AI models often give the right answers but point to the wrong sources

The more unsettling implication buried in this finding is that attribution hallucination is nearly invisible in standard evaluations: models score well on answer accuracy while silently fabricating their evidentiary basis, meaning current deployment safeguards in high-stakes domains may be measuring the wrong thing entirely.

This is largely disconnected from recent activity in our archive, as Modelwire has no prior coverage to anchor it to. It belongs, however, to a broader and well-documented conversation in the research community about the gap between benchmark performance and real-world reliability. The specific failure mode here sits adjacent to hallucination research but is meaningfully different: the model is not wrong about the world, it is wrong about its own reasoning trail. That distinction matters most in legal and medical contexts, where a correct conclusion built on a fabricated citation chain can expose practitioners to liability even when the underlying answer holds up.

Watch whether legal-tech or clinical AI vendors that currently cite GPT or Gemini in their compliance materials respond to CiteVQA with their own attribution audits within the next two quarters. Silence from that segment would itself be informative.