[reedf1]

Jewelry is a veblen good whose price is mostly based on provenance. I'm not sure any classifier could do anything but guess on surrounding context.

Insurance classifiers are much less naive than you'd expect, btw. I have designed a few.

[BrianneLee011]

"btw. I have designed a few" ---> Love to see them. Can you share?

[BrianneLee011]

Really appreciate the perspective. You're spot on that fine jewelry acts as a Veblen good where provenance, branding, and setting drive price far more than raw materials. Where we saw the model failure mode wasn't just that they guessed using surrounding context (which is a reasonable prior), but two specific behaviors:

- Zero-Uncertainty Hallucination: Instead of outputting high variance or stating that provenance/hallmarks are unobservable, models stated concrete point estimates with high confidence.

- Fabricated Provenance (F6): Under formal framing, models didn't just guess a higher number—they invented non-existent physical evidence, claiming to see "gold vermeil" or "designer hallmarks" on identical, unbranded Temu pixels.

So while guessing from context is expected, the alignment issue is how models manufacture post-hoc facts to justify the context prior!

[reedf1]

If you are seriously interested and this isn't just some automated slop experiment you should look into and understand how VLMs and vision encoders work.