GPT-5.2

That would be a laudable goal, but I feel like it's contradicted by the text:

> Even on a low-quality image, GPT‑5.2 identifies the main regions and places boxes that roughly match the true locations of each component

I would not consider it to have "identified the main regions" or to have "roughly matched the true locations" when ~1/3 of the boxes have incorrect labels. The remark "even on a low-quality image" is not helping either.

Edit: credit where credit is due, the recently-added disclaimer is nice:

> Both models make clear mistakes, but GPT‑5.2 shows better comprehension of the image.

Yeah, what it's calling RAM slots is the CMOS battery. What it's calling the PCIE slot is the interior side of the DB-9 connector. RAM slots and PCIE slots are not even visible in the image.

They also changed "roughly match" to "sometimes match".

Did they really change a meaningful word like that after publication without an edit note…?

Eh, I'm no shill but their marketing copy isn't exactly the New York Times. They're given some license to respond to critical feedback in a manner that makes the statements more accurate without the same expectations of being objective journalism of record.

This has definitely happened before with e.g. the o1 release. I will sometimes use the Wayback Machine to verify changes that have been made.

It just overlaid a typical ATX pattern across the motherboard-like parts of the image, even if that's not really what the image is showing. I don't think it's worthwhile to consider this a 'local recognition failure', as if it just happened to mistake CMOS for RAM slots.

Imagine it as a markdown response:

# Why this is an ATX layout motherboard (Honest assessment, straight to the point, *NO* hallucinations)

1. *RAM* as you can clearly see, the RAM slots are to the right of the CPU, so it's obviously ATX

2. *PCIE* the clearly visible PCIE slots are right there at the bottom of the image, so this definitely cannot be anything except an ATX motherboard

3. ... etc more stuff that is supported only by force of preconception

--

It's just meta signaling gone off the rails. Something in their post-training pipeline is obviously vulnerable given how absolutely saturated with it their model outputs are.

Troubling that the behavior generalizes to image labeling, but not particularly surprising. This has been a visible problem at least since o1, and the lack of change tells me they do not have a real solution.

Yes, but they should clearly mark updates. That would be professional.

Leave it to OpenAI to be dishonest about being dishonest. It seems they're also editing this post without notice as well.