Clément Canonne

See cims.nyu.edu/~tristanb/st... for what one side is saying, esp. page 2 onwards.

cims.nyu.edu

A list of principles put forth by mathematicians, for mathematicians and other researchers, regarding the use of AI in research. "Number #9 will surprise you!" leidendeclaration.ai

Leiden Declaration on Artificial Intelligence and Mathematics leidendeclaration.ai

Take some time (very little) to weigh on this: "Now Is the Time to Give LLMs Access to the ACM Digital Library" docs.google.com/forms/d/e/1F... (not that I want to influence anyone, but my own position is along the lines of "hell no.")

Now is the Time to Give LLMS Access to the ACM Digital Library Why ACM Believes the benefits outweigh the risks in opening up the ACM Digital Library to large language models docs.google.com

Nice second sentence. arxiv.org/abs/2608.113...

Distribution-Free Halfspace Testing with Samples arxiv.org

Our paper on Entropy Equivalence Testing was just accepted to #RANDOM2026. Congratulations to Joy (Qiping) Yang and Yash Pote, who led the charge! arxiv.org/abs/2605.23225

[2605.23225] Entropy Equivalence Testing arxiv.org

So many good points in this post by @nsaphra.bsky.social: only quoting a couple, to encourage you to read the others. "My colleagues and students adopt the writing quirks they read throughout the day, and their own writing becomes more like an LLM’s." nsaphra.net/post/uncanny/

Life on the Uncanny Precipice | Naomi Saphra nsaphra.net

FWIW, here are Terence Tao’s slides at the ICM on maths and AI: teorth.github.io/tao-web/slid... @teorth.bsky.social (I am not endorsing nor criticizing the content, but this is a useful and thoughtful set of points and views in the discussion, from someone who has deeply enga…

teorth.github.io

He did it again! My PhD student Kenny Chen has a new preprint on the arXiv, establishing (nearly) tight classical and quantum query complexity bounds for testing Fourier dimensionality, which show an exponential quantum advantage for the task! arxiv.org/abs/2609.25816

Exponential Quantum Advantage in Testing Fourier Dimensionality arxiv.org

First preprint by Jiaqi Mao, currently Masters of Computer Science student at #USyd doing a research project with me (and soon PhD student): arxiv.org/abs/2608.19538

A simple and practical $o(\sqrt{n})$-time algorithm for shortest paths in power law graphs arxiv.org

New preprint by my PhD student Abigail Gentle (@abigailgentle.com) and her coauthors on differentially private testing of graph properties! 📝 arxiv.org/abs/2609.14394 "Our main results are privacy amplification theorems for different graph sampling schemes [...] which lead to …

Private Graph Property Testing arxiv.org

(this program, by the way, was definitely far from free for Google, but great PR for them, and the TCS community obliged.) research.google/blog/gemini-...

Gemini-backed Paper Assistant Tool provides automated feedback for theoretical computer scientists at STOC 2026 research.google

For context, to ground the conversation and explain one of the issues. USD20M, which is a self-reported (likely low-balled) estimate of what OpenAI burned in *one week* in their scooping race, would fund 100 full-time postdoctoral positions for 2 years (including overhead). 100 postdocs. 2 years.

Me, reading some badly written one-shotted AI "preprint" claiming to solve an open problem in the least interesting way possible

The OpenAI breakthrough on the unit distance problem seems genuinely impressive to my semi-layman eye. (It is!) One possible takeaway, though, is that if you throw an incredible amount of money and resources at focused research, there WILL be impressive progress. I wish we tried that, too.

The "paperclip maximizer" is a silly thought experiment: an entity mistakes an (imperfect) metric for an absolute goal, and proceeds to diverting all resources to producing paperclips. Silly. In other news, Anthropic and OpenAI are burning millions in a race to establish Erdős' 9173439 conjectures.

Looking for takes by TCS (Theoretical Computer Science) senior academics/researchers about AI and their discipline/research values. E.g., essays, detailed blog posts. (Specifically TCS: there is a lot from Maths, but while adjacent there are enough differences in culture it's worth distinguishing)

This is what "research" looks like for many paper-producing groups or individuals, in light of the recent advances in LLMs/AI systems. PSA: this may look tempting! But this is not good, this is not research, and, very much like when I tried to play Quake 3 back in the day, this will not end well.

I uploaded a picture of a random root vegetable and asked ChatGPT to identify it. It told me it was a stochastic carrot.

FWIW, since reviews for a prominent Theoretical CS conference are now out: if your contribution to a PC is a bunch of verbatim AI-generated reviews and basically nothing else, I believe you should never be invited to a Program Committee again. This isn't even about AI, this is about doing your job.

If you think that AI reviews in conferences "to save time" or "be thorough" or "handle the large # of submissions coming due to AI", ask yourself how much it'll cost, and who will pay. If not "gifted" by companies, this is going to be north of $30+/paper. You expect 1000+ submissions? Budget that.

Related to my previous post about people using GenAI/LLMs for everything, regardless of whether they should, whether it makes sense, and whether what they are doing with it even is something that is worth doing in the first place.