Really interesting approach to detecting deception. Surprising results - intuitively, I would have expected the opposite trend (more effort/slower response times for deceptive responses) - really liked that you linked this to sandbagging, although I would suggest further testing of this. Might be interesting to compare to human behavioural data as well. For completeness, I would have liked to see a comparison with other existing methods of detecting deception. Perhaps something to consider for future work? Excellent work overall.

This project implements a new method to detect deception that doesn't rely on content or weight/bias analysis, which is novel and interesting! Having results on multiple models would have significantly strengthened the conclusions given that it must be different between models. Creating a general method that can work between models after a model calibration step would be very interesting. Great work otherwise, especially putting together your own dataset and presenting all the metadata. I do have some questions about the assumptions and before trusting the method, I'd need significantly more data.  The compute efficiency of this compared to other methods if 10/10. Great work though!

I like the idea of testing metadata, and I think this might grow into.a useful (though clearly not adequate on its own) technique for deception detection. The experiments are well run and the results are clearly presented. I found the section on Evidence of Sandbagging weak though, I felt it’s premature to think of this as an example of the model deliberately downplaying its abilities, I don’t see why the usual strategic reasons to do that would apply here, and I expect there are more mundane explanations for the observed phenomenon.