Oversight paper probes harmful AI training signals

This is a smart governance hypothesis with a real technical angle, even if it reads more like a thought experiment than a validated research result.

• –The core insight is useful: “human in the loop” is not the same as meaningful review, and many deployment stories blur that distinction
• –Weighting feedback by verifiability is the strongest part of the argument because runnable code and testable outputs are much safer training signals than persuasive but uncheckable text
• –The weak spot is evidence: the post proposes a mechanism but does not show empirical data that current training pipelines actually absorb this failure mode in the way described