killerstorm

Related: "Recursive Language Models" https://arxiv.org/abs/2512.24601

Mathematicians can only make these demands because they believe that AI labs get a lot of credibility from math results. So they propose a trade: we give you credibility, you give us funding and let us to gate-keep.

The proof itself can be studied, either with AI or not.

"Intermediate reasoning traces" might be interesting but they are not by any means requited.

This letter has nothing to do with reasoning traces anyway: they just don't want math research to be front-run by internal models, that's all.

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Are you confusing "uses solar power" with "could rely solely on solar power"?

And norther areas of what, exactly? I mean that there are areas where nuclear power would be useful (as it can displace coal/natgas).

There's not much sun in the northern areas in winter. Storage might cover 12 hours worth of usage, not 6 months of usage, including all the heating.

I got some extremely long responses from 'ChatGPT' yesterday - e.g. 10+ pages long essay whereas other models reply with 1-2 pages to the same question.

I wonder if it's a regression of 6.x Sol. They removed model indicator from 'chat' section, so it's now mystery meat model.

Also, I'm generally very angry that we don't have "this response was generated by XXX" for each response because that's pretty damn important.

There's no "imminent" danger, but right now we are on a course where ASI will be developed. There are many reasons to believe that ASI will be dangerous. There are some books you could read...

To a guy who knows nothing about nuclear physics, an atomic bomb is very abstract until it blows up and becomes very concrete. Should we wait until it explodes to consider the risk?

No, layer looping increases effective depth, but it still has to go through decode. So it's more like they increased number of layers from 100 to 200 without increasing number of parameters.

"Latent reasoning" is rather trivial - you can just replace unembed-embed step with a MLP. But labs don't do that largely because they want to read the output of unembed.

There have been a huge progress in AI reasoning in the past 2 years. GPT-4o mentioned in the article would struggle with high-school math problems, OTOH GPT-6 can solve problems beyond capability of professional mathematicians.

I'd wager GPT-6 would not depend on high-quality prompts, although it might still be good to get a trained person to enter information and do a sanity check.

There are several papers demonstrating AI is at least as good at diagnostics as fully qualified doctors. So why would NP + AI be worse? AI should compensate for the lack of knowledge.

And I'm not saying NP + ChatGPT - it should be properly calibrated system which would defer to a 'proper doctor' in more complex cases.

You need to train data for a BERT-based classifier, and then there's a risk that it will pick up specific biases from the data instead of what you want.

As far as I understand, the idea of Jev is zero-shot or few-shot classifier: it learns a lot of stuff at pre-training, but unlike a classic LLM it doesn't need to learn how to chat, so it can be much smarter at a particular size

Education system was set up in XIX century. It's not clear how much of it is necessary in XXI century.

Back in the day religious books were copied by scribes educated in a monastic tradition. Now printers can print them in a completely godless manner but the result isn't any worse.

It's low because there's no competition. MDs are like medieval guild: once you're in, you're set for life. Restrictive regulations are lobbied by MD associations, which limit competition.

Probably better solution is to upskill nurses + AI to do handle all the simpler tasks like prescribing standard treatments, etc. There's already a concept of mid-level practitioner which can be expanded.

There's basically no need for GP to be a doctor.

> my ideal sandboxing is "prevent writing to anything outside this dir but still allow reading to most things so that I don't have to manually copy things into a container/VM"

That's what Codex does out of the box, and it's not good against malware - i.e. a rogue npm packet (or even just codex after prompt injection) can read your ssh key and send it to the attacker.

Making model to consists of many small modules is inefficient on GPU, especially as routing adds data dependencies, etc, and especially with pytorch (compared to a custom kernel).

The difference might be smaller on a CPU which has limited parallelism.

But it's basically equivalent to a very deep model which might be problematic for training.

True, but I think in the end people were better off with automation. You know a lot of people were suffering even when they were fully employed.

Luddites also complain about disruption, you know

I'd say this social contract which got "broken" never existed in the first place.

Even before AI we heard lots of complaints like "I made a popular open source library which is now used by corps with trillion-dollar market cap and I don't get anything out of it; halp". There was always some kind of a conflict, now the nature of the conflict just changed

> Look like how AI slop has unrealistic physics

> Posts a link to real moon landing footage

I'd delete the article if I was you...

You know, in academia, they sometimes retract articles, even if they believe they are directionally correct

This is, basically, text-to-LoRA with some extra stuff.

I.e. it basically takes text, computes and embedding and makes a LoRA adapter out of this embedding.

Note that it is equivalent to a recurrent module attached to a transformer. Dynamically generated weights (proposed in the article) are computationally equivalent to multiplicative-gating network with fixed weights. Basically just a beefier variant of GLU operating on a slightly larger state.

Here's what Victor wrote about inets in Bend2 (on X):

> interaction combinators still parallelize better than anything else, but the graph overhead prevents us from compiling to maximally efficient assembly. bend2 is basically inets without the overhead. in a way, inets live in it architecturally, but they don't exist at runtime

From what I understand, the main difference between lambda calculus and inets is that in LC you can refer to a binding multiple times for free, i.e. call same closure multiple times, etc. In inets, you can't - they are more like physical wires where each reference costs. You can definitely see inets in Bend design here (from the guide):

> A closure is affine: it can be called at most once, even when everything it captures is Data. Only top-level definitions can be called freely.

So programming in it might be very different from the normal functional programming. Seems like a big limitations. But I guess that's what lets it run without GC, on GPUs, etc.

A lot of information here:

https://gist.github.com/VictorTaelin/77fd5a2a8a4a07e1da6157e...

https://github.com/victortaelin

A complete implementation have been released, how is that not a substantiation?

Academic people might have more trust in a paper which when through a lengthy publication process. But if you think about it, it's not a better proof than a direct access to the thing. It used to be hard to try out software but with modern tech it literally takes minutes...

That's a start-up style marketing: when you make a product you focus on a big vision and positive sides and de-emphasize weaknesses. I'm afraid that's actually 100% Victor's decision to do it this way, and it seems to be working in terms of generating hype: it got ~4k likes on X, which is a lot for a new language.

Regarding substantiation -- they released source code and demos. As far as I understand, the weakness is that proofs are very verbose as there are no strategies. etc. However, they are making a separate service for making these proofs using proprietary technology: https://bend-lang.com/bender

Victor put 5+ years of research into this. You can find many of previous versions (which use different approach, do a different kind of a thing, etc.) on the github. "Bend2" in particular have been in development for 2 years.

Calling this "a random vibecoded project" is rather disrespectful, don't you think?

Regarding the paper, he states it clearly "designed by the human author". That's not at all the same as just asking Fable to write a paper. I mean the important thing is ideas, not the way they are described.

Please tell me how "I'm glad you're having fun vibecoding" is not disrespectful?

I thought that you thought Bend web site is all that is to it and wanted to point to relevant information. But if you think that "having fun vibecoding" is an appropriate thing to say to somebody who spent many years doing research, I don't know what else to say.

Again, as a "proof of research" take a look at : https://github.com/VictorTaelin/Interaction-Type-Theory that's 3 year old, pre-dates Fable, but OMG doesn't look like a paper.

Victor Taelin has been doing interesting PLT research for 10+ years.

I suggest you read his history: https://gist.github.com/VictorTaelin/77fd5a2a8a4a07e1da6157e...

before making slop accusations. Older variant of what became Bend is 5 years old, so definitely not "vibe coded": https://github.com/HigherOrderCO/HVM1

Interesting that you mention LG and Samsung TVs.

Does it happen on Google Pixel phones?

Obviously, the quality of the walled garden depends on the maintainer. Google's quality standards are lower than Apples, but higher than LGs.

Hmm? Malware on iOS is extremely rare.

I remember in Bitcoin community ~10 years ago, standard recommendation was than an iOS wallet was secure enough (I don't recall even a single case where wallet was stolen via malware), but any private keys on Windows were strongly discouraged, as most cases of stolen wallets were on Windows.

I'd say popularity of iPhone shows which way people prefer, but you do you - what prevents you from voting with your wallet and buying a Linux phone?..