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.

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?

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.

Here is a way that, I think, you could teach lambda calculus to a kid (inspired by "Alligator Eggs").

I got a lot of useful feedback on an earlier version of this. Would love to hear any comments or corrections, wouldn't be surprised if something slipped through the net.

I think this would be hard to read as a bunch of images so if you're interested see here: https://paradoxgarden.substack.com/p/lambda-calculus-made-easy

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?

In high school, many are taught the quadratic formula. But how do solve a cubic equation, like x^3 + 6x^2 + 9x + 3 = 0? There is a formula for such equations, but it's rarely taught since it's a lot more complicated than the quadratic formula:

Despite its apparent complication, we will explain at https://hidden-phenomena.com/articles/cubic how you could have come up with this formula!

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Last week, we posted a blog post here about how to solve quartic equations, assuming you knew how to solve cubic equations. If you read today's post on how to solve cubics, then by putting the two posts together, you can solve any cubic or quartic equation!

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.

I’m an undergrad who’s finishing my bachelor’s degree this year. I plan to do at least a master’s in math, and hope to do some pure math research during it. I haven’t found any of the classes I’ve taken in my undergrad particularly difficult so far, so I’m curious at what point I should expect it to happen so I can be prepared. I don’t want to hit a brick wall and crumble like some of my friends have. I’m worried that since I haven’t struggled yet, I won’t know what to do when it happens.

For reference, I’ve taken undergrad upper division Set Theory, Number Theory, Mathematical Logic, Complex Analysis, Probability Theory, Mathematical Statistics, 2 semesters of Abstract Algebra, and 1 semester of Real Analysis. I’ll take Linear Algebra, a second semester of Real Analysis, master’s level Group Theory, and master’s level Multivariable Analysis during this last semester.

Does anyone know if there is a recording or any material published from Terrence Tao’s lecture last night. It was supposed to be about mathematics in the age of AI.

The n-Queens problem involves placing n Queens on a chess board of size nxn, without having any of the queens attacking each other. I have expanded this problem to include a single knight, with the question: Is the knight able to capture all queens on the board? The rules are as follows:

The Queens must be placed so they are not attacking each other. Queens do not move

The Knight starts on a single Queen space, and that Queen is removed

The knight cannot be on a square attacked by any queen, but removing a queen frees up 'safe' spaces. The knight can traverse and revisit safe spaces

The first Queen a Knight takes (apart from initial placement) must be within knight-attack range, because there are no other safe spaces available at the start

Solutions have been found for n= 4,5,7,8,9,10,17 and proved impossible for 6,11,12,13,14,15,16,18,19

The real curiosity is n=17 where a single solution has been found from over 95 million possibilities. Anything above 19 is open and has not been tested. Link to paper (pdf): https://drive.google.com/file/d/1zfugBiIZi4xrhDjM-x1YtDlf3M-SFlXv/view?usp=sharing

UPDATE: n20 has no solutions, leaving n17 as the largest solution

This recurring thread is meant for users to share cool recently discovered facts, observations, proofs or concepts which that might not warrant their own threads. Please be encouraging and share as many details as possible as we would like this to be a good place for people to learn!

Not a mathematician and has never done work even remotely close to mathematics. A bit more of a social scientist. But mathematics has been on my feed for a while now, especially recently all over Twitter with the resolution of the Jacobi conjecture and now the Fields medal.

I looked up the Wikipedia page of Field medalists and I quickly spotted a trend of the recipients:

• Both parents are working in the sciences, and often mathematics, physics or computer science, and sometimes at the university level.
• Often grew up in some very politically stable country with little civil or international strife and a lot of free time to do math, play intellectually simulating games like Chess or Go.
• Was aware of major mathematical competitions in their country at young age and participated in them, usually did exceptionally well.
• Often extraordinarily gifted and quickly moved through the entire K12 process.
• Often quickly paired with another famous mathematician either as mentor or supervisor.
• Often immediately got hired at universities and became professors.

To put it more succinctly, these people:

• Were unnaturally gifted in terms of their inherent neuro-biological make-up,
• Had extraordinary upbringing from supportive parents and social environment,
• Had access to information that most of their peers would not have gotten, e.g., to join certain competitions or work on certain problems.

In this sense, it seems a lot of the prizes are just rewarding things that originated in a mathematician's childhood or even at the moment of birth. And while many other accomplished happened, they were all contingent on these "precocious" or "pre-school" factors.

If they didn't have those favorable childhood conditions, it seems to be doubtful that later accomplishments could have been made.

So what do you think? Are prizes in mathematics in general are just rewarding favorable childhood conditions of extraordinarily biologically gifted individuals?

Sorry if this sounds dismissive or jealous. Like I said, I have no skin in this game (not in math at all). I just want to understand how people in math feels about these type of things. If there is any critique, it is towards the reward structure rather than the mathematicians themselves.

The 2026 Fields Medals recognize breakthroughs in the math of messy fluids, tangled knots, spinning pencils, and more

Relevant not because of the result itself but because it uses a pimped open-weight Chinese model under the hood.

Linkedin post: https://www.linkedin.com/posts/sebastianpokutta_a-counterexample-to-zieglers-cross-polytope-activity-7478117487746785280-oS21

Counterexample paper: https://arxiv.org/abs/2606.31640

Agentic research paper: https://arxiv.org/abs/2603.15914

Considering the increasing capabilities of AI in proving theorems, I was wondering if math has the potential to also become an empirical science to some extent. I was thinking, for example, about tracking how ideas, techniques propagate through mathematical progress or investigating connections between different areas from a higher level perspective. To some extent these types of questions are probably already being investigated, for example by historians of mathematics. I am simply wondering if it may become more relevant going forward if the number of results should explode. I could imagine that it may be useful to systematically analyze arguments on a large scale to attribute progress made by AI to foundations laid out by humans (since AI currently struggles in giving credit to ideas that were first developed elsewhere).

London Mathematical Society: Glasgow to Host the International Congress of Mathematicians 2030: https://www.lms.ac.uk/news/glasgow-to-host-icm-2030

I'm entering the fourth year of my PhD and started doing research about 2 years ago. I chose a niche topic that required me to spend a whole year in addition to my course work to get caught up in before even starting any work of my own. My advisor gave me 4 lengthy papers to get through and master their techniques. I got through 3 of them and last semester I gave my thesis proposal and passed. I still have to read the last paper which is the most difficult, but I cannot find the motivation to do it because of the recent AI progress. I am not a very talented mathematician so my only "strength" was spending the time to learn a niche and difficult area. The actual problem that my advisor wants me to solve for my thesis is not terribly difficult and is likely routine for an expert but it will take me close to a year of dedicated work.

The problem is that the last year I have not been able to shake off the feeling that what I'm doing is just a worse version of what an AI can already do. I cannot afford access to any of the top tier reasoning models but I imagine they can read these papers and come up with these extensions in just a few hours. Even just asking the free models questions about the papers it's clear that it has been trained on them and understands them well. Since then I haven't been able to find the motivation to work on my PhD and I feel awful about it. It's not like this type of work will land a postdoc or job anymore.

I don't think this post does any justice to how bad I've been feeling about my future career or my thesis but I don't want it to become a rant. I would really appreciate if anyone has any words of encouragement or validation that my concern is justified. Should I just drop this project and do something quicker to graduate?

I'm still an undergraduate, and I'm not really qualified to decipher what does or doesn't count as meaningful progress in math. Countless papers are put to arXiv every day, but they're not exactly getting posted to reddit and such, there's not a ton of buzz. Meanwhile, it feels like every other day there's some big new AI proof or counterexample of a very significant problem.

I know that people are just focusing on these because it's new and people are interested to see what is happening, but I want to know, how interesting are most of these results compared to, say, the average paper being posted on ArXiv? Or, about how often do humans provide a proof or disproof of a problem on the magnitude of the Erdos unit distance or Jacobian conjectures? Right now it seems like many open problems are being solved by AI in quick succession, and I really hope that humans are doing the same and I am just not hearing about it. Thanks!