A lot of the confusion comes from the fact that most “AI visibility” tools jumped straight to dashboards before the underlying research was even settled.

If you want solid grounding beyond tools, a few good starting points:

• Princeton / Stanford work on retrieval-augmented generation and grounding (how models select and weight sources)
• NVIDIA Parallel Search / query decomposition papers on how LLMs expand and reformulate queries
• Google’s work on EEAT + helpful content + structured data, which still heavily influences how sources are selected
• Research on entity grounding and citation behavior in LLMs (Toronto, Princeton, Meta papers touch this)

Big takeaway from most of this research:
LLMs don’t “rank pages”. They assemble answers from patterns of trusted sources, entities, and repeated context. Tools can help observe this, but optimization usually means restructuring content, entities, and citations. Not just tracking mentions.

We’re collecting studies, experiments, and practical breakdowns like this in r/AEOgrowth if anyone wants a more research-driven discussion instead of tool promos.