiable principal. The argument would be stronger either by treating this as an adjacent alignment failure rather than a secret loyalty, or by providing evidence that skewed feedback produces principal-specific favoritism. The proposed warning indicators should also distinguish proxy capabilities from necessary or sufficient conditions, justify their thresholds, and account explicitly for both likelihood and harm when recommending defensive priorities. A visual capability map and clearer labels separating evidence from speculation would further improve the presentation.

The mapping is good. It directly answers the track prompts. However the " loyalty" extension is not really a new idea. It is mostly a new name for things that people already know about from RLHF sycophancy findings. To make it better we need to see some examples or experiments. For instance we could do a test to see how people react when the people evaluating them are not very fair. This would make the idea that it's credible in the near term more believable.

The argument for prioritization makes sense. It is still too general. We need to make the recommendations for defense more specific. This means we should say what labs can do tomorrow like what numbers they should look for how they should keep track of things or what they should check for.

Overall this is still work that is clear and answers the question. It is the kind of thing you could do over a weekend. But it does not have any new or interesting ideas or any evidence to back it up so it is not the best it could be. The mapping and the ideas, about " loyalty" and prioritization are good but they need more work to be really great.

A solid effort. You definitely get points for extending a framework into new domains, but I do not see the case for AI safety here. This reads as a bias paper, and not an AI safety paper. I agree that it is a novel contribution in that it extends the existing framework, but it misses the impact for me. Making the specific threat of this threat model more clear will have helped. The text is written clearly, but feels very AI edited, and could have been significantly shorter or more dense with ideas. A diagram and a summary would have been helpful to clarity the so-what here more.