标签： DeepSeek_en

Subtitle:
Multi-round evidence gathering, time cutting, higher sensitivity thresholds and commercialization moving forward — platform rules have changed, and the playbooks of enterprise brands and GEO practitioners must be rewritten.

Introduction

In the past, many people understood GEO in one sentence: make it easier for AI to find me.
But looking at the recent changes across DeepSeek, Kimi and Doubao together, it becomes clear that the problem is no longer as simple as “being found”. DeepSeek is upgrading networked answers from single-round retrieval to multi-path evidence gathering and cross-verification; Kimi shows stronger “trusted time window” characteristics, with the latest information entering the cognitive layer more slowly; Doubao, after 315, has pushed some marketing/consumer questions into a stronger safety gate and commercial gate. At the same time, public signals from official and research levels are reinforcing the same direction: DeepSeek has explicitly strengthened multi-step agent capabilities and tool use in complex search tasks, Kimi officially continues to advance web search, tool calling and agentic capabilities, while the 315 exposé and subsequent regulation have put “AI poisoning” and “AI-generated ads” directly on the governance agenda.

AI search platforms are shifting from “answer as much as possible” to “answer with credibility first”, and from “single-round hits” to a composite system of “evidence orchestration + risk control + platform governance”.

1. These three changes are essentially not three separate things

1) The change in DeepSeek: not “more citations”, but “the answer mechanism has changed”

From official public information, when DeepSeek launched web search at the end of 2024, it already stated clearly that for complex questions the model automatically extracts multiple keywords and searches in parallel; then in 2025, DeepSeek-V3.1 wrote “stronger Agent capabilities” and “stronger multi-step reasoning for complex search” into its official release notes; by V3.2, the official notes further explained that the model supports integrating thinking directly into tool use, allowing multiple rounds of reasoning and tool calls before giving the final answer. More importantly, DeepSeek’s publicly documented web/app search template requires answers to synthesize multiple relevant web pages, avoid repeatedly citing the same page, and place citations near the corresponding sentences instead of piling them all at the end. In other words, DeepSeek’s networked answers are increasingly unlike a linear “search once, answer once” process, and more like a layered agentic workflow of query understanding → planner/router → parallel retrieval workers → verifier/synthesizer.

When the platform starts multi-round verification, direction-specific retrieval and unified synthesis, the logic of single-round feeding begins to fail. This means GEO’s competitive object is no longer just a single page, but whether you can enter multiple sub-questions, multiple evidence chains and multiple verification nodes.

2) The change in Kimi: not “cannot go online”, but “more cautious about what to believe”

The platform’s acceptance threshold for the latest information has risen. This is especially interesting when viewed against public information: Kimi has not stood still. Moonshot’s official documentation and site clearly show that Kimi already supports web search and tool calling; in 2025 it moved further toward K2, K2 Thinking and Open Agentic Intelligence, and has even put the direction of Agent Swarm on its front-end product. In other words, Kimi does not lack capability; rather, beyond “being able to search”, it puts “what should be believed, when it should be included, and with what weight” closer to the front.

This matches a public trend that appeared in the industry after 315. A March report by Wallstreetcn mentioned that one common practice for large-model vendors to resist RAG poisoning is to downweight batch-generated, source-less, low-credibility content, and adopt a “time cutting” strategy, delaying or downweighting the indexing of recently batch-published content to reduce the risk of being quickly “brainwashed”. This is a typical side effect when AI search platforms put credibility ahead of timeliness.

For brands, this signal is very important: chasing novelty no longer automatically equals being seen by AI.
If your 2026 information cannot smoothly enter the platform’s trusted time window, then no matter how new the release or how intensive the distribution, it may only be “updated” in your own channels without entering AI’s cognitive layer.

3) The change in Doubao: not “cannot answer”, but rewriting who is qualified to enter the answer

After 315, Doubao showed three changes in some marketing/consumer Q&A: more refusals to answer, higher sensitivity thresholds, and duller understanding of users’ implicit expressions and real intent; at the same time, cases of ad slots and commercialization moving forward also appeared. This shows that the platform is not just “more conservative”, but is redistributing the right to answer, the right to explain and the right to commercial exposure.

This is likewise supported by a clear external environment. After the 315 exposé, authoritative media such as Xinhua explicitly pointed out that some institutions “feed” customized content to large models through mass soft articles, fabricated reviews and fake authority, affecting model crawling, citation and recommendation results. Subsequently, the 2026 national advertising regulatory priorities listed livestream e-commerce ads, testimonial ads and AI-generated ads as key and difficult issues in internet advertising supervision. In other words, why platforms raise the safety gate, why they are more cautious about certain questions, and why they bring “seemingly neutral recommendations” back into the governance view — these are not isolated actions.

When the safety gate rises and the commercial gate moves forward, AI answers are no longer merely technical products, but the result of platform governance, risk control and commercial distribution acting together.

2. Putting the three things together, GEO will see five new gates

If you look at only one platform, it is easy to read the changes as product fluctuations.
But looking at DeepSeek, Kimi and Doubao together, you will find that AI search is forming five new gates:

Gate one: the retrieval gate.
Can you be found?

Gate two: the evidence gate.
After being found, can you survive multi-source comparison?

Gate three: the trust gate.
Does your information come from highly credible sources, and is it within the time window the platform is willing to accept?

Gate four: the safety gate.
Can your way of expression, content boundaries and question scenarios pass platform risk control and sensitivity thresholds?

Gate five: the commercial gate.
Will the platform, in certain scenarios, cede part of the answer space to ads, recommendation slots, commercial partnerships or stronger platform-owned distribution logic?

In the past, much GEO stayed at the first gate: as long as AI crawls, indexes and mentions you, it counts as success.
That is no longer enough. Changes at DeepSeek show that gate two is increasingly important; changes at Kimi show that gate three is increasingly critical; and changes at Doubao remind us that gates four and five are genuinely affecting brand visibility.

3. This means: the object of GEO optimization has changed

1) From “page competition” to “evidence chain competition”

GEO research itself points out that visibility in generative engines is not a simple ranking issue but depends on whether a source is selected, cited and organized into the answer; the same research shows that adding elements such as citations, quotations and statistics can increase source visibility by up to 40%. When DeepSeek’s official search template further requires synthesizing multiple web pages and citing in-sentence nearby, the conclusion becomes even clearer: what will be most valuable in the future is not “which article ranks where”, but whether the brand has a set of evidence assets that models can break apart, compare, cite and cross-verify.

2) From “single-question optimization” to “multi-sub-query optimization”

DeepSeek’s parallel search for complex questions is highly aligned with the “multi-query generative engine optimization” emphasized in the 2026 IF-GEO paper. The latter explicitly points out that facing diverse queries, document optimization becomes a multi-objective problem with conflicting constraints, requiring divergence first and then convergence. Translated into GEO practice: you can no longer bet that one page, one title and one keyword will hit all questions. You must build callable content units around multiple semantic facets: “definition, comparison, price, scenarios, pros and cons, cases, target audience, risk boundaries”.

3) From “chasing timeliness” to “trusted time management”

When platforms tighten the inclusion of the latest content to prevent poisoning, what brands most easily encounter is not a traffic decline, but AI aphasia.
At this point, content updating is no longer just a rhythm-of-publishing issue but a “time governance” issue: which are evergreen facts, which are version updates, which need historical archiving, which must be redirected or marked invalid. Otherwise, the brand’s image in AI will show two extremes: either stuck in an old year, or old and new information coexisting and contradicting each other. After 315, platforms downweighting low-credibility content and delaying the indexing of batch content are essentially turning “trusted time management” into a new threshold.

4) From “content publishing” to “cognitive asset management”

This is also the true meaning of “brand GEO should return to mind management”.
What brands compete for in the AI search era is not just a single mention, but whether they can form a clear, stable, credible and repeatable entity impression in the model’s long-term cognition. Official website, media, encyclopedias, industry platforms, founder/executive viewpoints, third-party reviews and customer cases are no longer scattered PR assets; in the AI era they jointly constitute the brand’s external cognitive layer. Whoever has more consistent entity information, clearer definitions and more stable evidence is more likely to be retained when platform risk control tightens.

5) From “more citations = more real” to “verifiable = more important”

Here we should also especially guard against a misconception: more platform citations do not automatically mean the answer is closer to the truth.
A 2025 study found that as long as an answer carries citations, users’ trust rises significantly, even if the citations themselves may be random; another large-scale experiment also found that reference links and citations increase users’ trust in AI search, even if those links or citations are wrong or hallucinated. At the same time, the EBU/BBC research reported by Reuters shows that in news Q&A scenarios, nearly half of AI answers have major problems, and about one third have serious source attribution errors. In other words, more citations first raise “the feeling of credibility”, not necessarily “the density of truth”. For GEO, this precisely shows why “being citable” is no longer enough, and why we must further pursue “verifiable, accountable, cross-verifiable”.

4. For enterprise brands: what should be done next?

1) Upgrade the official website from a “display page” to a “fact foundation”

What an enterprise’s official website must first carry is no longer just brand display, but a fact foundation that AI can call.
At a minimum, these types of pages should be built systematically: brand definition page, product capability page, version/price page, FAQ, comparison page, case page, update log page and historical version notes page. Each page should ideally have a one-sentence definition, structured parameters, timestamps, scope of application, boundary descriptions and citation basis. This is not for the “word count” of traditional SEO, but to give AI extractable evidence blocks in multi-sub-query, multi-round verification.

2) Upgrade “publishing press releases” to “managing the trusted time window”

Many brands’ content problems are not that they publish too little, but that old content is not governed.
Once platforms adopt “time cutting” or delayed inclusion of low-credibility content, historical pages, old parameters, expired campaigns and vague wording will all harm the brand in turn. Enterprises need content lifecycle management: what stays valid year-round, what needs review every six months, what is automatically archived on expiry, what must be 301-redirected, marked invalid or explicitly versioned. Essentially, this reduces time noise in AI cognition.

3) Upgrade “brand communication” to an “external source portfolio”

AI does not only read what you say about yourself; it also reads what others say about you.
So enterprises should not only work on the official website but also actively manage the external source layer: in-depth media pieces, industry interviews, research reports, third-party reviews, institutional collaboration content, founder and expert output. Future GEO competes not just on “whether it is on my site”, but on “whether an entire public network forms a consistent, credible and cross-verifiable cognitive network about me”.

4) Upgrade “feeding thinking” to “evidence thinking”

After 315, there is no grey space on this point.
A small number of samples can cause serious poisoning risks to models; research by Anthropic and partner institutions shows that about 250 malicious documents can create a stable backdoor-like influence on large models; and authoritative reports such as Xinhua Daily Telegraph have explicitly identified “fabricated reviews, fabricated experts and mass soft-article feeding” as industry chaos that damages the credibility boundary of AI. If enterprises still understand GEO as “bombarding AI with content”, they are essentially trading short-term exposure for long-term trust risk.

5. For GEO practitioners: the methodology must also be upgraded

1) Work platform by platform; stop fantasizing about “one set of content conquering all”

Even within AI search, platform governance logic has begun to diverge.
For DeepSeek, content should be designed around multi-semantic coverage, multi-source evidence, comparable content, sentence-level citable blocks;
for Kimi, brand cognition should be built around highly credible official sources, stable time series, consistently consistent entity descriptions;
for Doubao, more attention should be paid to compliant semantics, scenario practicality, weak marketing expression, coverage of users’ real intent, so that content is not blocked by the safety gate as soon as it appears.

2) Upgrade the keyword library into a “question universe + semantic facet map”

GEO practitioners should not only make keyword lists but build a user question universe.
Each core business question should be at least broken down into: definition, comparison, budget, who it suits, how to choose, pros and cons, cases, risks, alternatives, industry standards and common misconceptions. Then check accordingly: on which facets the brand has content, on which facets content is missing, and on which facets content exists but lacks a citable structure.

3) Write content as “atomic evidence blocks”

The most valuable content in the future is not the most lyrical, but the easiest for AI to extract precisely.
It is recommended that every page prioritize:

• A one-sentence definition
• 3 to 5 conclusions up front
• Clear data/parameters
• Comparison table
• Timestamp
• Scope of application
• Source notes
  such “atomic evidence blocks” are best suited to enter DeepSeek-style multi-round evidence gathering and comprehensive orchestration.

4) Change the monitoring object from “rankings” to “answers”

What real users see is not the abstract result returned by the API, but the final answer on the App/Web side.
In particular, DeepSeek officially reminds that the API model version is not completely identical to the App/Web version. Therefore, GEO monitoring must return to the real front end: whether the brand is mentioned, where in the answer it appears, which sources are cited, whether the description is accurate, who it appears alongside, whether it is replaced by old information, and whether it is suppressed by safety thresholds. The most critical report in the future is no longer a keyword ranking table, but an “answer monitoring table”.

5) Change KPI from “clicks” to “cognitive share”

What is truly worth tracking long-term is not just sessions and organic traffic, but these metrics:
brand mention rate, citation rate, first-position appearance rate, competitor co-occurrence rate, source-layer coverage rate, brand description accuracy rate, information old-new mismatch rate, assisted conversion rate.
In the AI search era, users often complete comparison, shortlisting and preference formation before they click. If KPIs do not change, teams will keep using the ruler of the old era to measure victories and defeats on new platforms.

Conclusion

GEO is not over; what is over is “feeding-style GEO”.

DeepSeek tells us that the answer mechanism is moving from single-round retrieval to multi-round verification and evidence orchestration;
Kimi tells us that platforms are starting to put “what to believe first” before “what to include first”;
Doubao tells us that safety governance, content risk control and commercial distribution are already genuinely changing how brands enter answers.

After these three are superimposed, the essence of GEO becomes clearer:
it is no longer “write more content so AI sees me”, but building a public knowledge infrastructure that lets AI find you, believe you, let you through, and remember you in long-term cognition.

In the future, the truly competitive brands will not be those with the most press releases or the fastest mass distribution,
but those with clear entity definitions, stable evidence assets, credible time governance and cross-platform consistent cognition.

In other words, the next stage of GEO is not about who is better at “feeding”,
but about who is more worthy of being treated by AI as part of the answer.

Subtitle:
Multi-round verification, time slicing, safety-first gating, and multimodal evidence are entering the picture—the changes across the three platforms are rewriting the underlying rules of GEO.

Introduction

Over the past year, many people have reduced GEO to one sentence: Make it easier for AI to find me, cite me, and recommend me.
But when the recent changes from DeepSeek, Kimi, and Doubao are viewed together, the problem is clearly no longer as simple as “being crawled.”

On the surface, DeepSeek’s changes look like more citations, more diverse sources, and longer answers; in essence, the answer mechanism is shifting from a single-round process of “search once, answer once” to an agentic search workflow that “decomposes questions, splits directions, searches in parallel, and verifies centrally.” On Moonshot’s official side, web search, search/crawl, multi-step tool use, and the agent cluster collaboration of K2.5 have been written into its products and documentation; this means Kimi does not lack the ability to access the web, and the front-end observation of “stopping in 2025” is more like the platform becoming more cautious about “what to trust first.” As for Doubao, the higher refusal rate, stronger risk avoidance, and more obvious commercialization front-loading on consumer, marketing, and recommendation questions after 315 are not isolated events either, but are happening together with AI-generated advertising, citation advertising, platform responsibility, and data-poisoning governance.

First, let’s set a boundary. Some of the following content is front-end observable phenomena, some is secondary industry interpretation, and not all of it equals official platform announcements. Statements such as “a single answer’s citations expand to about 50,” “authoritative sources carry 6 times the weight of self-media,” “DeepSeek launched ‘source backtracking’ on a certain day,” “Kimi adopts ‘time slicing’,” and “Doubao has a 60% refusal rate” are better treated as industry samples and product observations rather than official positions. What can truly be publicly verified is these three points: first, DeepSeek officially continues to strengthen web search, multi-step reasoning for complex search, and thinking in tool-use; second, Moonshot officially documents web search, official tools, multi-step tool calling, and agent collaboration in its product documentation; third, the 315 exposé and the 2026 advertising regulation documents have indeed pushed “AI poisoning,” “AI-generated advertising,” “citation advertising,” and “platform compliance responsibility” into the foreground.

For this reason, the changes across the three platforms cannot be understood merely as product fluctuations. They collectively point to this: AI search platforms are moving from “answer as much as possible” to “answer trustworthily first,” and from “broad indexing” to a composite system of “evidence orchestration + source governance + risk control + commercial distribution.”

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1. DeepSeek: It Raised the “Evidence Gate”

If anyone has most clearly exposed the upgrade of AI search’s underlying mechanisms in this round of change, it is DeepSeek.

According to official public information, when DeepSeek launched web search on the web at the end of 2024, it already stated clearly: for complex questions, the model will automatically extract multiple keywords and search them in parallel; the 2025 V3.1 release notes directly emphasized stronger multi-step reasoning for complex search tasks and stronger agent capabilities; by V3.2, the official documentation further integrated thinking directly into tool-use. More importantly, DeepSeek’s publicly available Web/App search prompt explicitly requires: answers should synthesize multiple relevant pages, citations should be placed near the corresponding sentences rather than piled up at the end, and the same page should not be cited repeatedly. In other words, DeepSeek’s web-grounded answers are clearly no longer single-pass / single-query linear retrieval generation, but closer to a kind of layered agentic search orchestration.

This also corresponds to that judgment in your social feed:
It is not simply “search once, answer once,” but more like first performing query understanding / intent decomposition, then planner / router distributes the subtasks to parallel retrieval workers / search sub-agents, and finally the orchestrator / synthesizer / verifier handles deduplication, conflict resolution, cross-verification, and citation orchestration.
Put more plainly: It used to be like one person looking things up; now it is more like a small team working in parallel.

This has a very direct impact on GEO. In the past, what was optimized was a page under a keyword, competing for “position”; now the competition is: when the model breaks a question into multiple subtasks—definition, comparison, background, cases, price, pros and cons, risk boundaries, and so on—can your content enter more subqueries, more evidence chains, and more verification nodes? The GEO paper itself points out that generative search does not simply return a list of links, but synthesizes multiple sources into a single answer; source visibility depends on whether it is selected, displayed, and cited by the model, not just on ranking. The paper also shows that an appropriate GEO strategy can increase content visibility by up to 40%. By 2026, IF-GEO further framed this problem as a multi-query, conflict-aware optimization problem: not optimizing a single query, but maintaining stable visibility across multiple heterogeneous queries.

This means what DeepSeek’s change truly brings is not the shallow layer of “more citations,” but a rewrite of GEO’s competitive unit from “single page” to “evidence chain.”
“GEO is no longer a content-volume game played alongside search engines, but infrastructure work that provides agents with verifiable evidence assets.” A more rigorous phrasing would be: GEO’s optimization target is migrating from single-point hits in single-turn Q&A to evidence coverage, citation eligibility, and cross-verification pass rates in multi-subtask retrieval.

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2. Kimi: It Tightened the “Trust Time Gate”

The second change comes from Kimi.
The piece you shared, “When Kimi Stopped in 2025,” is most valuable not for “which year it stopped at,” but for accurately grasping a deeper problem: platforms are beginning to redefine “what information deserves to enter the cognitive layer.”

Because according to Moonshot’s official documentation, Kimi does not have a “cannot access the web” problem. The official tool documentation explicitly lists web-search as an official tool and states that its role is to call the latest information, cite authoritative sources, and make results verifiable; the official web search example demonstrates a search/crawl tool-calling flow; after 2025, Moonshot continued to document kimi-k2-thinking and kimi-k2.5’s multi-step tool calling, deep reasoning, and agent capabilities in its documentation; in 2026 it even described an “agent cluster collaboration model” in the K2.5 notes. This shows Kimi’s issue is not “a lack of search and tool capabilities,” but rather more conservative adoption and refresh of the latest content on the front end.

Why would the platform do this? It becomes easier to understand when the post-315 data-poisoning context is brought in. Xinhua News Agency’s client directly documented the GEO gray industry’s language of “paying for placements and feeding content, poisoning, and influencing model citations” in its 315 exposé; Outlook (Liaowang) subsequently stated clearly that the abuse of GEO techniques is pushing mass pseudo-original advertorials, low-quality content, and false information into AI’s retrieval and citation chains. Meanwhile, Anthropic’s research shows that in its setup, 250 carefully constructed poisoned documents are enough to stably implant backdoors across models of different sizes. In other words, if platforms do not raise the bar for the newest, bulk-generated, low-credibility, hard-to-trace information, they will remain exposed to the risk that “even a small number of samples can cause contamination.”

So a more reasonable explanation for front-end phenomena like “Kimi stopped in 2025” is not product regression, but that the platform is doing some kind of trusted time-window management: it does not first ask “is this the latest,” but “is this trustworthy enough to include now.”
This is also the real meaning of “brand GEO should return to mind management.” For brands, competition in the AI search era is not just about publishing information faster, but about getting the platform to form, in its long-term cognition, a clear, stable, credible entity perception with time semantics. If your 2026 new technology, new product, or new strategy is merely published but never enters the platform’s trusted time window, then for users it effectively did not happen.

This directly rewrites a core GEO action: time governance.
Content teams used to care more about “whether it was published” and “how fast it was published”; now they must care more about: which content is evergreen fact, which is a version update, which needs updated timestamps, which needs historical archiving, and which must be taken offline, revised, or redirected. Otherwise, the brand’s image in AI will fall into two extremes: either stuck in an old year, or old and new information mixed together and contradicting each other.

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3. Doubao: It Moved the “Safety Gate” and “Commercial Gate” Up Front

The third change comes from Doubao.
After 315, what matters most about Doubao is not any specific percentage, but the three recurring signals: first, some consumer/marketing questions are more likely to be refused or answered generically; second, the platform’s interpretation of users’ implicit intent and vague expressions has become more conservative; third, the presence of advertising or commercial exposure in the answer space is rising.

To be clear: these are not officially confirmed product metrics from Doubao, but front-end observation samples.
But even treated only as phenomena, they reveal a new reality: what shapes AI answers today is no longer just “what retrieval can find,” but also “how far risk control allows the answer to go” and “how much answer space commercialization occupies.”

This actually aligns with 315 and the 2026 regulatory logic. In the 315 exposé, the GEO gray industry was directly tied to “manipulating model citations and recommendations through paid placements and poisoning”; the 2026 National Advertising Regulation Work Priorities explicitly lists livestream e-commerce ads, citation ads, and AI-generated ads as key difficulties in internet advertising regulation; another regulatory notice specifically noted that if an ad uses an AI-fabricated persona for recommendation or endorsement without clear disclosure, it may be deemed false advertising; it also imposed stricter requirements on citation ads, misleading large-print-small-print tactics, and absolute claims such as “first/best/leading.”

This means platforms like Doubao being more conservative on consumer decisions, marketing Q&A, and recommendation questions is not accidental but driven by a full set of external pressures:
platforms must avoid being manipulated by “data poisoning” and fake authority, reduce compliance risks from confusing AI recommendations with advertising, and manage their own commercial distribution logic. The result is that some questions become safer, but less useful; some questions become more standardized, but less able to understand what users really want to ask; and in some commercial scenarios, the phenomenon of “answer space being squeezed by commercial placements” will become increasingly obvious.

From a GEO perspective, this change is highly instructive.
Many practitioners used to focus only on “how to get AI to mention me”; now they must add another question: even if my content is retrieved on this platform, can it pass the safety, compliance, and commercial gates?
If not, then the GEO problem is not merely “insufficient optimization”—you simply do not have a license to enter the answer.

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4. Looking at the Three Platforms Together, GEO Sees Five New Gates

If the three sets of changes from DeepSeek, Kimi, and Doubao are put together, a new entry mechanism for AI search becomes visible.

The first gate is the retrieval gate.
Can you be found?

The second gate is the evidence gate.
After being found, can you survive multi-source comparison and multi-round verification?

The third gate is the trust time gate.
Is your information trustworthy enough, and is it within the refresh window the platform is willing to include?

The fourth gate is the safety gate.
Can your phrasing, question context, industry risk, and answer content pass platform risk control?

The fifth gate is the commercial gate.
In some scenarios, will the platform prioritize part of the answer space for ads, recommendation slots, partner ecosystems, or its own distribution logic?

Much of past GEO was in fact only fighting at the first gate: as long as AI crawled and mentioned you, it counted as success.
But that is no longer enough. DeepSeek raised the evidence gate, Kimi tightened the trust time gate, and Doubao moved the safety and commercial gates forward. GEO is therefore no longer just “indexing competition,” but has entered a more complex trusted cognition competition.

More notably, platforms giving more citations does not automatically equal a higher density of truth. Research shows that as long as answers contain citations, users’ subjective trust in the answer rises significantly, even if those citations may be random; and the EBU/BBC research reported by Reuters shows that AI assistants still make large-scale errors in news Q&A, with nearly half of answers containing major problems and about one-third having serious source attribution errors. That is, more citations first raise the “feeling of credibility” and do not necessarily raise the “factual accuracy rate.” This is why GEO must move one step beyond “competing to be cited” into “competing to be cited correctly and verifiably.”

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5. Five New Trends in GEO

1. From “Single-Round RAG” to “Agentic Search”

The official documentation of both DeepSeek and Kimi is evolving in one direction: multi-step tool calling, complex search reasoning, and the fusion of tool-use and thinking. For GEO, this means the optimization target is no longer a single query hit, but multi-semantic coverage after question decomposition.

2. From “Page Rankings” to “Evidence Chains and Citation Eligibility”

The GEO paper and the 2026 IF-GEO both emphasize one thing: visibility in generative search fundamentally depends on whether content enters the candidate sources, whether it is selected and cited, and whether it stays stable in a multi-query environment. What will be more valuable in the future is the evidence chain, not just the page.

3. From “Chasing Hot Topics With Press Releases” to “Time Governance”

What Kimi reveals is not temporary conservatism, but that “the latest information no longer automatically equals prioritized information.” Brands must begin to manage the temporal semantics of content: versions, changes, archiving, expiration, redirects, and update timestamps. Otherwise, what ages first in AI is not your pages, but your brand cognition.

4. From “Plain-Text Optimization” to “Multimodal Evidence Assets”

Another frequently mentioned judgment about DeepSeek is “video enters the picture.” Even without treating claims such as “Douyin videos are already integrated” as official announcements, it reveals at least one trend: GEO’s evidence assets are expanding from plain text to multimodal. Relevant 2026 research already describes AI-native search as synthesizing multimodal evidence, and incorporates VLMs and agents into the GEO framework. In the future, tutorial, demonstration, step-by-step, and specification videos that can be parsed by ASR, OCR, and visual understanding will look increasingly like “evidence blocks callable by models” rather than mere traffic material.

5. From “Traffic KPIs” to “Answer KPIs”

Bain research shows that 80% of consumers already rely on AI-generated results in at least 40% of their searches, which has taken a 15% to 25% hit on organic traffic. In other words, users are increasingly completing comparison, judgment, and initial screening “before they ever click into your website.” GEO teams should track in the future not just sessions and CTR, but: mention rate, citation rate, first-position answer appearance rate, source-layer coverage, competitor co-occurrence rate, outdated-information rate, refusal rate, ad-slot squeeze rate, and AI-assisted conversion.

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6. A Practical Guide for Brands: First Upgrade “Visible” to “Trustworthy and Usable”

First, the official website must upgrade from a display page to a “fact foundation.”

What will be most valuable to AI is not vague brand copy, but pages such as: brand definition, product capabilities, version/pricing, FAQ, case library, comparison pages, specification pages, changelogs, and historical version notes.
Each page should have as much as possible: a one-sentence definition, structured specifications, applicable boundaries, timestamps, and source notes.
This is not for “word count,” but so the model can grab, during multi-round verification, atomic evidence blocks.

Second, do not interpret “the return of the official website” as “third parties are useless.”

There is a judgment that “the official website returns while external endorsements depreciate.” I would revise it more precisely:
What truly depreciates is not high-credibility third-party endorsement, but low-quality, untraceable, mass-produced third-party advertorials.
What truly appreciates is the combination of a brand-owned fact foundation plus high-credibility third-party verification.

Third, upgrade “press-release cadence” to “time governance.”

For new product launches, price changes, version updates, and service changes, do not just issue a press release.
Be sure to sync them to the official fact pages, FAQ, changelog, and version notes, and handle redirects, archiving, and expiration labels for old content.
The biggest noise in AI cognition is often not “no information,” but “multiple versions of information existing at the same time.”

Fourth, treat video as an evidence asset, not just traffic material.

Make the point in the first 3 seconds, keep the spoken structure clear, put key data on screen, ensure audio and visuals agree, and make steps repeatable.
Such videos are friendlier to AI because they are easier to break into structured facts.
Tutorial, comparison, demonstration, and Q&A short videos in particular will increasingly resemble a brand’s “multimodal manual.”

Fifth, high-risk industries should prioritize “strongly traceable content.”

In high-risk fields such as finance, law, healthcare, and education, it is even more necessary to write clearly about qualifications, licenses, standards, regulatory definitions, raw data, test reports, expert bylines, publication times, and applicable boundaries.
The more platforms emphasize risk control, the more they prefer such verifiable materials.

Sixth, stop all “feeding-style” and “black-hat” GEO.

315 has already made this clear: mass press releases, fabricated authority, invented reviews, fake products, and data poisoning are not “smarter optimization,” but an overdraft on a brand’s future credibility and compliance room. Xinhua News Agency’s client and Outlook (Liaowang) have directly linked such practices to “poisoning” and “gray industry chains,” and regulators are also including AI-generated ads and citation ads in priority governance.

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7. A Practical Guide for GEO Practitioners: Stop Doing Only “Content Optimization,” Do “Answer Engineering”

1. Build a Query Universe First, Then a Keyword Library

Break core business questions into: definition, price, who it suits, how to choose, pros and cons, cases, risks, comparisons, alternatives, industry standards, and common misconceptions.
The unit of GEO is no longer the keyword, but user tasks and semantic facets.

2. Build an Evidence Layer Map

Audit the brand’s content assets across four layers:
the fact layer, the explanation layer, the proof layer, and the time layer.
You will quickly find that many brands do not really “lack content”; rather, they lack content that AI can use as evidence.

3. Build a Citation Block on Every Page

A one-sentence definition, three to five core conclusions, specifications/data, comparison tables, timestamps, scope, and sources.
Research in 2026 has begun to push GEO from “generic rewriting” toward “citation failure diagnosis” and “structure engineering”: in other words, the future is not about whether you can write, but about why you were not cited and how to fix it in a targeted way. At the same time, structural features themselves affect citation behavior.

4. Do Differentiated GEO by Platform

For DeepSeek, emphasize multi-source evidence, cross-subquery coverage, page structuring, and citation eligibility.
For Kimi, emphasize time governance, authoritative sources, update cadence, and consistency of brand mindshare.
For Doubao, emphasize compliant semantics, authentic user-intent expression in consumer scenarios, and avoiding content that platforms misclassify as advertising, exaggerated, or manipulative.

5. Monitor “Answers,” Not Just “Rankings”

For DeepSeek in particular, the official documentation already states that the API version and the App/Web version are not fully identical.
So GEO’s real observation point must return to the front end:
Is the brand mentioned?
Where does it appear in the answer?
Who vouches for it?
Who does it appear alongside?
Has it been replaced by outdated information?
Is it blocked by the safety gate?
Is it crowded out by ad slots?
These are the metrics closer to business results today.

6. Manage “Cognition Assets,” Not “Content Volume”

The essence of GEO must return to mind management.
Truly effective long-term optimization is not suppressing an answer with large amounts of low-quality content, but getting the brand to form, in AI’s long-term cognition:
who you are, what problem you solve, how you differ from others, what evidence proves you, what the latest version is, and which old information has expired.

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Conclusion

If the changes across these three platforms must be condensed into one sentence, my judgment is:

GEO is not over; what is over is “feeding-style GEO.”

DeepSeek tells us that the answer mechanism is moving from single-round retrieval to multi-round verification and evidence orchestration;
Kimi tells us that platforms now put “what to trust first” before “what to ingest first”;
Doubao tells us that safety governance, platform risk control, and commercial distribution have truly entered the answer-generation process.

So the next stage of GEO is no longer just “let me be seen by AI,” but:

let me be found,
let me be trusted,
let me pass the platform’s safety and compliance gates,
and let me enter the answer in a correct, verifiable, and sustainable way.

The truly competitive brands of the future will not be those that issue the most press releases or stack the most keywords, but those that possess clear entity definitions, stable evidence assets, trusted time governance, multimodal explanation capabilities, and cross-platform consistent cognition.

Ultimately, in the AI search era, the competition is not about who is better at “feeding,”
but about who is more worthy of being treated by AI as part of the answer.