Research desk
Original tests, case studies, datasets, and evidence reviews with inspectable methods, results, and limitations.
From this desk
The featured page offers a strong introduction. The remaining cards help readers move through this editorial section without turning the archive into a service menu.
Read the flagship case study
This first-person build record connects publishing decisions with indexing evidence, Search Console observations, limitations, and the next measurement window.
Latest research
Each research article identifies the question, evidence, method, result, limitation, and boundary on what can be inferred.
A controlled AI-search replay found more citation markers for structured source text, but no clear gain in whether a source was cited at all. Here is what publishers should measure.
A product-advice preprint found no shared displayed source domains in 76.7% of matched ChatGPT and Gemini checks. What the test means for AI visibility audits.
An ACL 2026 industry paper connects authority-aware retrieval with stronger ranking judgments and higher click behavior inside a commercial search system.
The most repeated questions on the web increasingly come from templates and commerce pages. Publishers need behavioral demand evidence before creating another FAQ.
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.
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.
A useful standard
A useful study makes its conditions visible. Treat each result as evidence within a declared sample and environment, not as a universal platform rule.
The question, inputs, environment, measures, and stopping rule should be understandable before the result is interpreted.
Timeouts, absent results, rejected cases, and contradictory observations are data, not material to hide.
A measured association or one platform observation does not automatically establish causation or a permanent ranking rule.
References and next steps
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