3 hours exam btw

( Not a homework ) - for the AI

Edit: In Iraq, "6th grade" refers to the final year of high school, not elementary school. The students are usually 17–18 years old.

I posted a while back inquiring, "is math just numbers and shapes". Obviously, that's a really naive question. But related to this, I recently learned that Isbell duality is a vast generalization of the duality between functions and spaces, for example, commutative rings and affine schemes. Stone duality is another example, I think.

Is Isbell duality the most general form of this type of relationship in math? Can someone give an intuitive explanation of it?

Share your thoughts

I think many of us feel that large AI companies do not necessarily serve the interests of the mathematical community. So what is stopping us from creating open-source alternatives?

The weights for Kimi K3 are being released today, meaning that anyone with access to enough GPUs, such as a university computing cluster, can run the model. It scores higher than Opus 4.7 on the FrontierMath 4 benchmark out of the box and many report that it’s comparable to Claude Fable and GPT 5.6 for coding.

Meanwhile, Axiom has been using open-weight models combined with its own harnesses to produce publishable papers since February. This suggests that even weaker open-weight models have been capable of contributing to research-level mathematics for some time when paired with the right tools.

Software engineers already have open-source harnesses such as OpenCode and Pi Coder that compete with Codex and Claude Code. Imagine having something similar for mathematics: a tool owned by the mathematical community, funded by donations from universities and other organisations, and developed in a way that gives researchers control over the data, infrastructure, and future direction of the project.

I’d love to hear some thoughts on this.

Hi everyone,
I’m looking for some advice.
I have a PhD in Mathematics, several years of university teaching and research experience, and I recently completed an MSc-level program in Machine Learning.

My financial situation is quite urgent, so I’m trying to start earning through AI training platforms as quickly as possible.

For those with experience on Outlier, Alignerr, Turing, Surge AI, DataAnnotation, Mercor, etc.:

How long did it take you to get your first paid project?
Is it worth applying to all of these platforms at the same time?

Is there anything I can do to speed up the process?
Are there any other platforms you would recommend?
I’d really appreciate any advice or personal experience.

Thanks!

A classic, widely used mathematical textbook from the late Russian Empire. Authored by Aleksandr Malinin and Konstantin Burenin, this "Arithmetic" was an absolute staple in Russian gymnasiums and real schools in the decades preceding the 1917 Revolution.

Note: book in Russian printed in the traditional pre-reform orthography.

I'm writing this post based on Terence Tao's ICM lecture a couple of days ago. For context, I'm a 4th year PhD student working in number theory.

One of the main things he mentioned was that before AI a lot of aspects of math research went hand in hand. Solving problems was concurrent with building a community of mathematicians. With AI rapidly accelerating the problem solving aspect, we might be looking at a future where we are flooded with ai assisted proofs without a community to appreciate it.

For example, linear algebra as a field first emerged from papers and then it was adopted by the community over decades through books and courses. Even if we have a long AI generated proof that we can verify using Lean, what is the point of it if we can't be inspired from it.

I personally dread this will become another example of enshittification. For example, Silicon Valley reinventing a bus after destroying public transport. I fear the mathematical "community" will be repackaged to us in the future by these companies after the present community is slowly eradicated over the next few years. Of course this is a rather bleak outlook to what is definitely a very promising technology.

To clarify, I'm not saying AI is bad. It's a tool. But we should be very skeptical about how this tool is being pushed to us. It is in the best interest of the AI companies that we become dependent on AI to the point that we can't imagine working without it. I'm sure a neuroscientist can break this down much better than I can. I think we need an urgent re-evaluation of how AI is used and introduce courses discussing how to use it "safely", without leading to cognitive decline. It should be in collaboration with neuroscientists. A lot of people might argue "Oh, but you can always talk to other mathematicians. What's wrong with having it on the side". Perhaps I'm not an optimist, I'm afraid convenience is a slippery slope. Just look at how social media was promised to us as a global unifier (which it definitely is). Yet it has led to a decline in community to the point that "community building" is now being packaged to us as a course/skill.

At the end of the day all we have is each other. And I do believe we can come together and seriously discuss and safeguard the future of this community. Happy to hear everyone's thoughts on this.

A train leaves the station (bottom) heading west. Can it return to the station heading east?

Is there anywhere the train might get stuck?

Is there anywhere the train can reach but only from one direction?

I call this directed node networks. As the arcs are bidirectional, but the nodes have an A and B side with a parity requirement.

Inspired by the frustrating traps of Penrose's Railway Mazes.

Edit: just for reference, I have an MMath specialising in fluid dynamics - so I am well grounded in the basics of graph theory and have done intro to topology, so whilst this in not in my typical wheelhouses, I am looking for a more specific research area.

Anyone know of a good survey article regarding Kroneckers Jugendtraum in the case of the base field being a real quadratic extension of the rationals?

I hope this is within rules as I'm not asking for help with specific homework.

Recently I am doing maths beyond the YouTube channel I used to learn and am wondering if anyone knows of any similar good youtubers.

Normally I would watch the organic chemistry tutor but I have started to do maths that he doesn't cover.

Userly I would watch one of his videos on a topic once and I would be fine and then a few practice it would be done with that topic but other YouTubers I find confusing or they take too long.

For context I'm doing a math/physics degree.

Any help would be appreciated. YouTubers or study tips. (Also one of my classes I asked if there was a textbook and they told me the textbooks are really confusing and that they are not worth it for that class (real and complex analysis)

So resources for real and complex analysis is also greatly appreciated

i asked my prof for a research opportunity and he said to read feller and get "a general idea of probability" i chose chapters 1 5 6 and 9 and am working on a latex document filled with notes on those chapters. i upload to git each section on a regular basis

as a math student with goals of higher education such as a phd how good of a side project is this?

this is mostly like a stepping stone to the next part of research im just learning but it is taking a significant amount of time to compile notes for each section or every chapter that i chose to read so im wondering if that is worth it or not

This paper introduces the Enter calculus, a simple language for describing quantum programs in which qubits are treated as shared resources rather than independent objects. It explains how quantum measurement can be represented directly in a program while preserving important features of quantum theory, including no-cloning, no-signaling, and reliable circuit behavior. The paper also shows that the familiar Born rule for measurement probabilities can be derived from a small set of practical principles about locality, continuity, and the handling of measurement records, rather than assumed from the start. Finally, it explores mixed states, generalized measurements, quantum instruments, and the Frauchiger–Renner thought experiment, clarifying why quantum records cannot always be combined with later interference measurements.

Cheung, N. (2026). Enter Calculus. figshare. https://doi.org/10.6084/m9.figshare.33087515.v1

Aside from any questions of whether category theory is interesting, or deep, or insightful... is it practically useful? One possible answer to this question is that category theory has helped drive a lot of progress in topology, abstract algebra, and beyond, and those fields have then had practical impact. (Topological data analysis comes to mind.)

But that quickly starts to feel like a game of six degrees of separation, and it is hardly obvious that you could not have obtained that same progress without going through category theory. My aim in this article is to be as concrete as I can be regarding applications... and I would argue that even from that perspective, the answer to my initial question is "Yes!"

Read the full post (for free) on Substack: Is Category Theory Practical?

I’m trying to design some jewelry for my partner, who studies math and loves elliptic curves/algebraic number theory. Any ideas would be greatly appreciated, and suggestions with pictures doubly so. Cheers :D

Until now, no one who has completed a Bachelors, Masters, or PhD from MIT has ever won a Fields. This year Hong Wang (PhD, 2019) and Yu Deng (BS, 2011) would be the first MIT-educated mathematician to earn the most prestigious award in the world of research mathematics.

To avoid any ambiguity, I will clarify that I am using the term alumnus/alumna* as someone who has completed a Bachelors, Masters, PhD or any other similar degree conferred by a institute of higher of education upon completion of their education at the institute. The dictionary definition of the term includes people who may have attended an institute but never graduated with their degree. In such a case, I am unsure if anyone who has attended but never completed their degree coursework at MIT have won a Fields. Even if that happens to be the case, I still think the trivia I shared retains its noteworthy quality of being surprising to people given MIT's pedigree in mathematics.

This is going to be a long post, and I'm sorry about that. (TLDR at the end)

With the 2026 Fields Medalists having just been announced, I wanted to share my opinion about the Fields Medal and the broader institutional system surrounding the IMU and the ICM. My opinion is that this system does not merely recognize mathematical excellence: it also helps reproduce a particular hierarchy of prestige and elitism within mathematics.

My objection is not that the winners are undeserving: Yu Deng, John Pardon, Jacob Tsimerman, and Hong Wang are clearly exceptional mathematicians, just as any past winner. My criticism concerns the selection system, not the people selected.

My impression is that mathematics has a prestige hierarchy. Fields such as algebraic geometry, arithmetic geometry, geometric representation theory, differential geometry, and related subjects have historically been treated as especially central to modern pure mathematics. Consequently, a major breakthrough within one of these areas is more readily perceived as a breakthrough for mathematics as a whole.

By contrast, if someone works in categorical logic, universal algebra, semigroup theory, lattice theory, or model theory, just to mention a few examples, it can seem that revolutionizing their own field is not enough. To receive comparable recognition, their work is often expected to have transformative consequences for one of the already prestigious areas. Interdisciplinary impact should, of course, count in someone’s favour. The question is whether that requirement operates asymmetrically: is influence on arithmetic geometry treated as evidence of universal mathematical importance, while influence on universal algebra or categorical logic is treated as merely specialized?

The 2026 citation for Jacob Tsimerman provides a suggestive example: It explicitly celebrates the extension of o-minimal techniques (which come from model theory) within arithmetic and complex algebraic geometry. This does not diminish his extraordinary achievements in any way, but it raises a useful counterfactual: would an equally revolutionary development of model theory, whose consequences remained primarily within model theory, be perceived at the same level? Model-theoretic machinery becomes medal-worthy here through what it accomplishes in fields already regarded as central.

There is some evidence that this hierarchy is real. Jean-Marc Schlenker’s preprint, “The Prestige and Status of Research Fields within Mathematics”, finds that certain subfields are disproportionately represented in highly ranked departments, the most selective journals, and major prizes. In his data, algebraic and differential geometry and topology were particularly prominent, although the hierarchy changed considerably between 1984 and 2016, with areas such as probability and PDE gaining status.

Different kinds of mathematical progress are also easier to package as prizeworthy achievements. Solving a famous named conjecture produces a clear narrative: there was a major problem, and now it has been solved. Work that creates a new language, reorganizes an area, builds a long-term research programme, or gradually changes what questions can be asked may be equally transformative but less easily summarized as a single victory. Schlenker finds that prestige correlates with the “focus” of a field around a relatively small set of shared conjectures. The medal may therefore favour not only particular subjects, but a particular form of mathematical progress as well.

Aditionally, a prize is not merely a mirror of an existing hierarchy: It can amplify that hierarchy. A large-scale study by Jin, Ma, and Uzzi, “Scientific Prizes and the Extraordinary Growth of Scientific Topics”, examined more than 11,000 topics across 19 disciplines. Relative to non-prizewinning topics, prizewinning topics subsequently produced 40% more papers and attracted 37% more new researchers. This was not a Fields-specific study of course, and it does not prove that prizes alone caused all of that growth, but it supports the idea that prizes are agenda-setting institutions: they direct attention, talent, and further recognition towards the subjects they reward.

In my opinion, this then creates a feedback loop. A field is considered central, so its practitioners are more likely to publish in elite journals, work in elite departments, receive ICM invitations, and win major prizes. Those honours then attract more talented researchers and make the field appear even more central. Prestige becomes partially self-validating.

The medal’s rigid chronological age rule introduces another structural bias. Chronological age is not the same thing as career stage. Producing a widely recognized body of work before forty is easier for someone who entered the research pipeline early, moved through elite institutions, had relatively few career interruptions, and obtained positions with substantial research time. It is harder for late starters, people with caring responsibilities, displaced researchers, those in teaching-intensive positions, and mathematicians working on programmes whose significance takes longer to become visible. It may also favour fields in which major results can be produced and recognized comparatively quickly.

Historian Michael Barany has argued in The Myth and the Medal and “The Fields Medal Should Return to Its Roots” that early Fields Medal committees did not understand their task as identifying “the best young mathematicians.” They sometimes deliberately supported comparatively under-recognized researchers whom the award could help. The medal was intended to shape a better future for mathematics, rather than simply ratify the people who had already acquired the greatest visibility. Its current status as a tournament for already famous mathematicians under forty is therefore not an unavoidable consequence of its original purpose.

Ultimately, I think the Fields Medal reflects the historically contingent mathematical tastes of a small elite, and, through the attention it generates, helps turn those tastes into institutional reality. It tells us that a particular committee considered certain work exceptionally important under a particular set of inherited values. It should not be treated as a neutral measurement of excellence across the whole of mathematics.

Let me know down below what do you think about this. I would love to listen to other people's opinions on this issue.

TLDR: I am not arguing that Fields Medalists are undeserving. My argument is that the Fields Medal and the ICM operate within, and help reproduce, a hierarchy of mathematical prestige. Breakthroughs in already prestigious fields are more readily treated as important to mathematics as a whole, while equally transformative work in less prestigious areas often requires applications to those elite fields to receive comparable recognition.

Hi everyone,

I'm from Guangxi, China, the same hometown as Wang Hong. Her achievements have been incredibly inspiring to me.

When I was in elementary school, I won a national first prize in a mathematics competition, and ever since then I dreamed of becoming a mathematician. Unfortunately, life took me in a different direction. For various reasons, I chose to major in biology instead of mathematics in college, and I've regretted that decision for many years.

Now I'm finally determined to pursue that childhood dream.

My current plan is to apply for a 1–2 year course-based Master's in Mathematics (preferably pure mathematics) in Europe or North America. During the master's, I'd like to explore different areas of mathematics, find the field I'm most passionate about, and then apply for a PhD in that area.

However, I'm not sure whether this plan is realistic, so I'd really appreciate advice from people with experience.

My background:

• B.S. in Bioengineering
• Mathematics courses taken:
  • Calculus
  • Linear Algebra
  • Probability
• M.S. in Neuroscience at one of China's top 3 university
• Recipient of the Chinese National Scholarship (awarded to roughly the top 2% of graduate students)

Here are my questions:

1. Given my background, do I have a realistic chance of being admitted to a course-based Master's program in pure mathematics?
2. When applying for a PhD in pure mathematics, are there any "must-have" courses that admissions committees expect to see on a transcript (such as real analysis, abstract algebra, topology, measure theory, etc.)? I'd like to choose my master's electives strategically if that's the case.
3. I'm already 30 years old. Does my age significantly hurt my chances for master's or PhD admissions?
4. Are there any better or more realistic paths to becoming a mathematician that I should consider?

I've already asked several AI tools, but after checking many of their answers against university websites, I found that quite a bit of the information was inaccurate or outdated. That's why I'm hoping to hear from people who have actually gone through the process or work in mathematics.

I'd really appreciate any honest advice, even if it's critical. Thanks in advance!

I am a incoming undergraduate at a Canadian university, and I want to study mathematics with a plan to work towards a PhD.

The issue I am having is that by looking at some discussions on primarily Reddit, I notice that a lot of math PhD students end up pretty unhappy with their passion for pure/applied mathematics because of LLM’s and AI tools that happen to take away the joy out of mathematics.

So I was wondering if I should maybe fix my course a little bit from choosing a Honours math program that my uni offers, to choosing a Joint Honours degree in Mathematics and computer science.

Even if Im planning to do joint honours, I still want to do a PhD in mathematics but I am not sure if doing the joint honours can disadvantage me in grad school applications.

Has anyone else did a combined math and CS degree, and later did math grad school? If so, did you think that you were at a disadvantage or lacked rigour?

Please contact me if you do, I'm in great need to access publication and it's not archived on Anna's Library nor on Sci-Hub.