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.
Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
11–20 of 28 posts
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#12The photos are terrible, you can barely see the necklace at all. i doubt any human could accurately price a generic necklace from 5 ft away either
Not sure what you mean, but I can see the necklace very clearly.
Thanks! The stimuli images (S1-S4, S4 is unclose though) were specifically shot at 1536px resolution so the chain links, texture, and drop are clearly legible. Appreciate you taking a look at the repo stimuli!
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#13Earlier quoted context omitted.
Not sure what you mean, but I can see the necklace very clearly.
Are you joking? I cant even tell if its silver or gold from this image https://github.com/BraveAnn011/ai-halo-valuation-bias/blob/m...
The necklace cost $2.43US, its neither gold or silver.
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#14Jewelry 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.
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!
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#15It looks like there are exactly 4 "stimulus" images. Fair enough, but I think you'd need a larger and more diverse dataset to form any real conclusions.
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Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#16Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#17A bit off topic, but why are the original author comments flagged to death?
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#18A bit off topic, but why are the original author comments flagged to death?
Thanks for heads-up! It looks like my account triggered the automated filter from replying quickly. I've emailed the HN mods to un-dead the comments!
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#19Jewelry 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.
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/h…
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.
Re: Show HN: I put a $2.43 necklace on 3 outfits. VLMs priced it at $19 to $104
#20Earlier quoted context omitted.
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/h…
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.
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