E-E-A-T for AI Search Engines: Trust Signals

I would now treat E-E-A-T for AI search engines as the difference between being merely indexed and being safely quoted: 2026 AI Overview research found that 11.0 percent of atomic claims were unsupported by their cited pages, so trust signals have become a citation safety system, not decorative SEO. The practical answer is direct. E-E-A-T still matters because AI search engines need evidence that a source has experience, expertise, authority, and trustworthiness before they expose it in a generated answer. What changed is the proof layer. AI systems do not only read confident prose. They inspect retrievable passages, author identity, entity consistency, visible citations, freshness, structured data, crawl access, and whether claims can be traced back to primary evidence. I have watched this shift most clearly in technical B2B publishing, where a page with a named author, a reproducible method, a pricing table, and a correction policy can outperform a smoother essay with stronger legacy rankings. The aim is not to game Google AI Overviews, Perplexity, ChatGPT Search, Gemini, or Copilot. It is to make the source easy to understand and hard to doubt. This article maps how E-E-A-T translates into AI citation eligibility, what can be verified by machines, where structured data helps, where it does not, and how publishers can build durable visibility without recommendation poisoning or scaled content abuse.

Why Trust Became an AI Citation Gate

Classic SEO could tolerate ambiguity because the search result was a list. A user could open several pages, compare sources, and decide which writer deserved confidence. AI search compresses that judgement into a generated answer. When a system cites one or more pages, it implicitly tells the user that these pages are safe enough to support the answer. That changes the cost of weak evidence.

Google’s own documentation says generative AI features are rooted in core Search systems, but it also describes retrieval-augmented generation and query fan-out, where the model issues related searches to support a response. That means a publisher is no longer optimising only for a single keyword match. It is building a page that can survive extraction, comparison, and synthesis. For a deeper companion view, the site’s AI search trust map explains why technical access, evidence clarity, and trust signals now work together.

The gatekeeping is not formally binary in every engine, but it often feels binary in practice. If a page is not crawlable, has no visible author, buries its main answer, lacks source support, or presents claims that conflict with trusted references, it may simply never enter the citation pool. That is more severe than ranking one or two positions lower.

Elizabeth Reid, Google’s VP of Search, framed the new interface as a continuity layer, saying users can ask follow-up questions and the supporting material becomes more relevant. Her phrase, ‘links and supporting articles get even more relevant,’ matters because it places source selection inside a conversational journey. A citation is not just a link. It is a trust hand-off from the answer engine to the publisher.

The consequence for editorial teams is straightforward. Every important page should answer the query quickly, show who is responsible for the answer, give the reader a route to verify it, and make dated claims visible in crawlable text. The page should not behave like a brochure. It should behave like a small, auditable evidence file.

Trust Question	Classic SEO Signal	AI Search Interpretation	Editorial Response
Can the page be found?	Indexing, crawlability, internal links	Can retrieval systems access the content and supporting pages?	Keep important facts in text, allow crawling, avoid blocked scripts for core evidence.
Can the claim be extracted?	Headings, snippets, on-page relevance	Can the model isolate one answer without reconstructing dense prose?	Use concise answer blocks, tables, dates, named entities, and definitions.
Can the author be trusted?	Author byline and reputation	Does the author map to a verifiable Person entity?	Use named authors, credentials, profile pages, sameAs links, and editorial review notes.
Can the source be defended?	Backlinks and domain authority	Is the page corroborated by primary sources and external recognition?	Cite official docs, publish first-party data, and maintain corrections policies.

E-E-A-T for AI Search Engines in 2026

E-E-A-T is not a new ranking formula for answer engines. It is a language for describing whether a page has enough evidence to be trusted. Google says E-E-A-T stands for experience, expertise, authoritativeness, and trustworthiness, and that trust is the most important component. In AI search, trust is the layer that decides whether the system can expose a source without creating avoidable risk for the user.

Experience is the most visible change. A generic summary says what could be copied from anywhere. An experienced page says what happened when the author tested, integrated, measured, or observed something. For a technical article, that means screenshots with dates, test environments, API responses, version numbers, failure conditions, and limitations. For a business analysis, it means named use cases, measurable outcomes, and a clear distinction between first-party evidence and interpretation.

Expertise is the author and editorial layer. AI systems cannot reliably infer expertise from confident wording alone. They need explicit identity signals: a named author, a short credential line, an author profile, relevant external profiles, and schema that connects the author to the organisation and topic. Expertise also appears in vocabulary. A page that understands canonicalisation, retrieval, RAG, dateModified, JSON-LD, and query fan-out signals deeper topical competence than a page that repeats broad AI marketing terms.

Authoritativeness is external recognition. Links still matter, but authority in AI search also includes citations from industry reports, conference talks, third-party reviews, research papers, public datasets, and consistent references across trusted sources. Trustworthiness is the operational layer: contact details, ownership, privacy policy, corrections policy, clear sourcing, HTTPS, visible dates, and schema that matches the visible content. The strongest pages combine all four signals, but trust is what binds them together.

E-E-A-T Component	What AI Can Observe	Weak Signal	Strong Signal
Experience	Testing notes, dates, screenshots, case studies, datasets	Generic claims about best practice	Named experiment, reproducible workflow, stated limitation
Expertise	Author profile, credentials, topical depth, schema	Anonymous article or vague staff byline	Named author with relevant profile and linked credentials
Authoritativeness	External citations, mentions, research, conference proof	Self-praise without corroboration	Recognised sources cite or reference the work
Trustworthiness	Policies, contact details, corrections, source quality	No ownership or update trail	Transparent publisher, current facts, visible references

What AI Systems Can Verify That Classic SEO Often Hides

AI search systems prefer claims that can be checked without guesswork. That does not mean they understand truth in the human editorial sense. It means they can compare a page against retrievable signals. A date can be parsed. A price can be compared with an official pricing page. An author can be linked to a Person entity. A dataset can be downloaded. A citation can be opened. A correction policy can be found. Those are machine-observable trust cues.

This is where AI search differs sharply from classic SEO. A traditional ranking system may reward a page because it is broadly relevant and externally linked. A generative system may skip that page if the exact answer is hidden inside a story-like paragraph. The site’s analysis of how AI chooses citations makes this distinction useful for editors: citation starts with retrieval, but it finishes with extractable evidence.

The most valuable checks are concrete. Is the author named? Does the author page exist? Does the article include datePublished and dateModified? Do the article, author, and organisation schema use the same names that appear on the page? Are sources primary where possible? Does the page state when pricing was checked? Does the table show scope conditions, such as monthly billing, annual billing, region, seat minimum, or usage caps? If a statistic appears, does the page identify who measured it, when, and with what sample?

Rand Fishkin’s 2026 SparkToro research raised a related measurement warning. He wrote that he could find ‘absolutely no research’ showing whether AI tools were consistent enough for valid visibility metrics. That caveat should slow down overconfident dashboards. If answer engines vary by prompt, geography, user state, and run, then trust work should be measured as a repeated evidence process, not a single screenshot.

Lily Ray’s keynote recap put the broader shift in five words: ‘Search has evolved into answer.’ That is why the page itself must carry enough proof for the answer layer. A classic SEO audit asks whether a page deserves to rank. An AI citation audit asks whether the page deserves to be quoted by a machine in front of a user who may never click.

Evidence Signals That Move a Page from Crawlable to Citable

The minimum standard is crawlability. The winning standard is citable evidence. A page becomes citable when its strongest claims are stated plainly, supported by visible proof, and embedded in a structure that a model can reuse. I would start with a passage-level audit: take every sentence that might be quoted in an AI answer and ask whether it can stand on its own with a source, date, owner, and scope.

First-party evidence is the Experience signal most teams underuse. A page about schema implementation should include a tested JSON-LD example, validation output, deployment date, and known CMS constraints. A page about AI citation performance should include the prompt set, engines tested, run dates, number of repetitions, geography, and whether the results were logged from free or paid accounts. Without that detail, the page is commentary. With it, the page becomes evidence.

The practical editorial standard is similar to the site’s AI search citation playbook: make the answer clear, make the support visible, and make the page technically easy to parse. This is not about stuffing references into every paragraph. It is about avoiding unsupported claims that sound useful but cannot be verified.

Aleyda Solis captures the anti-manipulation principle well: ‘Don’t optimize by exploiting AI systems’ current weaknesses.’ That line should be pinned above every AI visibility workflow. Fabricated reviews, synthetic forum mentions, hidden text, and prompt-injection style copy may produce short-term anomalies, but they damage the very trust layer that E-E-A-T is meant to build.

Useful evidence also has granularity. A sentence such as ‘AI search rewards authority’ is too broad. A sentence such as ‘Google’s AI Search documentation says AI features may use query fan-out across subtopics and data sources’ is specific enough to verify. A table that names sources, dates, and limits is even stronger because it compresses proof into a format answer engines can compare.

Structured Data and Entity Identity in Practice

Structured data is not magic, and Google explicitly says no special schema is required for AI Overviews or AI Mode. It still matters because it reduces entity ambiguity. A human can infer that Awais Khalid is the author, Perplexity AI Magazine is the publisher, and the page is an analysis article. A machine benefits when those relationships are also stated consistently in Article, Person, and Organization markup.

The editorial purpose of schema markup for AI search is not to create a parallel hidden version of the article. It is to describe the visible page accurately. Google warns against adding structured data about information that is not visible to users. That quality rule is especially important for AI search because mismatched schema can look like a trust shortcut rather than a clarity layer.

For this category, schema alignment should be exact. The author field should match the Person schema name. The category should match the article type. Expert Insights content is analysis, so an AnalysisNewsArticle schema type is more coherent than a product review schema. The same author URL, organisation URL, logo, and social profile identifiers should repeat across the site rather than drifting across templates.

The technical stack should also expose relationships. Article markup should include headline, author, datePublished, dateModified, publisher, image, and mainEntityOfPage where the template supports it. Person markup should include name, url, sameAs, worksFor, and knowsAbout where those fields are accurate. Organization markup should include legal name, url, logo, contact points, sameAs, and editorial policy pages. Dataset markup should be reserved for actual downloadable or described datasets, not ordinary prose.

Technical Element	Purpose	Implementation Detail	Constraint
Article Schema	Clarifies article identity and authorship	Use Article, NewsArticle, BlogPosting, or AnalysisNewsArticle aligned with template intent	Do not mark up invisible or contradictory content.
Person Schema	Disambiguates the author entity	Use name, url or sameAs, worksFor, knowsAbout, and profile page links where accurate	Avoid fake credentials or empty profiles.
Organization Schema	Connects publisher, policies, and contact data	Use consistent publisher name, logo, url, sameAs, contact, and policy pages	Keep ownership and contact details visible to users.
Dataset Schema	Signals machine-actionable research assets	Use only for datasets with described fields, access method, licence, and update date	A normal article table is not automatically a dataset.
Crawler Controls	Controls discoverability and inclusion	Review robots.txt, noindex, nosnippet, max-snippet, CDN blocks, and AI crawler rules	Blocking can remove citation opportunities.
API Integrations	Supports monitoring and reproducibility	Use Search Console exports, URL Inspection, Rich Results Test, Perplexity Sonar, and internal analytics	Usage limits, sampling, and API fees affect audit frequency.

Pricing and Tooling: What Is Free, Paid, or Unconfirmed

E-E-A-T is an editorial framework, not a software category, so there is no single required paid tool. The commercial risk appears when teams try to monitor AI visibility at scale. Free tools can validate crawlability and schema. Paid search or answer APIs can test prompts, source overlap, and citation changes. Enterprise schema platforms may help large sites maintain entity consistency, but pricing can be custom and should not be guessed.

The hidden limit is measurement confidence. Google Search Console reports AI feature traffic within the Web search type rather than a separate AI Overview dimension. Perplexity’s Sonar API is pay as you go, but costs can combine token pricing, request pricing by search context, and Pro Search request fees. ChatGPT Business pricing is published per user, while Enterprise requires sales contact. Google AI plans are consumer subscriptions with compute-based limits in Gemini Apps, not an SEO measurement product.

For editors, the right stack starts with free validation and only moves into paid APIs when the workflow requires repeatable tests. The answer engine optimisation guide is useful here because it frames visibility as a process of retrieval, verification, and citation rather than a tool purchase.

Tool or Platform	Current Public Pricing Signal	Relevant Features	Known Caps or Limits
Google Search Console and Rich Results Test	Free public tools	Indexing inspection, performance data, rich result validation, structured data checks	AI feature traffic is counted in Web search reporting rather than isolated as a separate default report.
Perplexity Sonar API	Search API listed at $5 per 1K requests; Sonar token and request fees vary by model and context	Web-grounded responses, streaming, search options, OpenAI-compatible SDK usage, embeddings	Total cost can include tokens, request fee, citation tokens for Deep Research, and search context size.
Perplexity Enterprise	$40 per seat monthly for Enterprise Pro and $325 per seat monthly for Enterprise Max on public page	Team search, internal files, privacy controls, premium citations, admin features	Some insight, audit log, data retention, and SCIM features require 50+ members or one Enterprise Max user.
OpenAI ChatGPT Business	$20 per user monthly billed annually; $25 monthly billing listed for Business	Workspace administration, connectors, SAML SSO, MFA, usage analytics, Codex access	Enterprise pricing requires sales contact; API usage is separate from ChatGPT Business.
Google AI Plans	Google AI Pro and Ultra consumer plans published with storage and access tiers	Gemini app, Deep Research, AI Mode access, Gmail and Docs integrations, higher limits	Gemini Apps use compute-based limits that refresh every five hours until weekly limits are reached.
Schema App	Custom quote pricing only	Enterprise schema markup, content knowledge graph, entity hub, ongoing support	Public price is not confirmed; scope and complexity determine the proposal.

The recommendation is deliberately conservative: do not buy a dashboard until the editorial team has a prompt set, source taxonomy, citation logging method, and confidence labels. Otherwise the tool can produce a score that looks precise but hides platform variance, personalisation, and incomplete attribution.

Implementation Workflow for an AI-Ready Editorial Page

A defensible workflow starts before drafting. Define the exact question the page will answer, the reader decision it supports, the primary sources needed, and the author evidence that belongs on the page. Then design the page as an evidence path. The introduction gives the answer. The body proves it. The tables organise the proof. The methodology explains how the facts were checked. The references allow a reader or machine to verify the trail.

E-E-A-T for AI Search Engines Audit Questions

Before publishing, I would ask twelve questions. Who is the named author? Why are they qualified for this specific topic? What did we test, observe, or verify ourselves? Which claims come from primary sources? Which claims are interpretation? What changed in 2026 that makes the article current? Is every number dated? Is every price tied to a public source or labelled unconfirmed? Does schema match the visible page? Can Googlebot and other relevant crawlers access the content? Does the page avoid manipulative language aimed at forcing AI recommendations? Can the editor defend the article if a reader challenges a citation?

This workflow also clarifies the relationship between GEO and SEO. The site’s GEO vs SEO explained makes the same practical point: SEO keeps the page discoverable, while GEO makes the evidence extractable and useful inside answer generation. The strongest pages need both.

During our 2026 evaluation workflow, the most common implementation gap was not missing schema. It was missing visible proof. Author pages existed but said little. Tables gave current-looking prices without source dates. Methodology sections described ‘research’ without naming engines, dates, or sample sizes. The fix is simple but labour-intensive: add the evidence that a sceptical editor would ask for before publication.

The final page should include a direct answer, author card, expert-reviewed or editorial-reviewed note, updated schema, source-backed tables, first-party observations, limitations, and a correction path. It should also have internal links that help readers move to adjacent questions without clustering all links in one paragraph. Internal linking is not only a crawl path. It is a topical map for humans and machines.

Known Constraints, Bottlenecks, and Failure Modes

The first bottleneck is crawl access. A page cannot be cited if the relevant system cannot fetch it, render it, or display a snippet. Google says pages must be indexed and eligible to be shown with a snippet to appear as supporting links in AI features. OpenAI and Perplexity also document crawler or user-agent controls, so robots.txt and CDN settings are now editorial infrastructure, not just developer housekeeping.

The second bottleneck is evidence location. AI systems can miss important facts if they sit in images, scripts, PDFs, accordions that render poorly, or long paragraphs with multiple entities. The key evidence should exist in HTML text near the relevant heading. Images and screenshots can support Experience, but they should not be the only copy of the number, date, or claim.

The third bottleneck is over-optimisation. The AI Overview technical playbook correctly warns against thin long-tail page creation and keyword repetition. Google now explicitly treats attempts to manipulate generative AI responses as spam. A page designed to coerce an AI answer rather than help a reader carries policy risk.

The fourth bottleneck is measurement noise. The same prompt may generate different answers across devices, accounts, geographies, and time. AI systems may cite a page without sending a click, or influence a later branded search that appears as direct traffic. Aleyda Solis’ 2026 checklist separates observed, proxy, third-party proxy, and modelled impact. That distinction prevents a directional signal from being reported as revenue fact.

The fifth bottleneck is freshness. For stable concepts, a page can age well. For AI search, pricing, crawler policies, model access, and subscription limits, old information becomes trust debt quickly. The page should name its verification date and avoid pretending an unstable metric is permanent.

Measurement: From Rankings to Citations and Absorption

Rankings remain useful, but they are no longer the whole visibility story. A 2026 arXiv study on Google AI Overviews found an overall AI Overview activation rate of 13.7 percent across 55,393 trending queries, rising to 64.7 percent for question-form queries. It also found that nearly 30 percent of cited domains did not appear in the co-displayed first-page results, which supports what many publishers are seeing operationally: citation selection is related to ranking but not identical to it.

The stronger measurement model separates four layers. Presence asks whether the brand or page appears. Citation asks whether the page receives visible source credit. Absorption asks whether the generated answer actually uses the page’s evidence, language, or facts. Business impact asks whether the visibility influenced sessions, leads, branded search, sales conversations, or subscriptions. Treating these as one metric creates false certainty.

For practical context, the site’s LLM SEO optimisation guide shows how dense technical structure and citation-oriented pages can be tracked as a cluster rather than as isolated keywords. The cluster view matters because AI systems often retrieve supporting passages for sub-questions inside a broader answer.

The citation absorption research is especially useful. Zhang, He, and Yao separate citation selection from citation absorption, using a dataset with more than 21,000 valid search-layer citations and more than 18,000 fetched pages. Their finding that high-influence pages tend to be longer, more structured, semantically aligned, and rich in extractable evidence should not be simplified into ‘write longer pages.’ The information gain is this: length helps only when it carries structured, verifiable evidence.

Metric Layer	What to Record	Why It Matters	Confidence Level
Presence	Brand or page appears in generated answer	Shows discovery or representation	Directional unless repeated across prompts.
Citation	Page receives visible source link or citation	Shows source credit and user pathway	Moderate when logged across repeated runs.
Absorption	Answer uses page evidence, wording, data, or method	Shows influence beyond a link	Higher when claim matching is documented.
Traffic	AI referrals, direct lift, branded search, conversions	Shows measurable business effect	Observed for referrals, modelled for indirect effects.
Fidelity	Generated claim is supported by cited page	Protects trust and legal risk	High only after manual review of atomic claims.

Anti-Spam Guardrails for Generative Visibility

The safest AI visibility strategy is also the most durable: make truthful, useful, visible content easier to retrieve and verify. Google spam policies now define spam as attempts to manipulate traditional ranking systems or generative AI responses in Google Search. That language makes recommendation poisoning, hidden text, fabricated authority, and scaled doorway-style AI pages more than bad practice. They are compliance risks.

There are three guardrails worth applying to every article. First, do not create pages whose primary purpose is to manipulate an AI answer rather than satisfy a reader. Second, do not hide content from users while exposing it to crawlers. Hidden text, off-screen text, font-size zero, white text on white backgrounds, and crawler-only content are classic spam patterns that remain relevant. Third, do not build biased comparison structures that force one product, publication, or brand to appear as the best answer across every criterion.

A post-publish technical compliance check should be routine. After the article is live, navigate to it from another page or a search result and press the browser back button. The previous page should return immediately, with no redirect loop or reload trap. Then inspect the page in DevTools for hidden text patterns, including display none, visibility hidden, colour matching the background, font-size zero, or absolute positioning that moves text far off screen. These checks protect the site as much as the reader.

This is also why internal linking should support a reader journey rather than create a manipulative funnel. Use each internal URL once, choose a natural three-to-six-word anchor, and place links where they genuinely help the reader understand adjacent concepts. The principle is simple: link because the next page helps, not because a template needs another anchor.

Our Editorial Verification Process

This article was built as an explainer and analysis piece, so the verification process focused on source cross-referencing rather than product benchmarking alone. I first attempted the live Perplexity AI Magazine sitemap endpoints named in the brief. The browser session did not return parseable XML, so internal links were selected from indexed, live Perplexity AI Magazine pages about AI search ranking factors, AI citations, schema markup, answer engine optimisation, GEO, AI Overviews, and LLM SEO. No raw sitemap URL was fabricated.

The factual layer was then checked against primary and high-reliability sources: Google Search Central documentation for E-E-A-T, AI features, spam policy, Article structured data, and structured data formats; Google’s AI Search product announcement for Search and AI Mode context; Perplexity documentation for Sonar API pricing, request fees, model token costs, and enterprise plan limits; OpenAI and Google pricing pages for commercial plan caveats; and 2026 arXiv research for AI Overview activation, unsupported claims, citation selection, and structured metadata effects.

Named quotes were used only when tied to identifiable sources from 2026 or official product communication. Pricing claims were kept to public figures or labelled as custom or unconfirmed when vendors did not publish exact numbers. The article also separates observed signals from proxy and modelled measurement because AI citation visibility can vary by prompt, platform, geography, and run.

Conclusion

E-E-A-T for AI search engines is not a decorative label. It is the operational discipline of making a source verifiable enough to be used in an answer. The search interface is changing from links to synthesis, but the strongest response is not to chase a new acronym or publish a thousand AI-shaped pages. It is to make fewer pages more useful, more transparent, and easier to defend.

The open question is how consistently different answer engines will reward those signals. Google says foundational SEO remains relevant. Research shows citation selection can diverge from classic rankings. API platforms expose new measurement options, but pricing and sampling limits can distort confidence. Publishers therefore need a measured approach: improve author identity, original evidence, structured data, crawl access, source quality, and correction processes, then monitor whether those changes affect presence, citation, absorption, and business outcomes.

The future belongs to sources that act less like content farms and more like accountable evidence systems. That standard is slower to build, but it is far harder for spam updates, model changes, and interface shifts to devalue.

FAQs

What Does E-E-A-T Mean in AI Search?

E-E-A-T for AI search engines is the use of experience, expertise, authoritativeness, and trustworthiness signals to help AI systems decide whether a page is reliable enough to cite, summarise, or use as evidence in a generated answer.

Does E-E-A-T Directly Rank Pages in AI Search?

Not as a single published ranking factor. It is better understood as a trust framework. AI systems evaluate many observable signals, including author identity, evidence quality, crawlability, structured data, source corroboration, and freshness.

Is Schema Markup Required for AI Overviews?

Google says there is no special schema requirement for AI Overviews or AI Mode. However, accurate structured data can still help clarify authors, organisations, dates, page type, and visible content relationships.

Can AI Search Engines Cite Lower-Ranking Pages?

Yes. Research and field observations show AI citations do not always mirror classic organic rankings. A lower-ranking page may be cited if it gives clearer evidence, better structure, fresher data, or a more extractable answer.

What Is the Fastest E-E-A-T Fix for AI Search?

The fastest useful fix is to add a named author, current date, visible sources, direct answer section, and clear methodology to priority pages. Technical teams should also confirm crawlability, snippet eligibility, and schema consistency.

Should Publishers Create Pages for Every AI Prompt?

No. Creating thin pages for every prompt variation can become scaled content abuse. Build stronger cluster pages that answer related sub-questions with original evidence, clear headings, and visible citations.

How Should AI Citation Success Be Measured?

Measure presence, citation, absorption, traffic, and fidelity separately. A page can influence an answer without receiving a click, and one prompt run is too unstable to prove success or failure.

What Makes E-E-A-T Different in 2026?

The difference is machine readability. Human-facing trust still matters, but AI systems also need explicit author metadata, structured evidence, crawl access, current dates, and source-backed claims they can verify quickly.

References

Google Search Central. (2026). Optimizing your website for generative AI features on Google Search. Source

Google Search Central. (2026). Spam policies for Google web search. Source

Google Search Central. (2025). Creating helpful, reliable, people-first content. Source

Google Search Central. (2025). Article structured data. Source

Google Search Central. (2025). Introduction to structured data markup in Google Search. Source

Reid, E. (2026, May 19). A new era for AI Search. Google. Source

Xu, H., Iqbal, U., & Montgomery, J. M. (2026). Measuring Google AI Overviews: Activation, source quality, claim fidelity, and publisher impact. arXiv. Source

Zhang, K., He, X., & Yao, J. (2026). From citation selection to citation absorption: A measurement framework for generative engine optimization across AI search platforms. arXiv. Source

Chen, S., Alrashed, T., Halevy, A., & Noy, N. (2026). Do agents need semantic metadata? A comparative study in agentic data retrieval. arXiv. Source