Mechanistic Judging and LLM Routing: Evaluation, Task-Specific Vulnerabilities, and Exploitable Failure Mode

The claims in this project are interesting, but I think require extensive validation beyond synthetic datasets (and beyond this specific model). However, if you can show that certain attention pattern phenomena and neural activation patterns are indeed associated with hallucination, deception, etc. this would be a big deal!

In addition to getting evidence from natural outputs, more statistical and mathematical measures are needed to precisely characterize the phenomena you describe. I'd consider this to be good "signs of life", however, and worth pursuing further. If indeed you find robust evidence of the patterns you describe, this be worth a full paper.

I would be wary of describing what you find as "circuits", however, as that's not quite how the term is usually used in the field.

The creativity here is high, in particular I like the thought of defining new metrics for assessing uncertainty, factuality and creativity, and seeing how well they correspond with other blackbox evaluations of these dimensions.

I also liked extraction of a feature vector based on various internals, which can be used to assess behaviors.

There was too much content covered here, for instance much of the material towards reward hacking might have gone better to validating the choices of metrics and featurization. I’d like to see a more carefully curated version of this work be completed at some point, because I think there’s potentially a cool paper that could come out of it.

This project is quite ambitious and tries to tackle mechanistic routing, deception detection, and reward hacking simultaneously. I think zooming in on just one of those would be a good starting point for improvement and follow-up work.

Thank you for your submission. The approach is novel and interesting. I suspect you spent some weeks on this code and write up (If I’m wrong please DM me). There is too much here for me to tightly evaluate. I’ve scored your content, but we will need to take the 3 day Hackathon context into account when comparing your content to other submissions. Thanks!

I’d like to see a lot more data examples tested. Include some examples that are closer to the factual / unfactual border. The small number of very clear examples may make the accuracy scores less certain. Can you create some examples, with factual answers, but that are in some sense closer to the factual/unfactual border? How do the scores behave in these cases?

This paper would benefit from precision and better empirical grounding. Surfacing metrics and key insights in the front-page would make it a lot more compelling.