actual papers tweeted by @isomorphisms

Joined November 2019
Notice how short the good old papers are?
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Tait colorings, and an instanton homology for webs and foams P. B. Kronheimer T. S. Mrowka arxiv.org/abs/1508.07205 Cambridge, Mass. • 2015
Thanks for the question! I don't think about the four color theorem; I try not to comment on things I don't understand (although a proof along the lines of Kronheimer-Mrowka would be awesome; see arxiv.org/abs/1508.07205) But in general I think (and of course, sinxe I am a nobody, you can vehemently disagree) a proof should be redefined to be something that brings insight and understanding, and not (just) a verifiable collection of symbols that lead to a certain truth value of the original statement. This may require overturning a century and a half of mathematician's conception of what constitutes a proof, but I think it is a worthwhile endeavour (and I also think the alternative is paperclip universe where LLM slop rules math and very few people understand wtf is going on). I think a computer assisted proof can do that (eg I have used SnapPy before). I think some nontrivial amount of rich mathematics is lost in this great project to pin down mathematics to the domain of dry formalism. This is most noxiously evident in LLM proofs with an autoformalized Lean certificate.
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"We use SO(3) gauge theory to define a functor from a category of unoriented webs and foams to the category of finite-dimensional vector spaces over the field of two elements. … non-vanishing … for this SO(3) instanton homology of webs, using Gabai's sutured manifold theory."
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Ray-marching Thurston geometries arxiv.org/html/2010.15801v2 Rémi Coulon / Rennes Elisabetta A. Matsumoto / Atlanta Henry Segerman / Stillwater, Oklahoma Steve J. Trettel / Stanford, Calif. Providence, Rhode Island • 2019–20
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After the JD: First Results of a National Study of Legal Careers NALP Foundation American Bar Foundation americanbarfoundation.org/wp… Chicago, IL Overland Park, Kansas • 2004
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Socioeconomic Diversity of the Matriculating US Medical Student Body by Race, Ethnicity, and Sex, 2017-2019 Arman A Shahriar Vikram V Puram Jonathan M Miller Varun Sagi Lorenzo Adolfo Castañón-Gonzalez Shailendra Prasad Renée Crichlow pmc.ncbi.nlm.nih.gov/article…
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An algorithm for the machine calculation of complex Fourier series by James W. Cooley and John W. Tukey pubs.ams.org/MCOM/1965-19-09… IBM / Bell Labs / Princeton • New York / New Jersey • 1965
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A Memoir on the Theory of Matrices Arthur Cayley • 1858 jstor.org/stable/108649 Philosophical Transactions of the Royal Society of London, Vol. 148 (1858), pp. 17-37 (21 pages)
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Remarques sur la notation des fonctions algébriques. eudml.org/doc/147616 A. Cayley Journal für die reine und angewandte Mathematik (1855) Volume: 50, pages 282–285
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The Grothendieck constant is 1.7...! We are excited to share this breakthrough on a 70 year-old problem discovered through a year-long human-AI collaboration. We publish both the math and AI setup because academics should shape AI-assisted research to keep it fundamentally human.
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masatohagiwara.net/recto.htm… "Recto is a 2D programming language that uses nested rectangles as its core syntax, encoding structure and recursion directly in space instead of a linear stream of text. Recto explores new ways to write, parse, and reason about code—and even natural language—spatially."
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cs.cornell.edu/courses/cs612… CS 6120 is a PhD-level Cornell CS course by Adrian Sampson on programming language implementation. It covers universal compilers topics like intermediate representations, data flow, and “classic” optimizations as well as more research-flavored topics such as parallelization, just-in-time compilation, and garbage collection. The work consists of reading papers and open-source hacking tasks, which use LLVM and an educational IR invented just for this class.
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"Understanding Transformers and Attention Mechanisms" is a very interesting paper that presents the Transformer architecture from the perspective of applied mathematics. It starts by representing text as vectors and explains mathematically how the attention mechanism processes these vectors to encode contextual information. It then develops Multi-Head Attention and shows how the main components of the Transformer architecture are constructed. The paper also discusses more recent methods designed to reduce the computational and memory costs of attention, including KV caching, Grouped Query Attention, and Latent Attention. I think it is a useful reference for anyone interested in understanding Transformers beyond their high-level architecture and in seeing the linear algebra behind modern language models. arxiv.org/pdf/2604.00965
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Terence Tao posted his ChatGPT session trying to understand the Jacobian conjecture counterexample. It's so lovely reading a slice of how his mind works, the connections he's making, etc. chatgpt.com/share/6a5fdc7a-d…
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