New Rules for AI Text on arXiv

May 15, 2026, 20:38
GlobalScience
A2

New Rules for AI Text on arXiv

Introduction

The arXiv website has new rules. Authors must check their AI text.

Main Body

Some people use AI to write papers. Sometimes AI makes mistakes. It writes fake facts or wrong names. Thomas Dietterich says this is a problem. Authors must check all AI text. If the text has big mistakes, the author is in trouble. The author cannot send new papers for one year. After one year, the author must show their work to other experts first. The arXiv staff will check the mistakes. Authors can ask for help if they disagree.

Conclusion

arXiv wants high quality work. They will punish people who use AI without checking it.

Learning

The 'Must' Rule

In this text, we see the word must many times. We use it when there is a strict rule. You have no choice.

Examples from the text:

  • Authors →\rightarrow must check AI text.
  • Author →\rightarrow must show work to experts.

How to use it: [Person] + must + [Action]

  • I must study.
  • You must stop.

Watch out for 'Fake' and 'Wrong'

These words describe things that are not true. They are very useful for A2 learners to describe mistakes:

  1. Fake →\rightarrow Not real (Example: Fake facts).
  2. Wrong →\rightarrow Not correct (Example: Wrong names).

Simple Tip: Use wrong for a mistake in a test, and fake for something designed to trick people.

Vocabulary Learning

new (adj.)
not old; recently made or created.
Example:The new rules are on the website.
rules (n.)
a set of instructions or laws to follow.
Example:The rules say you must check your AI text.
website (n.)
a place on the internet where information is stored.
Example:The arXiv website has new rules.
must (v.)
an obligation to do something.
Example:Authors must check all AI text.
check (v.)
to look at something carefully to find mistakes.
Example:You should check your work before submitting.
write (v.)
to produce words on paper or screen.
Example:Some people use AI to write papers.
papers (n.)
written documents or articles.
Example:The author cannot send new papers for one year.
mistakes (n.)
things that are wrong or incorrect.
Example:The AI makes many mistakes.
fake (adj.)
not real; made up.
Example:It writes fake facts.
wrong (adj.)
not correct or true.
Example:The AI writes wrong names.
problem (n.)
a difficult situation that needs a solution.
Example:This is a problem for authors.
big (adj.)
large in size or importance.
Example:If the text has big mistakes...
trouble (n.)
difficulty or problems.
Example:The author is in trouble.
send (v.)
to give something to someone.
Example:The author cannot send new papers.
year (n.)
a period of twelve months.
Example:The author cannot send new papers for one year.
show (v.)
to display or present.
Example:After one year, the author must show their work.
work (n.)
a task or job.
Example:The author must show their work.
experts (n.)
people who know a lot about something.
Example:The author must show their work to other experts.
ask (v.)
to request information or help.
Example:Authors can ask for help if they disagree.
help (n.)
assistance or support.
Example:They can ask for help.
disagree (v.)
to have a different opinion.
Example:Authors can ask for help if they disagree.
high (adj.)
of great level or quality.
Example:arXiv wants high quality work.
quality (n.)
the standard of something.
Example:They want high quality work.
punish (v.)
to give a penalty.
Example:They will punish people who use AI.
without (prep.)
not having or lacking.
Example:They will punish people who use AI without checking it.
B2

arXiv Introduces Penalties for Unverified AI-Generated Content

Introduction

The arXiv preprint server has introduced strict penalties for authors who submit papers containing unverified content created by AI.

Main Body

The increase in AI-generated content in academic writing has forced the server to change its moderation rules. Thomas Dietterich, a member of the arXiv editorial council, emphasized that authors who submit papers with clear evidence of unverified Large Language Model (LLM) use will face serious sanctions. This evidence includes fake citations, incorrect data, or remaining AI comments in the text. According to the Code of Conduct, authors are fully responsible for the accuracy of their work, regardless of the tools they use. Consequently, if authors are negligent regarding AI errors—such as plagiarism or bias—they will be banned from submitting new papers for twelve months. Furthermore, these authors will be required to have their future work accepted by a reputable peer-reviewed journal before they can post it on arXiv again. This change follows previous rules for computer science review articles, which now require peer review to stop the rise of low-quality AI bibliographies. To ensure fairness, the administration has created a verification process where a moderator and a Section Chair must confirm the error, although authors can still appeal the decision.

Conclusion

arXiv has created a strict system to maintain academic quality by punishing the submission of unedited AI content.

Learning

🚀 The 'Cause & Effect' Connection

At the A2 level, you likely use 'because' or 'so' to connect your ideas. To move toward B2, you need to use 'Logical Connectors' that make your writing sound professional and academic.

🔍 Spotting the B2 Shift

Look at how the text connects a problem to a result without using the word 'so':

"The increase in AI-generated content... has forced the server to change its moderation rules." "Consequently, if authors are negligent... they will be banned."

🛠️ The Toolkit: From Basic to B2

Instead of repeating 'so', try these structures found in the text:

  1. Consequently →\rightarrow Use this at the start of a sentence to show a direct result.

    • A2 style: I forgot my keys, so I couldn't enter.
    • B2 style: I forgot my keys; consequently, I could not enter the building.

  2. Force [someone] to [do something] →\rightarrow Use this when a situation leaves no other choice.

    • A2 style: The rain made us stay inside.
    • B2 style: The heavy rain forced us to stay indoors.

💡 Pro Tip: The 'Regardless' Pivot

Another high-level phrase used here is "regardless of." It means 'it doesn't matter what.'

  • Example: "...fully responsible for the accuracy of their work, regardless of the tools they use."

Try this shift: Stop saying 'It doesn't matter if...' and start saying 'Regardless of...' to instantly sound more fluent.

Vocabulary Learning

penalties (n.)
Punishments or consequences imposed for breaking rules.
Example:The new regulations impose strict penalties for plagiarism.
unverified (adj.)
Not confirmed or checked for accuracy.
Example:The article contains unverified claims that need further research.
moderation (n.)
The process of reviewing and controlling content.
Example:Content moderation helps keep the platform safe.
sanctions (n.)
Official penalties or restrictions imposed by an authority.
Example:The committee issued sanctions against the violating member.
negligent (adj.)
Failing to take proper care or attention.
Example:The negligent editor overlooked several errors.
plagiarism (n.)
Using someone else's work without proper credit.
Example:Academic institutions take plagiarism very seriously.
bias (n.)
A preference or inclination that affects impartiality.
Example:The study must avoid bias in its conclusions.
peer-reviewed (adj.)
Reviewed by experts in the same field before publication.
Example:Only peer-reviewed journals are considered reputable sources.
reputable (adj.)
Having a good reputation or being trusted.
Example:She worked for a reputable university.
fairness (n.)
The quality of being just and impartial.
Example:The committee emphasized fairness in the decision-making process.
verification (n.)
The process of checking something to confirm its truth.
Example:Verification of data is essential before publication.
moderator (n.)
A person who oversees and manages discussions or content.
Example:The forum moderator enforced the rules.
appeal (v.)
To request a reconsideration of a decision.
Example:He filed an appeal against the ban.
bibliographies (n.)
Lists of sources or references cited in a work.
Example:The bibliography included all cited works.
C2

Implementation of Punitive Measures Against Unverified Large Language Model Outputs on the arXiv Preprint Server

Introduction

The arXiv preprint server has introduced stringent penalties for authors who submit manuscripts containing unverified AI-generated content.

Main Body

The proliferation of synthetic content within scholarly literature has necessitated a recalibration of moderation standards. Thomas Dietterich, a member of the arXiv editorial advisory council and computer science section chair, has articulated a policy whereby the submission of manuscripts exhibiting 'incontrovertible evidence' of unverified Large Language Model (LLM) generation will result in significant sanctions. Such evidence includes the presence of hallucinated citations, erroneous data, or residual LLM meta-comments. Under the established Code of Conduct, the responsibility for the integrity of a manuscript resides exclusively with the listed authors, irrespective of the tools utilized during the drafting process. Consequently, the discovery of negligence regarding AI-generated errors—including plagiarized or biased content—will trigger a twelve-month suspension of submission privileges. Furthermore, a conditional requirement will be imposed upon the offending authors: any subsequent submissions must first obtain acceptance from a reputable peer-reviewed venue. This regulatory shift follows a prior modification of policies concerning computer science review articles and position papers, which now require prior peer review to mitigate the influx of low-substance, AI-generated annotated bibliographies. To ensure procedural fairness, the administration has implemented a verification protocol requiring documentation by a moderator and confirmation by a Section Chair, while maintaining an appeals process for sanctioned authors.

Conclusion

arXiv has established a rigorous enforcement mechanism to ensure scholarly scrupulousness by penalizing the submission of unedited AI content.

Learning

The Architecture of Institutional Authority: Nominalization and the 'Passive' Agency

To transcend B2 proficiency, a student must stop viewing 'formal English' as a collection of big words and start viewing it as a strategic manipulation of syntax to evoke objectivity. This text is a masterclass in Institutional Register, characterized by a phenomenon I call "The Erasure of the Individual."

◈ The Nominalization Engine

Observe how the text transforms actions (verbs) into concepts (nouns). This shifts the focus from who is doing to what is happening.

  • B2 Approach: "The server is penalizing authors because there is too much AI content." (Subject →\rightarrow Action →\rightarrow Object).
  • C2 Execution: "The proliferation of synthetic content... has necessitated a recalibration of moderation standards."

Analysis: By turning "proliferating" into "proliferation" and "recalibrating" into "recalibration," the writer removes the human actor. The situation itself becomes the agent of change. This is the hallmark of high-level academic and legal writing: it presents decisions as inevitable logical outcomes rather than personal choices.

◈ Lexical Precision: The 'Weight' of Qualifiers

C2 mastery is found in the nuance of adjectives that signal absolute certainty or legal thresholds. Note the use of "incontrovertible evidence."

In a B2 context, a student might use "clear evidence" or "obvious proof." However, "incontrovertible" functions as a terminological barrier. It implies that the evidence is not just clear, but incapable of being denied or refuted. It moves the discourse from a conversation to a verdict.

◈ Syntactic Compression & Dependency

Look at the construction: "...the responsibility for the integrity of a manuscript resides exclusively with the listed authors, irrespective of the tools utilized..."

The C2 Pivot: The phrase "irrespective of" acts as a sophisticated logical pivot. It allows the writer to acknowledge a variable (the AI tools) while simultaneously stripping it of any legal or moral relevance to the conclusion.

Sscholarly takeaway: To write at a C2 level, cease describing actions. Start describing systems of causality. Replace "We decided to change the rules because..." with "A regulatory shift was necessitated by..."

Vocabulary Learning

proliferation (n.)
Rapid increase in number or quantity
Example:The proliferation of synthetic content on the platform alarmed regulators.
synthetic (adj.)
Artificially created rather than occurring naturally
Example:Synthetic data is often used to augment training sets for machine learning models.
recalibration (n.)
The act of adjusting or readjusting to improve accuracy
Example:The recalibration of the moderation standards followed the surge in AI-generated submissions.
articulated (v.)
Expressed clearly and distinctly
Example:The policy was articulated by the council in a formal memorandum.
incontrovertible (adj.)
Impossible to dispute or deny
Example:Evidence of hallucinated citations is incontrovertible and leads to sanctions.
hallucinated (adj.)
Fictitious or fabricated, especially by a model
Example:Hallucinated references often appear in unverified AI-generated manuscripts.
erroneous (adj.)
Containing or expressing a mistake
Example:Erroneous data can mislead readers and compromise research integrity.
residual (adj.)
Remaining after removal or elimination
Example:Residual meta-comments may indicate the model’s influence on the text.
integrity (n.)
The quality of being honest and morally upright
Example:Maintaining the integrity of a manuscript is the sole responsibility of its authors.
negligence (n.)
Failure to exercise proper care or caution
Example:Negligence in reviewing AI-generated content can lead to publication of errors.
plagiarized (adj.)
Copied from another source without proper attribution
Example:Plagiarized passages were flagged during the mandatory peer review.
biased (adj.)
Showing favoritism toward one side
Example:Biased language can distort the objectivity of scholarly work.
mitigate (v.)
To make less severe or harsh
Example:The new guidelines aim to mitigate the influx of low-substance AI-generated bibliographies.
scrupulousness (n.)
The quality of being thorough and morally precise
Example:The enforcement mechanism rewards scrupulousness in manuscript preparation.