







WOW!! 🤯Among many jaw-dropping results, this proves NP-hardness of the Closest Vector and Nearest Codeword Problems for *polynomial* approximation factors, for the first time ever, and via a totally new approach (Reed-Solomon techniques). Amazing! https://t.co/3yh3scLX5R— Chris Peikert (@ChrisPeikert) August 1, 2026
Timothy Gowers @wtgowers on Twitter / X
AI has now solved a major open problem -- one of the best known Erdos problems called the unit distance problem, one of Erdos's favourite questions and one that many mathematicians had tried.https://t.co/SD1vVPkrHR— Timothy Gowers @wtgowers (@wtgowers) May 20, 2026
Ten advances in mathematics and theoretical computer science
OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity.

Noam Brown on Twitter / X
And yes we did try other major problems without success. Sadly no Millennium Prize problems (yet).But also, we didn’t spend a lot on each problem. It’s possible to push test-time compute much further.— Noam Brown (@polynoamial) August 1, 2026
Przemek Chojecki | PC on Twitter / X
The Growing Map of Open Mathematical Problems.We mapped 15,000+ conjectures from UnsolvedMath to show potential links between concepts.It also shows how under formalized the frontier is (less than 10%). pic.twitter.com/nm0PCpXVPf— Przemek Chojecki | PC (@prz_chojecki) August 28, 2026
Michael Truell on Twitter / X
We believe Cursor discovered a novel solution to Problem Six of the First Proof challenge, a set of math research problems that approximate the work of Stanford, MIT, Berkeley academics. Cursor's solution yields stronger results than the official, human-written solution.…— Michael Truell (@mntruell) March 3, 2026
NP-overrated
If you learned about NP-hard problems in university, your takeaway was probably this:
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
An Introduction To Robust-First Computation
Did you know there's an entire field of computer science barely yet explored? Join me at the entrance to a deep rabbit hole as we take a look at Robust-First Computation.

Computational category theory
Computational category theory by D. E. Rydeheard, 1988, Prentice Hall edition, in English

In Search of Hardness
Protocol studies, the next crypto cycle, and the next age of the world

best coding - Reddit Search!
CompactLTJ: Space & Time Efficient Leapfrog Triejoin on Graph Databases
Leapfrog Triejoin (LTJ) is arguably the most practical and popular worst-case-optimal (wco) algorithm for solving basic graph patterns in graph databases. Its main drawback is that it needs the database triples (subject, predicate, object) represented as paths in a trie, for each of the six orders of subject, predicate, and object. The resulting blowup in space makes most systems disregard LTJ or implement it only partially, which makes their corresponding algorithms non-wco. In this paper we show that, by using compact data structures, it is possible to build an index that at the same time matches the query time performance of the fastest classic wco index, and uses a fraction of the space of non-wco indices (which are much slower). Concretely, we make use of compact tree representations to store functional tries using one bit per trie edge, instead of one pointer, and further reduce the space by storing partial tries. Our most compact variant uses 5–6 times less space than classic wco implementations and 2–3 times less than classic non-wco systems. At solving queries, it is on par with the fastest classic wco system, and 30–40 times faster than non-wco systems. We further incorporate improved query resolution strategies into CompactLTJ variants, which makes it considerably faster than classic wco systems as well, on queries that do not output too many results. Finally, we show how CompactLTJ can incorporate dynamism without altering its performance, even under very demanding update regimes. We leave a public fully-functional implementation of CompactLTJ that can be directly used by practitioners.

OpenAI's Unreleased Model Astra Solves Ten Major Open Mathematics Problems
Math is hard.

In Soviet Union, Optimization Problem Solves <em>You</em>
Cross-posted at Crooked Timber, as part of the seminar on Red Plenty. That version has uglier math, but allows comments.
Google's TurboQuant landed for vLLM.cpp's KV cache – but it's not just useful for KV! A possibly more impactful implementation is for quantizing embedding vectors for RAG. github.com/oaustegard/remex is a Python and Mojo for just that purpose.
GitHub - oaustegard/remex: Retrieval-validated embedding compression. 4-8x smaller vectors, proven recall. Based on TurboQuant (ICLR 2026).
github.comNew paper finally out in @NatureComms with E. Leib, D. O’Shaughnessy, C. Gallardo, @sferrigno.bsky.social, and @spiantado.bsky.social. 📝Children across cultures discover the latent algorithms that structure what they see, even without instruction, feedback, or formal schooling.🧵 tinyurl.com/4a238m2d