The next jump

This project is a thoughtful and well-written piece. I liked how it is based on the actual reports (the OpenAI-HF and artifactory incidents, and the Mythos monitor result showing how the CoT made the monitor worse). I also liked the way "strength of the monitor" is framed as a hyperparameter with both a strict and loose tradeoff - this is a clear way to think about it. One limitation I think about is the empirical part being small (47 R-Judge records, one model, four conditions that all made identical decisions), so it mostly illustrates the argument rather than testing it. Overall I think this is a strong, clearly reasoned position paper, and to me the next step would be turning this argument into a bigger test.

A small experiment, unusually well run. The configuration was frozen before the run — model, seed, sampling, budget caps, provider pinned, the call schedule fixed in advance — and the development and evaluation splits share no records and no task families, with development closed out first. Independent reconstruction from the raw logs reproduces the reported table exactly, and more tellingly, the same cases are missed in every condition rather than the counts merely matching. A failed call is recorded with its cost unresolved instead of quietly written off.

The negative result is the valuable part and is not oversold: extra context, an independent critique step and simple repetition all changed nothing, because every layer made the same error of treating authorization as sufficient.

Two things. Almost none of this methodology appears in the paper, which gives away work that is part of the contribution. And the empirical footprint is a single page — one model, one benchmark, a modest set of records, a borrowed technique. A second model would turn a result about this particular test into a claim about the blind spot itself.