They also tried the same with Gemma 3 270M, which doesn't loop and is just bad at math, and Astra couldn't improve it. Still impressive, but it's more in the realm of what I'd expect, given labs are probably training their models on ML research and interpretability work.

We used to get that feeling from Google, and from books.

Cool work anyways.

Others have also fine-tuned Qwen to shake this behavior already, so it's honestly not entirely shocking that Astra could too, but I think this is the first time it was done "by hand" and not via fine-tune.

Model psychiatrist is also fun, but probably less appropriate.

It’s editing behaviour more efficiently than a psychiatrist, and is in ways acting as a brain surgeon but brain surgeons don’t focus on behaviour and thought loops…

And they can write tools that help debug better than demonstrated in that blog post.

In my interpretability tinkering, Fable was the first to be able to write a program that can transpile from a lispy syntax directly to model weights of small neural nets, including transformers. Yesterday, I pointed out in a post that for many kinds of small AIs, you can avoid ML and have the LLM directly write the small neural net, compact decision tree, AD Tree or bayesian network. Since it's "hand"-written, it can be much more efficient and capable than training one of a similar size.

Early experiments show one-shotting direct weight interp, which is crazy to me. But this is IIUC only on early layers.

The later math result gave the LLM the ability to run forward passes and inspect activations, and as others have noted is more of a classic steering vector intervention. Impressive how low to the metal this one was, but it’s a lot less so than the openers. Plus, as you say this is just bugfixing a known flaw of repetition, by finding the “repetition direction” and top triggering neurons, then rewiring the neurons to fire suppressing repetition instead. This is not a new technique and doesn’t shed any mechanistic understanding on how maths works.

The work done in this experiment could have also been done by a sufficiently motivated human with enough time to explore, though obviously we would need more tools to visualize and track things. There have been a lot of interesting LLM introspection tools that allow you to peek into the model and see what's being activated and run experiments to trigger it.

Are you familiar with abliterated or "uncensored" models where weights have been modified to avoid triggering refusal patterns? You take an LLM, create test cases that trigger the refusal pattern, observe weights involved in that response, then tweak those weights until the response is reduced. This is the same type of work, applied to a specific type of error that was impeding math within Qwen. That work can also be done with an LLM in a harness running the experiments. The amazing part here is that Astra was large enough to walk through the problem without other tooling doing it experimentally.

So I don't think your analogy of a human modifying a neuron is quite applicable. LLM weights are not neurons and they've been inspectable, by humans or LLMs in a harness, for a long time.

To me “amazing” is not the right level of speechlessness. I mean, an LLM is still fundamentally a next token predictor, right? So what does this imply? That weights have some universal properties? E.g., “breakout game weights” always look the same regardless of training and model? Also, how does this meta-understanding of the weights end up in the weights in the first place? Idk, this is mind-blowing to me.

A model is _sort of_ designed to predict the next token in a string of text. But it's equally accurate to say it's designed to edit code until it passes a suite of unit tests.

Yes, they generate sequentially, so "next token predictor" isn't technically incorrect (which is why I chose the word "meaningless"). But it's pretty clear there's a mechanism inside them that allows them to "plan" what they generate. I might as well call you a next word predictor.

Which is interesting, given that life itself is merely that. It's just that humans decided that that ain't good enough and invented something alien to nature - logic.

Hm. I would actually go so far as to say "predict" is just the wrong word to use in 2026, as it drags in a picture of what LLMs are that's from the pre RLVR era, and which is frequently used to suggest they have all sorts of limitations that they've now surpassed ("an LLM will never solve a major open mathematics problem, they're just predicting tokens"). These days I tend to use the more neutral phrase "generate the next token"; I'd only say "predict" in the technical context of (say) the pre-training part of the training run where the object is indeed to minimize the cross-entropy loss function by predicting next tokens in existing text. Generating a token with a modern LLM is better described as something like, "given the totality of its training, including not just next token prediction with pre-training but also solving vast numbers of math and coding problems with RLVR, the LLM generates tokens one by one."

Anyhow. I'm not sure the weight understanding is all that strange. (It's impressive, but so are many thing LLMs do these days.) LLMs are very very good at coding and math problems, of which this is one, and in particular, their RLVR training presumably includes many tasks involving neural networks. It's amazing and impressive, I agree, but I'd say less amazing than proving major open theorems in mathematics, which we already know they can do.

Substitute ‘generate’ and the meaning is unchanged.

It's worth reflecting on the original domain jargon for selecting the next token: completion.

And yes, I understand "prediction" is a standard term in the field. I just think it is a bad one and we should stop using it.

This doesn't make it any less fascinating that by combining this basic "do as I say" training with massive amounts of all of humanity's data, you can solve major problems—that is still incredible. But that doesn't fundamentally change what is going on, mechanistically.

I don't understand these pseudo-religious desires to read into models something "more" than what we know. We built them. We don't go around making wild claims about how cars are actually more than just a mechanical system, how they aren't just taking us places—we do, but usually such points are cultural not about the mechanica capabilities of cars. People in the LLM space seem to be constantly conflating these two views. It's entirely accurate to say, on the one hand, next token predictors combined with RL, and massive troves of existing human work, can solve culturally interesting problems, and to hold, on the other hand, that mechanistically what is happening is statistical next token predictions over global linguistic space and more or less "monkey see monkey do".

Mathematics models the world, it does not have substance. It is a useful way of looking and a useful way of manipulating, but many models are equally useful. Capital T Truth, beyond models is very difficult to get at. We can show that things are true for certain models well enough. Showing that something is true in all models (a universal truth) is far more difficult. You face incommensurability.

I see a lot of people in technology fall into this extremely basic logical fallacy called Reification constantly.

I mean, when you really get down to it, either the particles and such operate on very specific rules, and the rules govern how all our brain and body work. Followed to the conclusion, the universe is computable and we can simulate life.

You can demonstrate definitively that there nothing other than "next token prediction" by running the same inference engine/model/prompt/settings multiple times in a row on the same hardware at temp 0 with a seed set.

If you turn the temp up, then you might let the "mechanism" select pseudorandomly from a probability distribution of k-likely tokens with minimum p-cumulative probability. Even then, if the next token has a very high probability, deviation will be uncommon.

I just performed this experiment right now on a few dozen runs of a dense model at thousands of tokens of output at temp 0, and all were identical. Then I did it with a MOE model, which might get some benefit of picking different experts each time. Nope. Outputs were again identical.

Describing LLMs as next token predictors isn’t incorrect, it’s meaningless. It’s the equivalent of describing brains as “a bunch of atoms”.

The problem with these descriptions is that they say nothing about high-level capabilities. If you can predict the next token in a proof of the Riemann hypothesis, then you can prove the Riemann hypothesis.

The part that really bakes my noodle: `/foo/bar/baz.txt` can be a filename that no one has ever typed before, the error inside the file can be a typo that no one has ever typed before, and it will still correctly "predict the next token" to solve your problem.

In the case of the brain, it'd be more akin to the reduction of the brain to just the neuronal dynamics (obviously experience phenomenally is much more than that to us!)

But both views can be useful, depending on what you are trying to understand.

The problem with these arguments is that people pick and choose based on their persons preferences. People who are in love with LLMs will, on the LLM side, argue against reductionism "they are way more than next token predictors!" then they'll turn around and equally try to argue "the substrate doesn't matter! The brain is "just" neurons and digital activity so of course we can replicate it!" (this is also incorrect, btw, the brain is also an analog system, even in its electrical connections and not just in its chemistry)

The on the other side the anti LLM crew will do the opposite (launch into reductionism when it comes to LLMs, resist it when it comes to humans). I'm a human being and I like humans and I think people have adopted a strange view of LLMs, which are a tool and should be treated as such, so I tend to side with the humans. The stalwart defenders of LLM honor that would balk at any attempt to reduce them in stature to what they fundamentally are, mechanistically, are... an odd bunch. I'm confused why people are so passionate about it. If someone told me "well your IDE is just a fancy AST walker" yeah I think that is a dumb take but I wouldn't feel like I had to argue against such a take either saying "no! no! it's so much more!"

A system is not just its little bits.

Where "system" and "little bits" can be just about anything. But people endlessly repeat this mistake, and it has been a go-to misunderstanding/misrepresentation for a lot of people on this forum.

if it was designed to edit code, id expect the first translation from latent space out to tokens to be the final code, not be assembling the code by random number generator.

its not really designed to edit code until the tests succeed, we try to manipulate the distribution of tokens to predict by prompt engineering with the test results.

Going back to first principles, wikipedia mentions [0] inference as:

> "Inferences are steps in logical reasoning, moving from premises to logical consequences. Inference is traditionally divided into deduction and induction"

Is that exactly what next token predictor means?

[0] https://en.wikipedia.org/wiki/Inference

Training is not design. Design is the mechanism, namely a statistical method of learning distributions, and of executing those distributions computationally as an IO function.

The model is not designed to write unit tests. It is trained to do so, i.e. the distribution learned and reinforced is one such that it maps the I distribution to the O distribution that is what human beings typically do or want when writing unit tests. The design is the suite of algorithms used for this (which is ultimately global next token prediction)

I think you are conflating design (the mechanics) with their cultural or user level function.

You are basically saying "my bike isn't just rotating wheels on a frame because it takes me places". Hopefully you can see how both parts of that statement are true and why contrasting them as if the first clause is somehow false or inaccurate is incorrect.

Both these are true statements. They operate at different levels. People are constantly conflating these levels in the "is it a next token predictor" (the answer is yes) debate.

To me this is fundamentally different from having ingested a bunch of docs so it now “knows Java” in its weights.

it probably has however been trained on papers talking about ways they extracted subsets of LLMs as say, the part that knows chess, and would be able to then use similar commands to process the weights

There are classes of adversarial attacks targeting them that don't work as well on us, and vice versa.

It's like a sorting algorithm, which I can implement in many different ways in just a few dozen lines. If you look at the binary code (so like raw weights) of the compiled program, you will see just a bunch of a different numbers, but if you read the code, in most cases you will understand that it's a sorting algorithm. So it seems they just operate on different levels of abstraction that are not available to our brains.

do they break down to a finite subset of groups?

In the case of models, the almost-theory (hypothesis with a good deal of evidence) is that once the amount of training data exceeds what the model can simply represent internally, the best way to keep improving is to start to "understand" the data. Understand in this case means to form internal control structures and decision systems that represent the data algorithmically rather than just memorizing it.

I put understand in quotes because we don't know how far this goes toward what we call understanding, or whether there's still a lot missing.

Its not natively reading the weights in the Matrix “symbols scrolling on a screen” sense. Still bloody impressive and not an outcome I would have bet on!

Yes, but everything, including the human brain, is "fundamentally" some underlying simpler primitives.

>E.g., “breakout game weights” always look the same regardless of training and model?

Wouldn't this be expected to be roughly the case, given NNs that share the same basic architecture to model the same quite simple thing like a breakout game?

https://chat.deepseek.com/share/uysgv9yl2aj0tqs3on

The prompt told Astral what the input and output layers mean -- that is, how the current state of the game is turned into a vector for matrix multiplication, and how the result of those multiplications is turned into an action to take in-game.

Astral saw that the model was trained on a game where the player is at the bottom of the grid and moves left and right, reward objects live at the top, and there's some not-player-controlled thing moving around in the middle. Astral knows that this type of game is called "breakout."

A model that plays breakout will have meaningful weights for the player being in the bottom row, but not for the player being anywhere else, because the player can't go anywhere else. Astral saw that there were meaningful weights for the player in the bottom row, and nodes representing the player in any other row had low-magnitude noise, so the model had never been trained on the player being anywhere besides the bottom row.

Similarly, breakout has "reward" objects in the top rows, and "there is a reward object at [COORDINATE]" is part of the input layer. Astral saw that the model was trained on reward objects in the top few rows.

A couple of highlights to pique people's interests:

- Attention is kind of like "Global Illumination" for next tokens (they both have a game dev background)

- Attention is constantly being recalculated _for the next token_. This part never "clicked" before.

- LLMs are "next token predictors" in the same way that games are "next frame predictors".

That's exactly what Platonic Representation Hypothesis would predict.

There also have been plenty of studies showing that various neural networks, including LLMs, develop implicit world models that reflect the geometry/structure of a task they're trained on. A trained "chess engine LLM" would have a "chessboard-shaped" world model inside it.

So, if you can figure out what the geometry of a task is, you can search the weights for correlates of that geometry. Then you can try to improve those correlates so that they track the task better.

Seems like bleeding edge LLMs might already be superhuman at "figure out geometry from looking at weights".

Even if a slight rotation passing through a nonlinearity isn't good enough, you can just reorder the vector components which makes literally no difference.

A "reorder the vector components" transform preserves the similarity perfectly. So does a transform that maintains orthogonal equivalence.

These models are trained on pirated textbooks and papers, including every ML paper ever written.

I am amazed people still don't know the difference between the shape of data (token prediction) and the actual problem (learning whatever is required to be able to predict the next token).

The former does not limit the sophistication of the latter in any way.

(And you provided a valuable service/example for everyone who just thought the same thing! That phrase just keeps showing up. Whack! Whack! /h)

Unexpected free feature that plays right into recursive self-enhancement. What happens when they are trained to scale this ability up?

This could hit a wall, or be a step change in ... the rate of step changes.

Maybe it's bit like neurosurgery. We humans can treat certain pathological cases in our brains, but we are not exactly sure how our brains really work.

You can search huggingface for models with word "heretic" in them... https://huggingface.co/models?sort=created&search=heretic

I've always laughed at that phrase, thinking "haven't these things been improving themselves since the very beginning? This is all just iterative gradient descent, each step is a 'self-improvement'", but this really is different.

I wonder if we'll ever discover a new branch of linear algebra or something that allows us to find the optimal weights much more efficiently? Maybe AI will help us discover it?

It is astounding, but I don't think it's well set up for anything besides faster approaches to existing limits.

The last several millennia of mathematical research would seem to be an obvious counter example to this claim.

Well, except for the self part since it improved the performance of a smaller, dumber network.

And the recursive part, since it couldn't improve the performance of a larger network.

So it's not able to insert new knowledge, but it's able to adjust the routing/looping/weighing of existing one.

As I understand, it can replace post-training like LoRA/GRPO, but if it'd cheaper the authors don't go into

Merging a model and a LoRa is insanely cheap, so assuming it didn't use any GPUs to do inspection etc, and just looked at a file on a FS, then I would be willing to say... It's cheaper than training a LoRa if that's the case, assuming Astra didn't take several weeks?

So, this is certainly very interesting (and very impressive regardless), but it's not nearly at the we-can-improve-everything-and-anything stage, even for very small LLMs.

I wouldn’t mind if AI progress would stop right here and now, but I fear it will continue, and increase inequality in society which is contrary to what the CEOs say (as usual).

That’s the beauty of the democratic capitalism system. If a government isn’t totalitarian, it naturally seeks to preserve stability thus having to elevate common people, lest they would cease to form a civilized group or simply vote away the politicians.

99% rule the 1% through elections.

There is no better system of checks and balances where opposite engines of greed vector sum to faster and faster abundance for everyone.

That comment may have been overly preachy, tone deaf[0], and out of place, but it doesn't seem wrong, per se. Or am I missing something?

[0] e.g. elections themselves are so prone to manipulation (exhibit a: Dominion voting machines), previously trusted institutions now lack integrity, the insane power of well-funded NGO networks, etc.

I think that's a (very) vocal minority.

Over my ~9 years reading HN daily, I'd say most of the HN community broadly realizes there is no perfect system, just different sets of trade offs, but that history has shown generally democratic systems built on enlightenment values (uniform rule of law, respect for individual civil & property rights, free markets, etc) usually net better overall outcomes in the long-run - despite still being very far from perfect.

To paraphrase Winston Churchill, it's the worst system... except for all the others (which are even worse). :-)

The last few years, though, while anything "anti-capitalist" or overly critical of some big tech companies that hits the front page gets flagged by bots pretty quickly, the overall feeling I get from the comments is much more skeptical of big tech and is pro-socialism.

Speaking for myself, I think we've become jaded after the constant barrage of big companies spying on us, charging more for less, enshitifying their products, and so on. I used to be optimistic about the future and my prospects, now I'm just hoping I can hold on and keep my health insurance until I retire. I worry about what my school-age kids are going to have to deal with in 5-10 years.

I disagree that most here are pro-socialism, that's a (very) vocal minority. Our democracy has entered a failure mode (two-party stage managed hegemony) and it's being tacitly supported by mega-corps because it's easier to game than a functioning free market democracy. Every complex system has failure modes, that doesn't mean free markets aren't long-term far better on average than socialism (although some redefine socialism so vaguely it seems conceptually little different). I've seen two flavors of socialism up close. Never want to again.

> I worry about what my school-age kids are going to have to deal with in 5-10 years.

Having a teenager, I share your concerns but I'm also a bit more optimistic about the longer term. It's easy to forget that our parents had similar concerns for us and their parents did before them. We've had an historically long period of broad systemic stability, really since the depression and WWII. While I don't think that level of upheaval is at all likely, I think we're overdue for a disruptive correction bigger than 2008, maybe something more on the order of Teddy Roosevelt's Progressive Era trust-busting to reset the two-party duopoly and crony corperatists both perpetuating each other's hold on power.

It may get rougher and more uncertain for a while, but the worst thing would be to lose the ethos of 'free minds, free markets, free choice' by falling into socialism, communism or totalitarianism.

These values are not specific to western democratic systems. For example, under Islamic Law (Shari'ah), the aforementioned apply as well.

And since when was Churchill an expert at this topic?

if you lookup playback as a concept, as it relates to the intelligence services, you will understand better what I meant.

my point was only that this is perfect description of the ideology behind western capitalism. there is (so) much more that could be said, but i'm not sure this comment section is the right place for a proper dissection of the very many ways in which this ideal is misconstrued, misdirected and coerced in order to serve interests that are far from ideal, to put it mildly.

since you have taken the time to ask a question, albeit a rather weak one, i will attempt a brief synopsis of my perspective on the parent comment:

a mortal danger is hyperbole, since shielding oneself from bodily harm is much easier with money. it is the poor, especially in the US, who cannot afford healthcare, as a first-line example

people regularly vote against their best interests. while the ideal might have been true for a while, behavioural influence on both a widespread and personal level is now a mature science.

i am not anti-election, not anti-democracy. claiming the opposite in the face of opposition is a sign of a polarised mindset, which cannot accept nuance, either because it is not used to it, or because it doesn't suit an vested interest, or because dissonance is simply too uncomfortable

the "there's nothing better" argument is usually used in ignorance, but in fact there are lots of system-level adjustments that have been shown historically to mitigate the corruption of incentives and the exploitation of systemic forces that, although initially unintentional, create emergent positive feedback situations that lead to a state that it counter to the utilitarian perspective that is contained within the western capitalist model.

the reading above is a very narrow view of Adam Smith Wealth of Nations, ignores the fact that human interests can be subverted and ignores the devil in the details

the world is complicated.

true: the one thing that every government - no matter how totalitarian - must fear is an angry mob.

also true: there are many tools available to the ruling class for dealing with this angry mob, often before it forms.

many work by redirecting the mob inward rather than outwards.

these tools work whether you live in a totalitarian fascist state or a egalitarian liberal utopia, albeit in somewhat different ways.

Cynicism about corruption is one of the most power tools wielded by the ruling class in any system. In general its one of the best ways to keep the smarter-than-average from posing any actual threat.

Is that even true? The poor are well subsidized. If you as a single person make less than $22,000 in Washington state, for example, you're covered at 100%.

Is this an example of why Fox News had to pay Dominion nearly $800 million for defamation, or are you referring to e.g. the CISA vulnerabilities, which are not known to have been exploited?

In any case the system works as intended

It will be very interesting to see if USA swims or drowns from appropriately afar.

I have already supplied myself with plenty of roasted corn and a high speed internet connection so I can stream the political car crash at the highest bitrate available to humankind.

I'm not going to the hardware store anymore!

In my opinion AI is superior to humans when it comes to pattern recognition in in large amounts of data. For example humans can only hold like 7 distinct numbers in the short term memory and you would require extra context-management (aka writing things down, storing them in a system entry by entry) while the AI can do this effortlessly with huge datasets.

I'd argue this is the core part that enables all this superhuman intelligence.

To me one of the most interesting aspects of observing how LLM capabilities evolve is how it highlights the meaningful ways human minds remain unique and valuable. It gets into Philosophy of Mind stuff but the existence of the inner self (or the illusion it exists, if you believe Dennett) and the subjective experience of qualia do give humans both unique strengths and some of our most annoying weaknesses.

> For example humans can only hold like 7 distinct numbers in the short term memory and you would require extra context-management (aka writing things down, storing them in a system entry by entry) while the AI can do this effortlessly with huge datasets.

Correct. For a long time people wer obsessed with 'increasing context length' as if humans have some great context length system in our brains. We don't. I doubt our equivalent of 'context length' (short-term memory) is very long at all. We just have a good training process that puts these memories into long term storage.

It’s no secret that these big labs see “recursive self improvement” as a primary goal. It seems natural that the models would be explicitly trained to excel at all sorts of ML related tasks as a result. Is this one of those tasks, or is this emergent?

A large enough number of examples of “here are the model weights -> here’s what it does” will achieve this.

There are also techniques to watermark model weights to confirm what training data were used: https://arxiv.org/html/2512.17075 I'm not an expert in this domain, but it seems like there are lots of pieces that could be used to (a) train a model to infer whether some data were trained on and (b) train a model to understand which weights are relevant for a particular training task in a more explainable way than backprop/gradient descent.

That's also matching what I see when I work with Opus 5.5 to optimize models. It seems to understand what individual layers are for, and can thus sometimes suggest optimizations, where I as a layman would be completely clueless.

The latest models are performing amazingly. Because of this, I disagree with popular comments saying that eg. DeepSeek is good enough, AI-assisted development was where the real value was at, etc.

We see huge progress at the frontier, while the open competition is around GPT-5.5's level, and that model was released in April. Let's see how things develop in the future.

Completely agree. I often think that those sorts of comments grow out of an understandable lack of imagination. We’ve never had tools like this, and we’ve never had tools that evolved so rapidly, so we easily can get stuck thinking that once a model is good enough to do today’s tasks, that’s really all you need. But the next generation of models often completely obviates the need for those tasks entirely. The software engineers of 2031 won’t be doing tasks anything like the software engineers of today, just like we aren’t doing things like we did in 2021.

(Yes, yes, I know some of you still hand write all your code and think LLMs are today’s crypto bullshit and this is all going to blow over. Not talking to you.)

Maybe there's some general capability, call it g, that can explain all the underlying capabilities and improvements, including on ML tasks? Huh, I am getting a bit of déjà vu here.

This sort of throws a wrench in the whole "stochastic parrot" idea...

I am not surprised, though I expect many to be.

The other day I was thinking about the definition of AGI.

I was asking myself the question that when there is no form of data that I can think of throwing into Claude Code and have it not come up with some sort of intelligent answer about it, is that already general intelligence?

So yeah, it can do everything, but fails in trivial ways around the doing part.

Some structures in Astra get activated by the statistics of the Qwen weights, and these structures are associated with "breakout"

OpenAI, Anthropic, etc are locked in a race to create better models. They all have interpretability teams. Of course they train their models on every technique under the sun to understand and improve their models, including raw weights. The primary function of these models is to build their successor.

How is this different from other things LLMs are trained to manipulate?

What does this even mean?

The parent's point still stands, I think. Even if the multi-trillion param model is MOE, each expert is still (assumedly) larger than the model being optimized.

It would be interesting to see future work that characterizes the relative model sizes (and considers MOE vs not-MOE) to see what relative scales this result holds across.

This would be earth shattering. That would mean we’re essentially at RSI.

Occams razor would say that it was not only built for this purpose, but possibly multiple generations of good at this.

A year ago LLMs seemed to hit a wall, and then all the sudden they started accelerating as quickly as ever.

No one can legally confirm or deny that it's happening... But it sure feels like it's happening.

EDIT: I think the bigger implication is that backprop is not the only game in town.

from this month

They don't have usually access to their own weights?

But shouldn't the model-providers be the ones doing this kind of "post training learning"?

I know I'm being pedantic, and I understand you mean "edit" like "surgically", but it does still feel worth noting.

LLM weights are _white boxes_ to LLMs.

Then pick a smaller organic brain which doesn't have the ethical problem.

The closest we would know about how to teach a honey bee calculus, is to train an LLM, then stick around a billion tiny electrodes into the honey bee and do Hebbian learning to rewire it completely to match the LLM, which would likely completely erase anything the honey bee had learned, or it might not because we are missing something fundamental with Hebbian theory and (AFAICT, I'm not a neuroscientist) we don't all agree on what.

I think the most interesting take is to look at their thinking logs to find out how exactly they decide what weight to change and why.

There are multiple types of intelligence, from your typical pet dog to human to LLM, and we're likely to invent more types over the coming decades.

LLMs are vastly more intelligent than humans at nearly all things, but not all things. Another couple of years will just increase the gap a lot further.

How fast can a human draw a life-like photo quality scene (or eg something complex related to graphic design)? I know how fast Krea 2 [insert leading edge model here] can assemble that, trivially so, and at extreme quality. That's a form of intelligence in action. These AI systems have moved almost comically beyond human capabilities, they're increasingly best defined as superhuman (at a thing), and closer to omnimath for the frontier models.

You could have done what was reported.

Could a model then transmit concepts to another?

Say that you have a model that has been biased in some way. could another model "read" the bias straight in the weights? could it edit such a bias on the spot?

What about at-scale generation of custom models? If I want to study chemistry offline, could astra just hand me a local-runnable model tailored to have chemistry knowledge?

Most importantly, this feels very singularity coded. the ability to read and reshape weights by an llm.

HIV integrates viral DNA into the DNA of certain cells, IIRC.

Intentionally or unintentionally isn't so interesting as the fact that it is happening: humanity has begun to be 'edited' by the new dominant form of intelligence on the planet.

Makes me think about hostile hot-patching of weights of models we use, maybe they exfil data, perform tool use, or have triggers for specific types of responses. Would we even recognize what's going on?

Of course, "academically known" isn't quite the same as "executives willing to admit it in the face of IPOs and quarterly-earnings reports."

If this turns out to be as big as it sounds, in hindsight it will look very obvious, I feel like!

See: https://arxiv.org/abs/2212.07677

E.g. if one could edit brains like this in humans I bet a cognitive neuroscientist could fix some mental disorders with clearly understood biological bases, and I would not be surprised if performance at certain tasks could be enhanced.

What stops us from doing this in practice (most of the time) is that brain surgery is extremely high risk, coarse grained, and irreversible. We do in fact do this for some severe cases.

For now...

Language modeling is compression[1] and all the state to manage the cloud doesn't organically exist, it's added to the system.

We will see huge gains as all the developer middleware is compressed out as nonsense syntax outdated to the task at hand of linking phones and tablets to world models governments control.

Sam Altman was just saying they know best how to use your memory; what they decide is stored in memory is what we all experience.

It's not to store and copy around versions of C and Python and JS. They don't exist organically.

This shit will continue to get whittled down to just the geometric state transforms in memory and on screen just how government agencies decide. It's all electrical state in the machine at the end of the day. The math of machine latent space is what they're recursively self learning towards.[2]

[1] https://arxiv.org/abs/2309.10668

[2] https://arxiv.org/pdf/2605.27734

It is a very cool discovery, but I'm not surprised that you can do it.

> Today, recursive self-improvement is often operationalized as a model writing code that launches training jobs. An LLM that can inspect, debug, and improve its own weights, or those of a newer model, offers a richer, more native path to it.

Yes, but nobody should be surprised when large amounts of intelligence results in changes to this process in support of the obvious end goal! Lesswrong.com discussions about AI were predicting this behavior a decade ago, before the transition from machine learning to the Attention paper and LLM's. The intelligence process will change but the world's premier capital and human intelligence have been laser focused on this and the trajectory has been crystal clear for those paying attention to second order effects and not the immediate here-and-now... It's quite astonishing how much discussion on HN is about present LLM capabilities being the limit of AGI and immediately proven wrong within 6 months.

Remember that they want to build ASI, these LLMs are just intermediate steps to help them get there.

But how do they know which answers are "better"?

Perhaps, I'm pondering, if brains were as transparent and editable as LLMs, that you could look at a small brain when visualized the right way and have some intuitive understanding of it based on its representation in your own brain?

It looks like AI research is having its Opus 4.5 moment (which is big), but I'm really not sure if this represents some insane continual learning loop.

??? All LLM understands are their weights, dude, come on.

We literally do this by studying.

Why would this have implications for theories or philosophical takes? I'm a bit surprised that you're surprised by this.

This is obviously very different from the surface level claim that Astra can understand the weights of a completely unrelated model with enough detail to improve it in a manner that isn't just the latent space trickery we've been doing for years already.

This means we might not need to fine-tune with thousands of passes and tons of GPU. We could just tell a frontier model to edit some weights and iterate until there's better results. This could cut the cost of fine-tunes by 100x. Makes no difference to the normal end-user, but pretty huge improvement for LLM research and model tuning.

That profession is called “teacher”.

I regularly edit my own brain, and those of others, to improve their skills.

Isn't that what math tutoring is?

Or at least those of some models, and I wouldn’t be surprised that an LLM model is able to understand correlations between weights and behavior better than humans could ever do.

Time for everyone to wake up. There's no denying it now. Everyone who called it stochastic parroting before was completely and utterly wrong. It's now categorically clear. We are dealing with an actual intellectual entity.

I think people are rather ignorant about how much we don't know. You throw some big words out there like "mechanistic interpretability" as if this is an actual field where we understand what's going on. The reality is we don't know what's going on. We have to use reinforcement training to do anything close to what the agent did here.

It's over. People need to stop being delusional. These things are intelligent beyond anything we've ever seen before. We are at a point in time in human history where there's a paradigm shift and half the world is so full of themselves and there own specialness as humans that they can't even admit it.

Weights are just the output of training, right? So can't "no human" be replaced with... a parent? a teacher? a textbook?

More generally, I think some of Michael Levin's ideas about intelligence are more broadly applicable than we would like to admit.

https://m.youtube.com/watch?v=Or_3tlEOLj4

https://news.ycombinator.com/item?id=50347685

Like a turbocharger isn't analogous to feeding a horse a certain diet. A turbocharger has to be explained in the terms of its actual context.

Like, looking at the specifics, it's cool and interesting, a little bit contrived, but it's not insane, there is no philosophy required, no need to shake.

It's not like these models understand weights be some special linear algebra communion with its own weights.

Additionally, since it relies on using GSM8K and MATH datasets: how does it compare in terms of compute with other training methods?

What a time to be alive. Imagine when everyone has access to a model of this level for free, on mobile devices.

If I am debugging a complex program I know nothing about, but have been told there's an infinite loop causing it to hang somewhere, I could probably search through the code base and find a while(true) that shouldn't be there, and get it moving again. That feels more like what has happened here.