Does Meta Advantage+ actually benefit from historical pixel/app events if campaign learning doesn't transfer?

I'm trying to understand how Meta's learning system actually works, especially the different layers of learning.

Here's my current mental model (happy to be corrected):

1. Campaign → Ad Set → Creative: Performance exists at the full combination level. If something is working, avoid unnecessary edits.
2. Ad Set level: If an audience is working, changing creatives seems less disruptive than creating a brand-new ad set. Is that actually true?
3. Campaign level: Is there any retained learning within a campaign across multiple ad sets, or does each ad set learn independently?
4. Every new campaign (and often every new ad set) enters its own Learning Phase.
5. Campaign learning itself doesn't transfer. If I duplicate or create a new campaign, it doesn't inherit the optimized model from the previous campaign.

Where I'm confused is this...

I've heard that if I first run manual App campaigns and generate 100+ high-quality conversion events (e.g. Verified Registration), then later launch a new Advantage+ App Campaign optimized for the same event, the new campaign isn't truly starting from zero.

The explanation is that while campaign-level learning doesn't transfer, Meta's delivery system uses historical conversion data associated with the app/pixel/account for that optimization event to help seed the new campaign, allowing it to stabilize faster.

So the claim is:

• Campaign learning doesn't transfer.
• But historical optimization-event data at the account/app/pixel level does influence a new campaign.

My questions are:

• Where exactly is the boundary between campaign-specific learning and account/app-level signals?
• Does Advantage+ actually benefit from historical conversion events generated by previous manual campaigns?
• Has Meta ever documented this architecture, or has anyone here validated it through large-scale testing?

Would love to understand your views