When USCIS decides whether to approve you for EB-1A or NIW, the officer receives the petition after AI-processing — and some denials and RFEs attorneys link directly to that. In this article I analyze four RFE patterns that Cozen O’Connor and Reddy Neumann Brown directly associate with AI tools used by adjudicators. I provide real Q3 FY2025 numbers, verbatim stories from petitioners on Reddit, and a final petition-defense checklist. This is the practical part — for those who have already filed or are preparing to file.
This is one of four articles in the USCIS and AI cluster. This piece is only practice: what most often "breaks" the model and how to protect your petition. The general overview is in the main article, USCIS AI systems are here, and courts and FOIA lawsuits are here.
Contents
Q3 FY2025 numbers — why now
Why did AI-RFEs become a hot topic in 2026, and what do the numbers say?
First, let’s clarify what “Q3 FY2025” means, because you’ll see this label often below. FY is the federal fiscal year. It doesn’t match the calendar year: it begins October 1 and ends September 30. So Q3 (third quarter) of fiscal 2025 is April, May, and June 2025. In short, when you see “Q3 FY2025”, read it as “spring–summer 2025.”
That quarter was the first when approval statistics sharply diverged from historical trends. And the divergence wasn’t even across the board: it was the categories requiring qualitative judgment that fell, while the formal checklist-driven O-1 remained stable.
Why this matters for understanding AI. EB-1A and NIW require discretionary assessment: the officer must decide whether the petitioner meets “extraordinary ability” or “national importance” based on the totality of the evidence. That’s a qualitative judgment, which is hard for AI. O-1 is a more formal checklist check (are specific awards present, is a certain number of publications met). It’s the discretionary categories that fell, while the checklist-driven category held — an asymmetry consistent with automation failing where judgment is required.
EB-1A has a feature that surprises many. Even if you formally satisfy three criteria out of ten, that’s not an automatic approval: at the second stage — the so-called final merits determination — the officer reevaluates the petition as a whole and decides whether it forms the picture of a person of “extraordinary ability.” It’s at this stage that “denials after criteria were met” happen, and it’s where automation fits worst. I analyzed how that second stage works and why it became the main denial point in a separate article on Final Merits Determination — if you’re preparing an EB-1A, read it before filing.
What these numbers mean for you
If you filed earlier — approval rates were higher. If you file now — treat Q3 as a new baseline. RFEs are more common, and according to Cozen O'Connor and Reddy Neumann Brown, the increase correlates with specific RFE patterns attorneys link to AI tools. Direct causation isn’t proven, but the four patterns below appear often enough to prepare for. Source for the numbers: Manifest Law.
Pattern 1: mis-tagged evidence
The most frequent and the most insidious pattern, because you and the officer can both be right at the same time. Let’s start with this one.
RFE on documents you definitely submitted
When you upload a petition via myUSCIS, the ELIS Evidence Classifier tags each page. If it errs — for example, it classifies two of eight recommendation letters as "Other Document" due to unusual formatting — the officer will see six letters instead of eight and write in the RFE, "petitioner submitted only 6 recommendation letters." You open your copy — all eight are there. Factually, you’re right, and the officer is also correct in their logic: they only saw what the classifier showed them.
This isn’t speculation; Cozen O’Connor documents this pattern explicitly.
“USCIS has not published any error-rate data, and practitioners report RFEs for documents that were in fact submitted, consistent with classifier mis-tagging.”
USCIS does not publish error-rate data, and practitioners report RFEs for documents that were actually submitted — consistent with classifier mis-tagging. Source: Cozen O'Connor.
recommendation_letter_Prof_Smith_Stanford.pdf instead of RecLetter1.pdf. The ELIS Classifier uses the file name and content for tagging.
Don’t combine disparate evidence into a single 200-page file. Each item should be a separate PDF or an explicitly separated section.
“Exhibit A-1 — Letter from X — pages 47–50.” In your RFE response, point exactly where the "missing" document is located in the original.
Pattern 2: cross-document mismatch
If the first pattern is about a lost document, the second is about an apparent conflict between documents that a human wouldn’t see.
When a single word or date format triggers an RFE
USCIS has a model that compares data across all your documents and flags inconsistencies. If your DS-160 lists “Senior Software Engineer,” your I-129 lists “Principal Software Engineer,” and a recommendation letter says “Lead Engineer,” the machine sees three different entries and raises a flag. To a human, these are obvious variations of the same job title. To an automated system, they’re three distinct values.
This is especially painful for Russian-speaking applicants because of transliteration and date-format issues. One case illustrates this well.
One date in three formats, and the system flags a discrepancy
A client received an RFE for "date inconsistencies." It turned out the Russian document used 15.03.2023, the certified English translation used 03/15/2023, and a third document used 15/03/2023. The same date written three ways. AI saw three different strings.
Name transliteration
Egor / Yegor / Igor / E. Akimov in different documents — this triggers a system flag. Attach a name-variation memorandum listing all variants and the transliteration standards (GOST in Russian documents, BGN/PCGN for U.S. passports).
Organization names and job titles
Skoltech / Skolkovo Institute of Science and Technology — standardize to a single official English name. Translate job titles once and keep them identical across all documents.
Pattern 3: hidden text inside the PDF file
The third pattern is the most subtle, because the issue is something you don’t see on screen but the computer reads perfectly.
A file can contain text you don’t see, but the program reads it
Simply put: when you open a PDF, you see a neat document — your letter, diploma, article. But inside the file, besides the visible text, there can sometimes remain invisible text. How does it happen: you copied a section from an old template or from the web, converted Word to PDF and old edits/comments remained, or you scanned a document with OCR and the OCR layer contains stray text. It’s not visible to the eye, but USCIS’s program sees it. If, for instance, the phrase “Dear Hiring Manager” from someone else’s template remains inside, your letter may be marked as templated even if you wrote it yourself.
Good news: you can check this in a minute without special software.
Select all the text with your mouse
Open your PDF, press Ctrl+A (on Mac — Cmd+A) — this will select all the text in the file. See what is highlighted. If more text is highlighted than you can see on the page (some extra phrases or fragments), there’s hidden text in the file.
Or copy the text into a plain document
Same: Ctrl+A, then Ctrl+C (copy), and paste into an empty Word document or Notepad. You’ll see all the text actually contained in the file — including invisible items. This immediately reveals if there’s anything extra.
Pattern 4: boilerplate RFE written by AI
The first three patterns are about AI reading your documents. The fourth is about AI possibly drafting the officer’s document.
A template that generated a template
If an internal AI assistant drafts an officer’s RFE and the officer lightly edits and sends it, under load this mode can become widespread. Signs: identical wording for each criterion, citation of case law that’s not on point, long chunks of the Policy Manual copied without analysis of your specific petition, mention of another employer or field unrelated to your case.
Reddy Neumann Brown provided the best description of such an RFE.
“disorganized, boilerplate recitations of USCIS Policy Manual provisions... often copied verbatim and presented without analysis... looks official but reads as though no human being meaningfully reviewed the filing.”
Disorganized template-like recitations of Policy Manual provisions, often copied verbatim and presented without analysis... looks official but reads as though no human being meaningfully reviewed the filing. Source: Reddy Neumann Brown.
This firm also made an observation worth noting separately. The strangest kind of such RFE is one that contains no actual request. It sounds absurd, but it happens: the officer sends a notice that doesn’t state which document is missing, why what was submitted is insufficient, or what exactly needs to be sent. Just a set of general phrases from the Policy Manual. For you, that means you’ll likely respond effectively blind — which makes it all the more important to lay out clearly, in your response, what you already have and where it is.
Attorneys directly link this to what technologists call AI “hallucination” — when the system confidently produces text disconnected from the real case data. In RFE form, this shows up in four ways, and it’s useful to recognize them.
- The RFE misrepresents evidence that is plainly in the file (for example, it states something is missing even though it was attached).
- The RFE applies the wrong legal standard — not the one actually used to evaluate your category.
- The RFE cites policy provisions that do not relate to your case.
- The RFE asserts an inconsistency that does not follow from the petition’s content.
A documented sign is incorrect citation of case law. The Seltzer Firm analyzed a typical tactic: RFEs for EB-1/O-1 cite cases to support things that those cases do not actually say.
A classic example is a template RFE phrase: “the terms ‘original’ and ‘major significance’ are not superfluous and therefore matter,” with citations to Silverman v. Eastrich and APWU v. Potter. It sounds authoritative. But Seltzer checked the cases:
- Silverman v. Eastrich — a dispute about the nonrepayment of a $10M loan, unrelated to immigration. The word “major significance” doesn’t appear, and “original” appears only in the sense of “original loan,” not meaning “unique.”
- APWU v. Potter — about an anthrax mailing investigation. Neither “original” nor “major significance” appears there at all.
- Visinscaia v. Beers (the Moldovan ballerina case) is often presented as if the contribution must affect “the entire field.” In reality, the court said technique should have been adopted by at least some studios or dancers, not necessarily the whole field.
Another subtle substitution to know: the regulation requires contribution “in the field” (within one’s area), while an RFE may quietly change this to “to the field as a whole,” raising the bar above what the regulation states. That’s expressly forbidden by Kazarian.
Source: The Seltzer Firm. This could be old officer templates or AI hallucinations. You can’t tell from the outside, but the takeaway is the same: when you get an RFE, check every cited case — it may be off-point, or the RFE may have substituted regulatory language.
Real stories from Reddit
The most valuable input isn’t firm press releases, but individual petitioners describing their RFEs. Below are verbatim posts with author attribution and a thread link so you can open the source yourself.
“What concerns me is that the RFE does not mention a single exhibit, achievement, or employer document I included... it even contains an employer name that has nothing to do with me... For clarity: I have never worked for FAANG (the company named in the RFE).”
The RFE mentions none of my exhibits or achievements, and even contains an employer unrelated to me. I have never worked for the company named in the RFE. This is pattern 4 in its pure form. Source: Reddit.
“I submitted evidence for 6 criteria, but the RFE only addresses 4 of them. The other 2 aren't mentioned at all - not approved, not denied, just completely ignored as if I never submitted them.”
I submitted evidence for 6 criteria, but the RFE addresses only 4. The other 2 are not mentioned at all — as if I never submitted them. This is pattern 1 (mis-tagged evidence): the classifier didn’t show part of the file to the officer. Source: Reddit.
“The denial letter is 6 pages, out of which 5 pages are a copy-paste of USCIS policy manual text... without once mentioning any details regarding my PE or anything else from what I submitted in the RFE.”
Denial letter of 6 pages, 5 of which are copy-paste from the Policy Manual, not once mentioning details of my proposed endeavor or anything I submitted in the RFE. Author profile: PhD, postdoc, U.S. patent, 3 first-author publications. Source: Reddit.
Balancing note: AI field is not a sentence
To avoid alarmism: in r/EB2_NIW in March 2026, a person working in AI described having their petition approved without any RFE and concluded — “Being in AI does NOT automatically mean RFE.” Working in AI doesn’t automatically trigger an RFE. Quality of argumentation matters, not the mere fact that you’re in AI.
Petition defense checklist 2026
These steps work whether your petition is processed by AI or a human — they protect against both scenarios.
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✓Explicit mapping evidence-to-criterion on the first pages
A table mapping each exhibit to a specific criterion (for EB-1A — to the 10 criteria; for NIW — to the 3 Dhanasar prongs).
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✓File names with clear labels
evidence_C1_press_coverage_NYT_2024.pdf is better than exhibit_3.pdf. This signals the classifier.
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✓Recommendation letters with facts
Concrete dates, figures, projects, comparisons. Letters without specifics read as templated to both AI and the officer.
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✓Certified human translations
Do not rely on USCIS machine translation. A certified human translation per 8 CFR 103.2(b)(3) neutralizes the risk of AI mistranslation.
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✓Name variation memorandum and unified dates
List all transliteration variants of your name and use a single date format to avoid cross-document mismatch.
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✓Check PDFs for hidden text
Select all text (Ctrl+A) or copy it into Word. If there’s extra invisible content, recreate the file: print and rescan it.
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✓Audit your social profiles BEFORE filing
Open your LinkedIn, Facebook, Instagram and compare publicly posted info (employment dates, employers, locations, marital status) with what you state in the petition. If LinkedIn shows "working since 2020" but your petition says "2021," the system will spot the mismatch. Attorney Oleg Gherasimov (SG Legal) advises such an audit: opaque discrepancies create risk even if easily explainable.
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✓Identify the pattern
Which of the four: lost evidence, apparent cross-document contradiction, hidden PDF text, or boilerplate RFE from AI. Your response should be targeted to the pattern.
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✓Provide precise references to the original
For each "missing" document — Exhibit, page, paragraph. This forces the officer to acknowledge the evidence was submitted.
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✓Verify every case law citation
If the RFE cites Silverman, APWU, or similar off-point authorities — point that out and request a relevant precedent.
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✓Save the full denial text
If it resembles AI-generated text (formal, cites regulations without analysis) — preserve it for a motion to reopen.
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✓Motion to reopen or appeal to AAO
Present the specific facts the officer didn’t consider. Don’t argue "AI bias" — legally that doesn’t work; rely on facts.
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✓Watch Mukherji and Pangea
If those cases produce precedent, your motion can include new arguments (see the article on FOIA and courts).
Conclusions
EB-1A 66.6%, NIW 54%, while O-1 holds at 93.8%. The drop is concentrated in discretionary categories. Filing in 2026, use these figures as your reference, not the historical 75–80%.
Lost evidence, apparent cross-document contradictions, hidden PDF text, and boilerplate AI RFEs. Most problematic RFEs in 2025–2026 fall into one of these four, and each has a specific defensive approach.
Two hundred pages without mapping loses to 120 pages with clear structure. The classifier reads linearly and gets lost in large unstructured files.
Real petitioners’ stories show: if you identify the pattern and reply precisely with facts and figures, approval after an RFE is still achievable. Panic doesn’t help; a precise response does.
Related articles in this cluster
- USCIS and AI 2026 — general overview
- USCIS AI systems — use cases from the DHS Inventory
- FOIA lawsuits and courts — Pangea, Mukherji, Loper Bright
- AOS memo 2026 — parallel changes in Adjustment of Status
Disclaimer. I am not a licensed immigration attorney. The checklist is based on analysis of publications by Cozen O'Connor, Reddy Neumann Brown, The Seltzer Firm, and real Reddit cases. Consult a licensed attorney who knows your case before filing. If any link is broken — tell me, and I’ll update it.
Author: Egor Akimov, eliteskillset.com. Published 2026-06-02, updated based on original analysis May 25, 2026.