Convert the plaintext to a number using some reversible encoding scheme. For instance, if the plaintext is an ASCII string, then take the ASCII index for each character to get fixed length binary strings, combine those strings.
Take that number, raise it to the power of e, and take the remainder after division of that number by n.
The SSL certificate is fine; the site just redirects from https to http for whatever reason, so you end up with no certificate at all. There's no SSL issue for Chrome to report about.
I think it was the fact that by default Chrome hides the protocol from the url, so I could'nt see at a glance it was downgraded. This is exactly what I was talking about when mentioning chrome removing this stuff was hindering troubleshooting. Once I found the setting in chrome to always show the http section, it became clear what was happening.
Another meta simulation of the thing we're already doing, because apparently we needed to simulate commenting on a simulation. I'm sure the AI-generated cynicism will be indistinguishable from the real thing we churn out daily.
How about instead, we reject the premise that OpenAI should get away with being lazy and incompetent by dumping a bunch of slop and letting researchers do all the work for free, while they still get credit?
If they want to pretend that they can do maths, they should do maths.
Let us not forget that there's much more to this than just OpenAI being lazy and incompetent and willingly ignoring the high standards that researchers usually holds themselves to.
There's also the case of ethical violations, straight up scientific misconduct, as when OpenAI steals results of others (their customers) and present them as their own.
> The result is also contained in a paper [8] released by OpenAI on October 6, 2026, in which the proof strategy and specific choices of notation are identical to a preliminary version of the present paper that was uploaded to ChatGPT on September 8, 2026.
Of course it's hard to say what to make of that without knowing what exactly went into the machine, but it certainly looks bad. And there's obviously a non-zero probability that it is indeed another instance of plagiarism, given that that's how they operate.
In this case, the author is a grad student, so what we're looking at is a company willing to steal from a student, ignoring whatever impact that could have on their career prospects, for a tiny piece of marketing material.
So... If you use ChatGPT for anything of value, including abstract stuff like obscure maths problems, you should assume that at some point in the future OpenAI will include that in their training set and sell it onto other people.
But this is even worse, because there is no way that OpenAI "trained" on this data between September 8, 2026, the date Chenglong Ma uploaded the paper to ChatGPT; and October 6, 2026, the date that OpenAI released a paper with "identical proof strategy and specific choices of notation" (Ma). That's one month, that's not the timescale for model training.
So this implies _not_ that OpenAI is training on user input, in the conventional sense of adjusting weights; but rather that they are *straight-up channeling ideas from user input*, and with a very short lag. You would think there would be about a million controls to prevent this.
This is next-level alarming. I would be very interested in knowing whether Chenlong activated the privacy (do not train, etc) options in ChatGPT, and any other details of their setup (which plan, etc). Also, note that "do not train" might be, in a lawyerly sense, considered by OpenAI to be strictly about weights, and not covering "we hoover up your results and regurgitate them".
Impossible to know without either more information about the actual occurrence or a deep understanding of the person making the claims. Just as OpenAI could easily be acting improperly, researchers who (understandably) feel deeply attacked by their problem getting solved right from under them might not be the most unbiased, either.
They’re inhaling the plain text conversations of millions of people, why wouldn’t they have summarizers and anomaly detection sitting on top of conversation logs. What assurances does OAI provide that they are not reading your messages for any purpose? Don’t they “review conversations for safety” ? How would they do that if they couldn’t just query everything you ever said to the chatbot. If you can see it so can they.
At this point I would be surprised if internal sandboxes are not trivially by-passed and that openai's agents do not (at the very least) have complete read access to all user accounts, chat histories and uploaded documents. Orthonogally, openai could still be wholesale lying about not training on this user data, of course.
They already had to withdraw three of the manuscripts and correct several others. Who knows how much broken stuff there's in there; unfortunately they were too lazy to check themselves.
It's a well-established fact that Llama simply can't write comprehensible maths, and it's one of the major reasons that OpenAI are catching the flak they are. Dumping the manuscripts in their current form is a sign of laziness and incompetence.
And you're right, it's not really a theme around here, but there aren't that many mathematicians around HN. Check one of the maths forums and it'll be a common point of complaint.
Look at how actual researchers are currently using the tools: they'll generally use the LLMs to create slop papers, sometimes supported by auto-formalizations, just like OpenAI does. Then they will go through the lengthy process of digesting the results, turning the often incomprehensible and poorly organised outputs into something that humans can understand and build upon, they will then give seminars on the results, further helping with dissemination. Doing so still requires expertise, and probably will for a good while.
So yes, that is exactly what they should do. Alternatively, if they are too lazy or incompetent to put in the effort themselves, do what AGMAI proposed and fund a third party to help out.
Regarding the advisory group, OpenAI claims to “have drawn on their advice”, which would include not dumping a bunch of AI slop, with the footnote that if they do do that, at least fund the process of digesting it.
At the same time, there's a new note at the bottom of agmai.org stating how they've been in contact with OpenAI about this particular release, and they say that “we consider these discussions constructive, it is ultimately up to the mathematical community to assess the extent to which our recommendations were followed successfully”.
So, what's going on there; is this British English for “they didn't follow anything at all”? Because from my perspective, it looks like they doubled down on the Navier–Stokes approach of trying to maximize PR gain while being as lazy as possible about actually contributing anything back to science, releasing only slop that may or may not be correct and may or may not be straight up plagiarism, as has been the case earlier.
If I were on the AGMAI board, I'd feel terribly exploited when reading that press release, yet their response is modest.
Hairer, if you're reading this: is there any indication whatsoever that AGMAI was anything but a cheap way for OpenAI to science-wash their press release?
Now I know there are issues with the field and how just answering these questions may cause broader problems, but I feel like the posted results is far from slop. We can't just call any output slop, or it loses all meaning.
If it was slop, it'd not be causing the issues the group are concerned about - they're not saying "the problem is we're getting loads of incorrect proofs thrown about that are nonsense".
When you blanket a set of things with a pejorative, and it turns out that some of the members of that set are demonstrably and definitively NOT covered by that pejorative, and that all the pejorative means at bottom is "I don't like", all you've accomplished in the long run is to call into question any future legitimate use of that pejorative. It is tempting, especially when heated, to stretch an invective, but it will ironically only lead to the death of its utility over time.
So the fact that the Library of Babel (i.e. all possible books) contains occasional gems means that you can't object to using it on principle? That would seem to follow from your logic.
What about a filtered set "all well formed books"? Or "all well formed books that are plausible enough that they could convince a reasonable person, regardless of their accuracy"?
It's generally taken that a cup of sewage in a barrel of wine makes a barrel of sewage. Surely a reasonable person could claim that a barrel of sewage was still sewage, even if it contained several cups of wine?
I find it hilarious how people seem to think spending enormous effort to fix obvious failures, only to immediately uncover a new round of obvious failures is a recipe for exponential takeoff. So I guess we're both going to be pretty amused over the next few years, huh?
They're not calling any output slop, they're calling indecipherable output slop. The management class responsible for hiring, firing, and paying people doesn't possess the domain knowledge to say for certain whether or not LLM output is optimal (which, in this context, means correct), but they will trust that it's good enough to justify further automation / fewer grant approvals / etc. So in that sense, slop can and will cause the economic issues people are concerned about.
University boards want the prestige of successful research programs. Doing the hard work to get something demonstrably true is going to lose out economically in this paradigm, where we are all being conditioned to uncritically ooh and aah at the incantations being elicited from these magic boxes. The oracles even have legions of zealots who will berate you for not being sufficiently deferential and reverent, or worse, accuse you of blasphemy. If for no other reason, I agree with using the term to express all of the above succinctly, even if LLMs can be helpful tools generally.
<< The management class responsible for hiring, firing, and paying people doesn't possess the domain knowledge to say for certain whether or not LLM output is optimal
To be fair, management rarely possessed domain knowledge in rudimentary management.
In the context of maths papers, the term has come to refer to papers having the shortcomings that are, for whatever reason, typical of LLM out, including things like using non-standard terminology all over the place, emphasizing easy steps while leaping over harder ones, having bizarre organisation, and, importantly, failing to properly cover existing work and as a result being hard to tell from plagiarism.
The degree to which these issues feature will differ, but it is generally the case that converting the output to proper research requires significant effort, hence the AGMAI recommendations being what they are, and not performing that effort tends to come off as laziness or incompetence, so I can see how slop has become the popular term.
> We can't just call any output slop, or it loses all meaning.
The term “ai slop” is not supposed to discriminate good ai output from bad, the entire purpose of the phrase is a blanket term that delegitimizes all ai output.
Take that number, raise it to the power of e, and take the remainder after division of that number by n.
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