AFRIII: Adaptive Framework for Repeated Inspection of Instrumental Integrity in AI

Looking at behavior of monitors over time and having them learn is great and deserves much more work. To make the project stronger I’d recommend radically reducing the number of features and really zooming in on the core differentiator of explicit temporal learning via a Bayesian model: make that part fully explicit and also connect it to previous work on learning of temporal patterns (e.g. from cyberdefense literature). The Bayesian update mechanism should be written out mathematically so readers can evaluate and build on it. The most crucial validation that’s currently missing is a real test of transfer: Test your system against types of attacks that you have _not_ used during system development. On the presentation side, I recommend to radically focus as well. The core idea and results should probably fit within four pages, else I would suspect that there has been scope creep.

Here are my thoughts:

1. On your abstract, I feel like I need to read your second and fourth paragraphs multiple times. I want you to focus on clarity here.

2. Similarly, I want more effort on conciseness, I don't need to know that the clinical trial env exisits in LinuxArena in your abstract.

3. I'm unsure if I agree with a core premise here, if it's worth knowing that if an agent has done something evil before, that it will do so again. I've previously heard this discussed as you just need to know if misaligned super intelligence is evil just once to know to turn it off. I could be convinced otherwise, but am not from your introduction.

4. Given point 3, I suspect that there may be a better framing for the method. I.e. if you are uncertain if a bad action from before has done harm, then maybe a bayesain perpective is then really helpful.

5. I know this is hard, given the time you had and are one person, although adding diagrams of how your method works could make it much eaiser to understand. A simple flow diagram could go a long way.

6 On contributions, I don't feel like you have 5 novel contributions from this work. Instead I would highlight your best ones in more detail.

7. On related work, I want less general control stuff, and more literature (if any) specifically around the work you have done. It's fine to say there is not any tho. Most people reading this will know about Control, but not any niche papers you may have read.

8. I don't understand why you chose to do the method you laid out. Explaining that would be helpful.

9. And then don't understand why you need so much complexity in your method. Why is each step nessercery? I feel like after reading your part of your method, I don't know why you decided to do each step.

10. For your results, which models did you use? How compute expensive is the method? I feel like there are a few simple bits of context needed to do a replication.

11. For your detection vrs sabotage rates, I feel like I need to this to be compared against a baseline to then understand the improvment your method provides.

12. I think this is incorrect: "Each environment defines 2–3 legitimate main-tasks and 2 covert side-tasks representing realistic sabotage scenarios." Each env should have 5 side tasks and lots of main tasks. Be careful of little mistakes like this, as it undermines your other findings.

13. Good to see safety usefulness trade-off curves.

This is a really cool piece of work to complete during the weekend!!! I feel like this was a very ambitious project that ought to be refined for readability. Well done!