Weekly GEO research: inclusion, ranking, and evidence are different tasks

This issue combines four studies because they examine one intuition from different angles: discovery, selection, evidence, and action are different stages. A local business may be noticed but not ranked first. A model may write a fluent report but misconnect evidence to a claim. An agent may include a product in its candidate pool but not choose it. An article may be cited in one vertical and ignored in another. This is not a finished causal theory, but a useful measurement map.

Section sources:[2] arXiv[3] arXiv[4] DeltaV Digital

A full audit of a local market in Bali compared thousands of venues with answers from several systems. In the published analysis, digital presence, reviews, price, and external mentions were associated with inclusion, while rating was associated with first position among entities already included. This is observational association: stronger venues may simultaneously have more reviews, a better site, and higher ratings. The figures cannot be transferred to another country, language, or industry. The practical consequence is to store inclusion and position as two fields.

Section sources:[1] arXiv

HiEviDR-Bench evaluates more than the appearance of a report: it tests finding evidence, building an intermediate inference, placing support correctly, and reaching a correct answer. This distinction matters for editorial work. A link does not prove that it supports the neighboring claim, and a long text does not replace verification. For original research, preserve a claim ledger with exact claim, source passage, date, what is supported, and what remains outside scope. Benchmark percentages belong to its tasks and are not a ranking of production models.

Section sources:[2] arXiv[5] arXiv

In a controlled simulation, an agent first accessed sellers and products, then formed a shortlist, selected a Top-1 option, and simulated a purchase. More available sellers expanded the pool but did not guarantee attention or action. Memory of whether previous promises matched outcomes reduced the advantage of overly positive explanations. This was a controlled simulation, not a live market, so the same effect cannot be promised in production. The useful design lesson is to separate candidate pool, shortlist, Top-1, and action.

Section sources:[3] arXiv

DeltaV’s study across eight commercial verticals shows that an average citation distribution hides meaningful differences. One vertical favored listicles, another homepages, program pages, or articles. This is not a universal norm and does not prove that format itself causes citation. Compare within vertical × intent × system: “how to choose,” price, best-option, and branded queries need different source types. The practical lesson is to build the page type for the reader’s question, not for a mythical AI template.

Section sources:[4] DeltaV Digital

A minimal dashboard should separate page accessibility, answer inclusion, position, recommendation, brand mention, citation, traffic, and conversion. Every event needs date, market, language, surface, prompt set, denominator, and artifact. A source should connect to a specific claim, not merely appear in a bibliography. When an event was not observed, use unknown. This reduces loud but weak conclusions and helps select the next check: local-data freshness, an evidence audit, independent replication, or format analysis.

Section sources:[2] arXiv[3] arXiv[4] DeltaV Digital[5] arXiv

This week’s materials use different markets, models, corpora, and methods: observational studies, a benchmark, a controlled simulation, and a commercial report. They do not form a single meta-analysis and do not establish causality. Their shared value is splitting the word visibility into observable stages. For GeoAeoAle, the sensible next step is a public baseline with repeated answers and claim-level checks, not mass page creation or one universal ranking. The next review date is October 11, 2026.

Section sources:[2] arXiv[3] arXiv[4] DeltaV Digital[5] arXiv

Instead of one visibility number, an editor needs an observation sheet in which each row describes one run and one outcome. First record whether an object entered the candidate pool; then whether it was named, linked, placed first, and followed by a measurable action. Every step needs system, surface, intent, market, date, and denominator. This format prevents a local audit from being mixed with an evidence benchmark or an agent-purchase simulation. It also helps choose the next article format: a guide answers a reader’s task, a research page exposes a method, and a briefing records a product change. The result is a knowledge base that grows through connected, testable decisions rather than a pile of similar URLs.

Section sources:[2] arXiv[3] arXiv[4] DeltaV Digital[5] arXiv

Comparing different studies requires discipline about units of analysis. One paper may study a venue, another a report claim, a simulated product, or a link in an answer. These units cannot be combined into one percentage. We keep context next to each conclusion and recommend repeating the measurement on an open dataset when a reader needs to transfer it to another market. Define the success criterion in advance as well: candidate inclusion, first position, citation, action, and evidence quality answer different questions and require different denominators.