"The product of mathematics is clarity and understanding. Not theorems, by themselves. Is there, for example any real reason that even such famous results as Fermat's Last Theorem, or the Poincaré conjecture, really matter? Their real importance is not in their specific statements, but their role in challenging our understanding, presenting challenges that led to mathematical developments that increased our understanding."
> This has been the result of months of community input about the fundamental values and goals of the mathematical community. In retrospect, these were questions we should have been systematically discussing years ago, but in any event the exercise was extremely valuable, and the end result is excellent. I wholeheartedly endorse the statements and recommendations in this declaration.
Mathematics seems to be entering an era where human + machine maximizes performance, much like chess in the 1990s. However, imagine a future where even talented mathematicians are nothing but noise in the machine (as is the case in chess now). A future where AI generates and verifies proofs without humans in the loop. Where the mathematics may be beyond human comprehension.
In that future, does it matter that early career mathematicians are inhibited by these developments? Perhaps not. Programming faces the same issue. As AI crawls up the competence ladder, does it matter that fewer people have opportunities to develop the skillset of a senior engineer? Perhaps not.
This just feels like something that has always been true. Defending attribution in this way feels more like a panicked gatekeeping rather than something valuable and principled. I’m a bit disappointed to see people like Terence Tao endorse this.
Everything else seems to be organizational or standard politics. I would have avoided the political sections as it just sets those signatories up for getting into those fights, having not realized what they cosigned.
The issue of attribution is a hard problem that has festered for a while, that has mostly been obscured by the inability to discern an authors entire body of ingested information. I would be careful as, the proposed opt-out restrictions will be eventually imposed on people, which sorta blows up the whole math enterprise of universities/etc. Ie. the proposed leads to a book author revoking a person's ability to reference their arguments in a mathematic paper for money, spite, etc.
> 2. Don’t believe the hype
> 3. Regulate the artificial intelligence industryThere is no moral or ethical obligation to disclose tool use. The disclosure in of itself presents an asymmetric disadvantage to the disclosee. Especially in this charged environment where large swathes of people are champing at the bit to discredit or diminish any effort that leverages these tools.
This system incentivizes people to hide tool use to gain a competitive advantage.
This moralistic grandstanding will be seen as a reactionary movement of people trying to cope with transformative technology.
Lie about tool use, don't admit it. Use it as you see fit and rely on your taste, expertise and best judgement.
"Now, here, you see, it takes all the running you can do, to keep in the same place."
2. then they laugh at you <<<< the International Math Olympiad is basically just high school math
3. then they fight you <<<< this declaration
4. then you win
“Current automated techniques can produce plausible but unreliable (or even incorrect) arguments which are difficult to distinguish from correct mathematical proofs.”
That seems like a problem for mathematics with or without AI.
Isn’t this a problem with human proofs as well?
“Many current models are also built on data obtained by systematically exploiting licenses and access arrangements that were not made with artificial intelligence in mind, or indeed by simply violating copyright protections”
Copyright? The copyright arguments have been hard to make in domains where copyright is much stronger, mathematical knowledge isn’t even subject to copyright.
“Technologies which affect the way in which mathematics is practiced may disturb the current system of incentives”
Resistance to change again.
“Proper evaluation is endangered if results are communicated through informal channels”
Gatekeeping again.
1. AI proofs might be incorrect and difficult to demonstrate why. This implies they are not like human proofs in these qualities.
2. AI proofs are difficult to attribute correctly, because they don't follow established traditions. Nothing to do with the math, but ok.
3. Mathematicians without AI (for political or practical reasons) will not necessarily be able to participate in AI-assisted research. This history of Mathematics is littered with people having uneven access.
4. People/orgs are publishing that AI found things are fact before they are properly evaluated. Same issue.
5. All these things are bad, because AI might muddy the field with lots of unknowns.
I suggest if one looks at the history of funding for mathematics and science, the product of these efforts is not understanding, but rather power. Funding went way up after WW2 when the war demonstrated that power flows from them. Math not only contributed to the scientific weapons of the way, but was directly used in operation planning (the birth of the field of Operations Research) as well as in cryptography.
The reason this matters is that AI is also a quintessential power-oriented technology. From the point of those providing the monetary lifeblood on which modern mathematical practice depends, the current math-AI discussion presents no issue worthy of concern.
>I support this declaration. I have one small comment: the document notes that "Technologies which affect the way in which mathematics is practiced may disturb the current system of incentives." The current system of incentives seriously is flawed in many ways, and I don't think maintaining the status quo should be our goal. However, we should work to improve it, not let it be corrupted by outside forces, as has already been done for decades by university administrators, journal oligopolies, etc.
There's also the separate, less glamorous issue that people don't want to talk about, which is proof reliability. [0] If you have systems to help you formalise the problems and leave an algorithm or AI or whatever solve it in a verifiable way, that's a win for both the mathematicians and the rest of the world.
The deeper question is whether AI can replace the human role in deciding what mathematics should be done and what concepts matter. If that's automated, then yeah, we're screwed.
[0] https://lamport.azurewebsites.net/tla/proof-statistics.html
That would be AGI. My conjecture is that LLMs alone are not enough for that future. They are incredible, but AGI needs other breakthroughs.
In that sense, I think math is very different from chess or Go. Chess and Go are complete-information games with fixed rules and a fixed board. Math is open-ended.
Isn't chess more popular than ever? Ai dominating the game didnt seen to matter
The bar for being cited has never been lower in the history of mathematics: getting an arXiv account is easier than getting a lobste.rs account (at least by my experience, for I have the former but not the latter).
Tao knows what he is talking about, he was an early adopter of LLMs for proof-generation.
The Bourbaki group was (is) about documenting existing mathematics rigorously, not doing original research. This document is specifically about the future of mathematical research.
That's AI in a nutshell: the only point of view that matters is the point of view of people with a lot of money, and we've finally developed a technology that will allow all those other points of view to be squashed and discarded. The powerful won't need to be bothered with them anymore.
For them, math is an instrument. Disagree? Fuck you, you don't matter anymore. Be excited about the future!
The foundations of the WW2 technologies you cite were dependent on previous theoretical efforts (ex:relativity) to develop a good understanding.
Without understanding, you get brittle demos which fail as the environment or problem description changes.
Yes, and your examples are exactly examples of what the GP quote is talking about.
Of course people paying money want applications, which includes "power" in your kind of reductive framing (applications to war being only one of many types of applications, or we could redefine any gradient provided by expanded understanding as "power", in which case the choice of word just seems melodramatic).
What we've also learned over the centuries, a lot more clearly in the last few, is that seemingly pointless or applicationless understanding can very quickly become useful. This is why it's clearly worth still funding pure math.
For many who pursue mathematics, it is a refuge from ordinary life (or normal "power"). Within mathematics, there has historically been a tension between the "Babylonian" model of pure calculation and the "Greek" model of advancement through understanding. If current AI models subsume mathematics entirely, the Babylonian model will have "won" and the possibility of an informed citizenry will be in doubt.
At least for me, in many cases I have achieved a much better understanding of various things after I studied the historical development of the ideas related to them.
Therefore I agree with the point "2." at "Potential Threats". For me a novel mathematical demonstration that is not presented in a way which disentangles its really new elements from the previously known elements, by proper quotation of all relevant older sources, has a value that is many times lower than that of a demonstration with proper attributions.
1. pertains to the quantity of output adding stress to review processes; LLMs can feasibly produce a million plausible but incorrect 'proofs' in the time that a human can produce one. We already see this effect in software development, with bug bounty programs shutting down and open-source software rejecting AI contributions or closing altogether because LLMs flood review channels with an amount of spam for which there is no sufficient amount of human bandwidth to handle.
2. is nothing about "following established traditions" but rather the general concept of crediting people for their prior work, unless you think that "not plagiarising" is a trifling established tradition.
3. is more or less accurate to the point they made, but "it has historically been this way" isn't a compelling justification for "it should always be this way and also it's okay if it gets worse"
4. An existing issue being made 100x more common is a point worth bringing attention to even if it already existed, actually
5. said nothing that could possibly be interpreted in the vein of "muddying the field with lots of unknowns" at all. Point 5 was actually about economic incentives and the risk of mathematic research becoming beholden to tech monopolies
But for the most part, math discovery relied more on human curiosity than on resources to "do math". Conversely, if people allocate lots of money to developing AI, that doesn't mean mathematicians have an obligation to take the money provide ROI to investors.
> In September 2025 the Lorentz Center at Leiden University in the Netherlands hosted a conference entitled Mechanization and Mathematical Research. The around 60 participants from 10 countries comprised mathematicians, computer scientists, philosophers, historians and social scientists, including those with experience in industry and in government.
Did you do this on purpose to anger both Mathematicians and keen spellers?
Human proofs are themselves a kind of a proof of work. They certainly write flawed proofs, but you can expect a human author of a paper to have put in more effort--substantially more--than the human reader needs to verify it. Arguably, this asymmetry disappears for generated proofs.
Automated theorem provers help a bit here, but they don't eliminate the human verification cost.
Even when the proof is produced by the llm in a formal system like Lean4 it may not be “honest”[2] and it can be hard to tell if the proof is very long and complex and especially if it includes highly specialized results from lots of different areas of maths. Llms can (and do) do this just fine, but for a human proof that would require a team each of which was specialized in a particular area. Those people are more likely to be able to cross-check each other.
[1] https://pubs.ams.org/ebooks/conm/098/ and https://en.wikipedia.org/wiki/Four_color_theorem
[2] An “honest” proof may contain bugs or errors but it does not constitute a deliberate attack on the proof system or the math libraries it uses. Systems like Lean aim to not incorrectly validate an honest proof with mistakes but don’t guarantee anything in the case of a proof being dishonest. This is the sense used here https://lean-lang.org/doc/reference/latest/ValidatingProofs/ .
But that is the nature of establishment, when something is a sufficiently firmly established tradition, people see it as a truism.
Crediting people is a social convention. Plagiarism is a social construct. It can be useful, in many areas of science, to reference to support your arguments. This is less important in proofs, because a proof is a proof, but references aid in understanding.
These are all reasons to reference and attribute that benefit the writer, and could be done voluntarily. The notion of a duty to reference or attribute has no impact on the validity of the claims being made. It is a collective decision to proportion prestige.
Turning the duty to do so into an unquestioned truism means it has to be done regardless of whether it accurately represents any property of merit.
There are many instances where prestige delivered grossly mismatches what an impartial observer would consider a fair balance of effort and ability.
We should at least recognise that this is so because we have chosen to let it be this way.
Completely agree with this. Math, and especially related studies like probability/stats, are presented as laws of the universe, when quite often they represent branching paths that could have gone a different way. What we take as correctness is often just one specific metric, or one justifiable epistemological pathway, among many, and it's worth understanding why we ended up where we are, and what those other forking paths might have been, and often still are.
Getting funding can be quite difficult at times, so you'll see some portion of researchers (or mathematicians in this case) take the dollars they can get.
> 2. is nothing about "following established traditions"
> undermine the traditional system of attribution
Literally does.
Suffice to say, I find your interpretations to be surprising and disconnected and it has not changed my views.
Even Grigori Perelman took years to have his proof of Poicaré conjecture accepted, and he had a Ph.D. and a Berkeley fellowship.
Expecting a human author to put substantially more effort into a proof than needed to verify it is oversimplifying. It is more a matter of credentialing and collegiality in mathematics whereby someone’s reputation and work-product demonstrates that a purported proof put forward for review is likely to be true or at least a valuable or interesting contribution even if imperfect.
AI makes this a much bigger challenge.
> or even writing a thesis, is like climbing Mt. Everest. A lot of the value is actually in the effort you put into it.
As an analogy, in the music industry, if you need a jingle written, you wouldn't care if someone spent five minutes or five years writing it. AI is now filling that formulaic space very well. It won't replace the top end of humans output but it completely outdoes all the boilerplate stuff humans take an age creating
I don't think there's any computer system which autonomously come up with new directions in openings. As far as I know; a GM looks at stockfish's evaluation of the top x moves and analyzes one that hasn't been played a lot etc.
Notably you don't seem to be looking at either the list of identified values or their recommendations to researchers in their use of LLMs, which would seem much more important to engage with in any non-shallow dismissal of the document as "feel[ing] like gate keeping and resistance to change".
It's also kind of a bad look (and actively harmful for discourse) for people working on AI to be so dismissive of fields actively engaging with how their field is changing due to AI. I haven't seen any other field engaging this actively with its possible futures, have you? Usually we seem to only get some extreme of over-hyped utopia, doomerism, or dismissal of everything as slop.
Despite the fact that there are some dishonest researchers, who attempt to create the illusion that their work is revolutionary and does not owe anything or only very little to predecessors, the truth is that almost anything novel that is published today contains only a few percent of truly new ideas that are grafted on a big body that combines many results extracted for older works.
If a new research paper separates properly its new elements from the old elements, which are properly attributed, enabling the search of the original sources, that can help a lot the readers to understand it.
I consider the fact that when you ask a question to an LLM, it is unable to accurately provide the source of the answer, as their greatest defect.
In combining the parts you have the correct answer to a question, but is it that question you want to know?
Consider a proof that in the future all people will be happy.
You can methodically show this to be true but at the same time inadvertently include a proof that the number of people in the future will be zero.
It doesn't make the claim wrong, it stays undoubtedly true. It's just not what you assume it means.
> Can't all proofs be eventually broken down into their fundamental pieces and then it's clear as day if it's right or wrong?
The proof that there are 26 of these sporadic simple groups is part of the theorem known as the classification of finite simple groups, sometimes known as the “Enormous Theorem”.[1] It took over 100 mathematicians nearly 50 years and resulted in hundreds of papers. Even with that many mathematicians involved, there were still errors and revisions needed to the original proof. Some of the original authors are gradually publishing a somewhat simplified version of the proof but it’s still a massive effort.
[1] https://en.wikipedia.org/wiki/Classification_of_finite_simpl...
Plagiarism is a minor concern in research papers. Plagiarism means that the paper has 0% new content and everything in it is extracted from older works and combined.
However, even very valuable research papers may have only 1 new idea or only a few new ideas, which can be applied only together with a much greater amount of old knowledge, which might be mentioned explicitly in the paper, with proper attribution, enabling the reader to search the original sources, or the necessary old knowledge may be implicit, the writers assuming it to be known by the readers.
It is important that the writers should not imply that some old idea is new, by omitting to attribute it, but much more important is that when they mention some ideas as being old, they must also provide sufficient information about the sources of those old ideas, so that the readers will be able to access them.
This declaration calls for action to address the challenges posed by the use of artificial intelligence within mathematics research. It is the result of a community initiative and is endorsed by the International Mathematical Union (IMU).
Technological developments have repeatedly transformed the practice of mathematics. Recent artificial intelligence technologies, including symbolic and neural methods for the generation and formalization of mathematics, may already have initiated a significant chapter in this long history. Among researchers, artificial intelligence has produced a wide range of reactions: enthusiasm for its potential to yield new discoveries; intimidation by the pace of developments; indifference to these rapid changes; and concern for the implications, both for mathematics and in wider society.
Mathematicians have a choice about whether and how to adopt artificial intelligence in the conduct of their research. They also have a responsibility to ensure the continued flourishing of the discipline. This Declaration calls upon mathematicians to exercise this responsibility, and provides recommendations for individuals, institutions, government, and industry.
Although we adopt the perspective of mathematical research, much of what we write applies equally to other aspects of mathematics. This includes work in the broader mathematical sciences, education, mentoring, publishing, funding, science policy, and use of mathematics in the wider world.
The Declaration is conceived in solidarity with other research endeavors and creative professions facing similar challenges, both within and beyond academia. It complements other calls for action such as the Uppsala Code of Ethics for Scientists, the San Francisco Declaration on Research Assessment, the UNESCO Recommendation on Open Science, and the UK Universal Ethical Code for Scientists. The International Mathematical Union Committee on Publishing, the Society for Industrial and Applied Mathematics, and the American Mathematical Society have also produced related material.
We base our recommendations on what we take to be characteristic values of mathematical research that we have a joint interest in preserving. Among these are the following:
These characteristics of mathematics as a subject matter are also compatible with understanding mathematics as a human practice, and its place in the world. As mathematicians, and also as inhabitants of a shared world, we have a duty to care for other people and our environment.
Recent developments in artificial intelligence threaten each of these values, often in ways that disproportionately affect students and early-career mathematicians, and hence the long term future of the discipline.
All of these challenges arise at a moment when the consequences of large-scale investment in artificial intelligence are being widely discussed in regard to warfare, mass surveillance, political disruption, and environmental damage. These raise grave ethical concerns. By failing to act, we run the risk of becoming complicit in the support of technologies which threaten much more than the practice of mathematics.
We thus feel that there is an urgent need for a considered response from the mathematical community. The following constitute brief descriptions of actionable recommendations. We encourage professional organizations to endorse this Declaration, and to add provisions according to their own values, priorities, and governance.
Transparently disclose the use of automated tools, including large language models, machine learning systems, proof assistants, and other mathematical software. Include a “Tool and computational resource disclosure” section in your papers; many journals, publishers, and professional organizations have already developed guidelines for this, and though the precise form of such a section will necessarily evolve, we encourage authors to live up to the spirit reflected in the UNESCO Recommendation on Open Science and the FAIR principles. When acting as a reviewer, abide by publisher guidelines. If the use of artificial intelligence is allowed, be transparent about how you used it, and take responsibility for any significant recommendations you make.
The use of artificial intelligence in preparing papers can introduce material that makes reviewing more demanding. Make it easier for your peers to review your work by disclosing tool use, giving precise and complete references to previous results, and providing formal proofs where feasible and appropriate.
The international open science movement aims to make scientific research transparent and accessible to all. As mathematical research becomes more reliant on data and software, adhere to principles of open science. See also the UNESCO Recommendation on Open Science.
When automated techniques are employed in published mathematical research, the responsibility for the correctness and adequacy of the arguments and results, as well as for the completeness and accuracy of citations to relevant prior work, remains exclusively with the human authors.
Credit and responsibility continue to belong to humans within the mathematical community and should not be given to automated systems. Artificial intelligence may obscure, but does not replace, the collective human labor behind a result.
The known limitations of automated tools in properly attributing ideas create a corresponding obligation for proactive effort to find and credit the sources that made a new result possible. Where a satisfactory attribution is not possible, state this explicitly in the publication.
Mathematicians have a responsibility to support serious science journalism and to engage in public discourse to explain and contextualize artificial intelligence-assisted methods and results. This is particularly important for work within our own subfields, where specialized knowledge is required to assess claims about the depth, difficulty, and significance of results. Moreover, we encourage mathematicians to seek opportunities to cooperate with and support other researchers and creative professionals facing similar challenges.
As appropriate to your interests and research, stay informed about the capability of computer-aided mathematical tools. Such understanding is important for informing how our discipline adapts to new technologies and for participating in governance and public discourse.
The growing intersection of artificial intelligence and mathematics continues to attract researchers from other disciplines. We welcome this broadening of our community and the range of skills and perspectives these contributors bring. We encourage the mathematical community to actively engage with the broader community, to make our standards and practices explicit and accessible, and to create pathways for meaningful participation. In turn, we ask those entering our field to approach it with respect for our values, while also helping us to adapt and develop them.
Some automated tools and their developers will align with the provisions of this Declaration, while others will not. Consider this when deciding which tools to use, or whether to use them at all. Also consider whether non-proprietary, energy-efficient, or small-scale systems suffice for your task. If not, consider how preservation of the values articulated in this Declaration may be worth a delay in obtaining results.
Mathematics has led to technology which greatly improves everyday life for many people, yet it also has applications in the development of technology for use in warfare, oppression, mass surveillance, and the undermining of democracy. Evaluate the ethical consequences of your research to the best of your abilities, and if necessary withdraw from harmful work. Only enter into external partnerships which respect the values articulated in this Declaration.
Professional organizations should keep abreast of technical developments and be proactive in making informed recommendations to members and to the broader community. They should work together to guide the development of policy within academic publishing, funding bodies, and government. They should also actively prepare to become involved if major mathematical results are claimed using unconventional means.
Professional organizations within mathematics should take a leadership role in developing guidelines in regard to the use of automated techniques in publication and in reviewing. These would include, for example, tool and computational resource disclosure, attribution, rules pertaining to authorship, and codes of conduct consistent with the values of mathematics. These would supplement and support guidelines already being developed by publishers and journals.
When establishing policies, demand that results obtained by automated techniques be held to standards that address the risks raised by those techniques. These might include requiring human descriptions of central arguments obtained by automated tools, insisting on formal verification when appropriate, cross-checking theoretical and computational results, or external pre-submission review.
Automated mathematics presents new challenges to the rights of authors, and societies should be proactive in the development of sample licensing agreements to protect these rights. In particular, material should not be used as training data without consent, and publishing agreements should allow authors to opt-out of the use of their work in this way.
Demand that mathematical results continue to be published in peer-reviewed venues such as journals, proceedings, and books. Informal mechanisms such as press releases or blog posts can provide a valuable supporting role, but they cannot replace peer-review or community scrutiny.
Support the formation of university-based, national, or international research laboratories devoted to studying automated mathematics which are administratively and financially independent from industry. Support the use of less resource-intensive technologies accessible to individual researchers.
Mathematicians and academic organizations collaborating with industry often face asymmetries in their bargaining positions, as well as in access to professional support such as legal resources, or advice on intellectual property. Support researchers in such collaborations by providing access to legal representation, and by facilitating the development of codes of professional practice.
Alignment with the values of this declaration should be taken into account in the evaluation and funding of projects which involve collaboration between academics and industrial partners.
Strengthen legal protections for authors, in line with this declaration.
There is currently a strong commercial incentive on the part of the technology industry to overstate the capabilities of their products. Consult with experts, including mathematicians, in forming policy decisions rather than relying on press releases or popular reporting of mathematical results.
Recent developments continue to highlight the strong public interest in regulating the technology industry, for example in regard to involvement in military and mass surveillance programs, development of technologies which promote misinformation and undermine democracy, and environmental costs. We stand with others in calling for significantly increased public oversight.
Current events illustrate the need for public alternatives to proprietary technologies, from basic services for online collaboration, to computer clusters for mathematical modeling and machine learning applications. We support the funding of public infrastructure at university, national, and international levels.
While the mathematical community has recognized standing in academic and public policymaking, it has no comparable role in the corporate decision-making that is playing an increasing role in our discipline. Nonetheless, recent developments have drawn mathematical work into industrial artificial intelligence efforts in multiple ways. One is through the use of mathematics to advertise the capabilities of commercial artificial intelligence systems in public communications and public relations campaigns. Another is that artificial intelligence developers have increasingly used mathematical publications and formal mathematical libraries as sources of training data — not only for specialized models for mathematics, but for more general-purpose artificial intelligence.
What currently makes mathematics attractive for general-purpose artificial intelligence development is that the correctness of formalized proofs can be checked automatically, without the need for human oversight. This makes it possible to generate and check vast numbers of problems, both human-authored and computer-generated, to produce an effectively unlimited source of feedback for training artificial intelligence models. The rationale for this strategy often rests on a further assumption: that capabilities developed through mathematical theorem proving will extend to broader general reasoning. Some of the resulting general-purpose models are being commercialized for applications that raise grave ethical concerns, including those named earlier: warfare, oppression, mass surveillance, and the undermining of democracy.
We recognize that industry has offered lucrative jobs, monetary rewards, computing resources, and intellectually stimulating opportunities that some mathematicians have found attractive. This has taken place in an era of underfunding of higher education and precarious academic employment. We also recognize that many mathematicians did not expect their work to become entangled with social and ethical implications of such magnitude, nor to be incorporated into systems used for purposes they may find deeply troubling.
We call on collaborations between mathematicians and industry to abide, at minimum, by the standards we expect of our colleagues and that are described throughout this Declaration. Such collaborations must respect the freedom of conscience of employees or contributors to speak openly about corporate policies and priorities.
Jarod Alper
University of Washington
Michael Barany
University of Edinburgh
Alain Chavarri Villarello
Vrije Universiteit Amsterdam
Sander Dahmen
Vrije Universiteit Amsterdam
Walter Dean
University of Warwick
Karthik Ganapathy
University of California, San Diego
Michael Harris
Columbia University
David Holmes
Leiden University
Mateja Jamnik
University of Cambridge
Steven Kelk
Maastricht University
Bryna Kra
Northwestern University
Ursula Martin
University of Oxford
Bartosz Naskręcki
Adam Mickiewicz University
Warsaw University of Technology
Rodrigo Ochigame
Leiden University
Jim Portegies
Eindhoven University of Technology
Johannes Schmitt
ETH Zurich
In September 2025 the Lorentz Center at Leiden University in the Netherlands hosted a conference entitled Mechanization and Mathematical Research. The around 60 participants from 10 countries comprised mathematicians, computer scientists, philosophers, historians and social scientists, including those with experience in industry and in government.
During the eight months following the conference, a smaller working group developed this Declaration, with extensive feedback from the mathematical community. The Declaration reflects artificial intelligence technologies and mathematical practice as of May 2026. The working group was convened by Jim Portegies, who can be contacted for any further information.
The authors of the Leiden Declaration would like to thank the many members of the mathematical community who gave valuable feedback on an early draft of the Declaration.
We will share curated updates about the Declaration, including press releases, media coverage, talks, institutional endorsements, and launch announcements.
International Mathematical Union (IMU)
We take the rapid development and impact of Artificial Intelligence on our discipline very seriously: It opens new and exciting opportunities, but it also raises questions that cannot be left unexamined. By endorsing the declaration, the IMU affirms that the future of mathematical research must be guided by human judgment, fair and transparent practices, and the shared values of the global mathematical community. Mathematics is, and should always remain, a profoundly human endeavour.
Peter Scholze verified Director, Max Planck Institute for Mathematics
This is a wonderful declaration, coming at the right time. The goal of mathematical research is human understanding of mathematics, and so mathematics can only thrive in a community of human mathematicians. It is crucial to preserve this communal spirit. In my experience, mathematical ideas, like children, must be nurtured and grow over the years. Just like I do not want my children to be educated by AI, I am pondering my mathematical ideas without use of AI, and generally avoid reading AI-generated text as best as I can.
Terence Tao verified Professor, University of California, Los Angeles
This has been the result of months of community input about the fundamental values and goals of the mathematical community. In retrospect, these were questions we should have been systematically discussing years ago, but in any event the exercise was extremely valuable, and the end result is excellent. I wholeheartedly endorse the statements and recommendations in this declaration.
Robbert Dijkgraaf verified Distinguished University Professor, University of Amsterdam; President-Elect, International Science Council; Former Minister of Education, Culture and Science, The Netherlands
Scientific research is in the midst of a major transformation through the impact of AI, and mathematics is one of the disciplines most fundamentally affected through automated proof generation and machine-generated reasoning. It is therefore more important than ever that the mathematical community comes together to establish clear guidelines and shared standards, to safeguard not only the practice of mathematics but its deeper purpose: the cultivation of understanding, judgment, and human insight.
Ilka Agricola verified Chair of the Committee on Publishing, International Mathematical Union
AI is fundamentally transforming the way we do mathematics, and it is doing so at incredible speed. It offers fantastic possibilities when used honestly and competently as a ‘research assistant’. But this seems to be only a small part of what is going on: the by far larger part is a total mess where science itself is under attack. In this situation, it is essential—and courageous—to take a step back and ask ourselves: What does it mean for us as a community to conduct mathematical research in this context? What principles should guide us, and what dangers do we see? For the dangers are real: The advent of sophisticated AI language models has made it cheaper and easier than ever to produce fake research articles whose sole purpose is to be counted as ‘published’ and cited, rather than actually read, and the peer review process as we used to know it is endangered. The IMU Committee on Publishing is deeply worried by the current situation, and hence we strongly welcome and support the community effort that led to the Leiden Declaration. Mathematics as we know and love it is at stake!
Jeremy Avigad verified Professor of Philosophy and Mathematical Sciences, Carnegie Mellon University
As AI plays an increasing role in our decision-making processes, it’s as important as ever to keep mathematical reasoning and justification in the loop. The Leiden Declaration offers a helpful framework for mathematicians to decide how, when, and whether to engage with the new technologies.
Kevin Buzzard verified Professor of Pure Mathematics, Imperial College London
Mathematicians should find it quite striking that tech companies are suddenly interested in their work. The Leiden Declaration is a well-thought-through response to what is currently happening, as AI continues to disrupt this space.
Leslie Ann Goldberg verified Head of Computer Science, University of Oxford
The Leiden Declaration identifies an important way in which the incorrect use of AI may harm the progress of mathematical research: ‘Current automated techniques can produce plausible but unreliable (or even incorrect) arguments which are difficult to distinguish from correct mathematical proofs.’ This is a serious problem: research in mathematics (and in mathematical disciplines like theoretical Computer Science) almost always builds on previous research, so it is essential for researchers to know that the results in the literature are correct. Inaccurate AI-generated drafts are cheap to produce, and there is a risk of cluttering the literature with claimed results that are simply wrong. Once that happens, the errors are likely to propagate as new results are built on faulty foundations. I welcome the recommendations of the Leiden Declaration, particularly the disclosure of tool use, and continued publication through peer-reviewed journals.
Steven Strogatz verified Distinguished Professor for the Public Understanding of Science and Mathematics, Cornell University
AI has the potential to become a powerful partner in mathematical discovery. That power brings new responsibilities. The Leiden Declaration calls on mathematicians to protect what makes our subject trustworthy and illuminating: proof, attribution, and the quest for insight.
Scott Aaronson verified Professor of Computer Science, University of Texas at Austin
1051 Signatories
Bastian Hilder ORCID Postdoc, Technical University of Munich
2026-06-03
John M. Lee ORCID Professor Emeritus, University of Washington
2026-06-03
NAHUEL ALBARRACIN ORCID Teacher in charge of practical assignments, Universidad de Buenos Aires Facultad de Ciencias Exactas y Naturales
2026-06-03
Deepak Bal ORCID Associate Professor, Montclair State University
2026-06-03
Ye-Kai Wang ORCID Associate Professor, National Yang Ming Chiao Tung University
2026-06-03
E. Javier Elizondo ORCID Associate professor, National Autonomous University of Mexico (UNAM)
2026-06-03
Alfred Müller ORCID Professor, Universität Siegen
2026-06-03
Yousef Abdurahman Aburawi ORCID Assistant Professor, Misurata University
2026-06-03
Stephen Doty ORCID Professor, Loyola University Chicago
2026-06-03
Laura Fredrickson ORCID Assistant Professor, University of Oregon
2026-06-03
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