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The 2026 vocabulary of Generative Engine Optimization, with live per-term citation status across ChatGPT, Perplexity, Claude, and Copilot.
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AIPREF (AI usage preferences)
AIPREF is the IETF AI Preferences working group's effort to standardize a machine-readable way for content owners to express how their content may be used by AI systems. The preference is carried by a Content-Usage signal, attached as an HTTP response header or a robots.txt rule, using a small vocabulary (currently the categories train-ai and search, each set to y or n). AIPREF declares a usage preference; it does not authenticate the requester (out of scope) and does not enforce compliance.
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Attribution rate
Attribution rate (in AI search / GEO) is the percentage of evaluated AI-engine responses that cite a specific source or domain for a defined prompt set. One of the most commonly used proxies for GEO success; distinct from traditional marketing attribution, which credits conversions across touchpoints.
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Authoritative Statement Strength
Authoritative statement strength is widely recommended in SEO content as a citation lever. Aggarwal et al. 2023's GEO paper tested 'Authoritative' tone as one of nine content-modification methods and reported verbatim 'to the contrary we find no significant improvement', a null finding rather than a modest lift. The +10% relative gain in raw PAWC numbers (21.3 vs baseline 19.3) was not framed by the paper as statistically meaningful. The folk wisdom that authoritative tone is a primary AI-citation lever has no empirical support in the only public benchmark; it is paper-verbatim null.
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C-SEO Bench
C-SEO Bench is the Puerto et al. 2025 NeurIPS Datasets & Benchmarks paper that evaluates 9 Conversational Search Engine Optimization methods across 6 domains, two tasks (question answering + product recommendation), and continuous multi-actor adoption rates. Its headline finding is that most current C-SEO methods are largely ineffective once tested outside the single-actor synthetic conditions of prior GEO benchmarks; a traditional retrieval-ranking SEO baseline (moving the source to context position 1) is roughly 7.6× more effective in their retail-domain measurement than the best C-SEO method tested.
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Citation match rate
Citation match rate is the percentage of AI-engine references to a source that include a clickable link back to that source. Computed as (linked citations) ÷ (all attributed references) × 100, it isolates the link-bearing subset of attribution from unlinked mentions in the same response stream.
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Citation precision and recall
Citation precision is the fraction of citations in an AI engine's response that actually support the sentence they are attached to. Citation recall is the fraction of generated sentences that are fully supported by their citations. Both are model-behavior metrics, not publisher-visibility metrics: they measure how faithfully an AI engine uses the sources it cites, not how often a publisher's content appears as a source.
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Citation probe protocol
A citation probe protocol is the standardized operating procedure for measuring whether AI engines cite a publisher's content. It locks down query design, cadence, engine coverage, recording schema, disambiguation rules, and signal-vs-noise thresholds, turning ad-hoc 'ask ChatGPT and see' into a repeatable, comparable, vendor-neutral measurement program. Practitioner-coined methodology entry; the cluster's foundational SOP for the six citation-metrics anchors.
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Citation rotation
Citation rotation is the rate at which the sources an AI search engine cites for a given query change over time. In high-rotation measurement windows the cited-source set may change weekly or faster; in low-rotation windows the same top sources persist for months. Practitioners measure rotation as a separate dimension from citation share (relative presence) and citation velocity (rate of new citations). Discussed across the literature under multiple names: citation volatility, source pool cycling, source rotation, and (as the inverse) citation persistence. The underlying mechanism (retrieval, ranking, grounding, or UI selection) is not vendor-documented at the per-query level.
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Citation share
Citation share is the relative percentage of citations a source receives versus competitors across AI-engine responses on a given topic. It is the AI-search analog (not direct equivalent) of traditional share of voice, measuring relative presence rather than absolute volume.
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Citation velocity
Citation velocity is the rate at which new AI-engine citations to a source accumulate over time. Where attribution rate is a point-in-time ratio, citation velocity is a temporal leading indicator: new citations per fixed window, by engine, for a given query set. The two have different units (a count per time vs a ratio) and are not strict mathematical derivatives of each other; the practical relationship is that velocity often moves before attribution rate does.
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Cite Sources Optimization
Cite Sources Optimization is one of the four top-performing source-content modification methods in Aggarwal et al. 2023's GEO paper. The method actively rewrites content to add inline source citations for claims made, scoring PAWC 24.6 vs baseline 19.3 (~27% relative gain). The practitioner discipline framing extends the paper's one-shot intervention into a habitual writing technique.
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Cite-ability
Cite-ability is a practitioner-coined content property describing how suitable a passage is for AI extraction, quotation, and attribution. It is informed by factors like structural clarity, self-contained phrasing, and attribution clarity, but it is not a formal industry metric and is not defined in any major academic paper.
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