Something I learned today from the copyeditor of my book:
It is
f \colon \mathbb{R} \to \mathbb{R}
and not
f : \mathbb{R} \to \mathbb{R}
It is
f \colon \mathbb{R} \to \mathbb{R}
and not
f : \mathbb{R} \to \mathbb{R}
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RT @tallinzen: my question is, if you think I'm dumb enough to not realized your personalized email was tailored by ChatGPT, do you really…
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Francesco Orabona retweeted
Got linked to this paper by Francesco (@bremen79), it's really cool: arxiv.org/abs/2610.17343
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Francesco Orabona retweeted
Unfair desk rejections are taking place at ICLR2027. A colleague of mine was rejected simply because another person with the same name had also submitted a paper to the same conference. This is a serious issue that the ICLR organizers need to address properly.
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Francesco Orabona retweeted
Today @GoogleResearch is launching Deep Review — an agentic system designed to stress-test manuscripts, design docs, and complex proposals before submission or publication. After pilot partnerships analyzing over 15,000 research papers, we’re making Deep Review globally accessible to help researchers, engineers, and professionals bring structured critique to their daily workflows.
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Regarding the discussion around limits on papers at conferences/arXiv/etc, here's my unsolicited opinion:
Publish fewer papers.
Trivial results were always unpublishable. Thanks to LLMs, the space of trivial results is now much larger.
Let’s normalize calling out paper mills.
Publish fewer papers.
Trivial results were always unpublishable. Thanks to LLMs, the space of trivial results is now much larger.
Let’s normalize calling out paper mills.
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Periodic reminder: professors are terrible at forecasting the future. If they were any good at it, they wouldn’t be professors: they’d be making millions in the stock market.
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Does anyone want to do the same experiment with the accepted papers? 😉
Nihar Shah did a heroic experiment for TMLR: he spent 20-25 hours over two weeks interviewing authors of seemingly low-quality submissions about their own papers.
He confirmed what we all suspected: people submitting these papers have *no idea* what is going on in them.
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Francesco Orabona retweeted
Okay here is another (certainly less dramatic) "concurrent efforts" story about @icmlconf 2026 outstanding paper awards.
Peter’s post below refers to our ICML paper with Khashayar and @sitanch. He raised this point because another ICML paper, “High-accuracy sampling for diffusion models and log-concave distributions” by Chen et al., which independently and concurrently established a result similar to ours, received an Outstanding Paper Award.
The story starts on January 15, when we uploaded a paper to arxiv achieving high-accuracy (polylog(1/ε)) complexity for diffusion models.
Since Chen et al. are colleagues, they reached out shortly after: "We will soon upload a paper to arXiv with a similar high-accuracy result."
We discussed the relationship between the two works and agreed that the papers should be described as concurrent and independent.
So they added a section to their paper acknowledging that the two works are indeed concurrent and independent, and then uploaded their paper to arXiv.
Then, both papers were submitted to ICML 2026, received identical scores on OpenReview, and were accepted as oral presentations.
Chen et al. also received an outstanding paper award for "settling a long-standing conjecture in the theory of score-based sampling" (see the ICML award blog post), namely achieving high-accuracy sampling for diffusion models. Congrats to them! This award is well deserved imo, and their solution is quite elegant.
At ICML in Seoul, I was asked several times, "Why didn't your paper also receive the same recognition, since both papers resolved the conjecture independently?"
That's why one of the godfathers of our community (not @peter_richtarik) reached out to the committee to ask for the VAR 😂
I will paraphrase the committee's reply because those emails are private. Essentially, they said that it was unfortunate if there had been an omission, but that they could not revisit the decision because the selection process was too complex...
Which I understand, since they chose 2 papers out of 6,000+. On the other hand, I hope we can find a way to prevent situations like this from happening again.
Peter’s post below refers to our ICML paper with Khashayar and @sitanch. He raised this point because another ICML paper, “High-accuracy sampling for diffusion models and log-concave distributions” by Chen et al., which independently and concurrently established a result similar to ours, received an Outstanding Paper Award.
The story starts on January 15, when we uploaded a paper to arxiv achieving high-accuracy (polylog(1/ε)) complexity for diffusion models.
Since Chen et al. are colleagues, they reached out shortly after: "We will soon upload a paper to arXiv with a similar high-accuracy result."
We discussed the relationship between the two works and agreed that the papers should be described as concurrent and independent.
So they added a section to their paper acknowledging that the two works are indeed concurrent and independent, and then uploaded their paper to arXiv.
Then, both papers were submitted to ICML 2026, received identical scores on OpenReview, and were accepted as oral presentations.
Chen et al. also received an outstanding paper award for "settling a long-standing conjecture in the theory of score-based sampling" (see the ICML award blog post), namely achieving high-accuracy sampling for diffusion models. Congrats to them! This award is well deserved imo, and their solution is quite elegant.
At ICML in Seoul, I was asked several times, "Why didn't your paper also receive the same recognition, since both papers resolved the conjecture independently?"
That's why one of the godfathers of our community (not @peter_richtarik) reached out to the committee to ask for the VAR 😂
I will paraphrase the committee's reply because those emails are private. Essentially, they said that it was unfortunate if there had been an omission, but that they could not revisit the decision because the selection process was too complex...
Which I understand, since they chose 2 papers out of 6,000+. On the other hand, I hope we can find a way to prevent situations like this from happening again.
This paper should have received the ICML 2026 best paper prize (alongside the other two that did). I am serious.
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Francesco Orabona retweeted
The fundamental problem with ML conference is that there is upside to doing bad work & *zero* downside. I've unearthed paper-invalidating problems with nearly every conference's Best Paper and Orals that I've touched
(two papers forthcoming on this)
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(two papers forthcoming on this)
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PSA: Most biglab people now read almost zero papers and understand ICLR/ICML/NeurIPS to be mainly full of overclaims & fraud. (but there are a few diamonds in the rough of course)
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Francesco Orabona retweeted
I would support even stronger quotas: No more than 5 submissions per author sounds reasonable. An author is someone who has supposedly authored, at least part of a paper. Also, authors should be evaluated by their best papers, not how many they got through the process. When I see faculty candidates or researchers with 10-15 papers in one conference, it now counts as a negative signal to me.
ICLR 2027 has authorship quotas:
- No more than 20 submissions per author
- No more than 1 submission where no author has a paper previously accepted to a major ML conference
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Francesco Orabona retweeted
#AAAI2027 reportedly received at least 44,000 abstract submissions.
Opinion: The publish or perish era may be over. We have reached a point where publishing alone carries almost no signal
Opinion: The publish or perish era may be over. We have reached a point where publishing alone carries almost no signal
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I will give an invited talk on this result at the NExT-Game workshop at ICML tomorrow
sites.google.com/view/nextgame-…
Join me if you are interested in knowing the details of the proof, the mistakes in previous papers, and the ChatGPT proof.
sites.google.com/view/nextgame-…
Join me if you are interested in knowing the details of the proof, the mistakes in previous papers, and the ChatGPT proof.
I solved an open problem in optimization with ChatGPT Pro.
Consider min-max problems, where we have to minimize with respect to some variable and maximize with respect to other variables to find a saddle-point.
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Francesco Orabona retweeted
new post on harness engineering for AI self-improvement: lilianweng.github.io/posts/2026-07-…
It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter models keeps harnesses simple.
Even when many harness improvement get eventually internalized into core model, the need to specify goals and context will not disappear.
It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter models keeps harnesses simple.
Even when many harness improvement get eventually internalized into core model, the need to specify goals and context will not disappear.
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Me: LLMs are getting so good they might replace PhD students soon.
Him: Actually, there’s an easier job in academia to automate: the PhD supervisor.
Him: Actually, there’s an easier job in academia to automate: the PhD supervisor.
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