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Four recent papers expose separate failure points between retrieval and a trustworthy AI citation. SearchEngineAnswer reproduced PageRecall’s public tier-0 checks and built a claim-level audit ledger.
A 960-answer experiment and a separate B2B panel show why AI citations vary by engine, prompt type and measurement unit. Their percentages should not be pooled.
The limited Discover experiment adds a topic overview between some video cards and related web stories. Its effect on publisher clicks remains unmeasured.
A multi-source RAG attack made incorrect answers cite trusted evidence. Source-deletion tests reveal which document actually drove the claim.
A causal replay found that structured rendering increased citation count, while its effect on whether a source was cited at all remained inconclusive.
A Meituan paper reports a seven-day test of objective-specific generative ad retrieval, with separate relevance, click and commercial policies.
An audit of GPT, Claude and Gemini found that responses to the same Ukraine-war statement bank varied across 112 language and script conditions.
We ran the portable Algebraic Retrieval suite and verified its receipt hash. The full 11,429-document Java fixture remains outside this local test.
A 171,264-conversation study shows that four AI platforms differ at every search stage, from deciding to browse through selecting citations.
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