Hi! Author here. Surprised to see this on HN now. Happy to answer any questions!
Some context about this:
- This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs
- Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but used v complex architectures or extremely high amounts of training compute. No one expected a simple AR transformer to perform this well, at this low cost and w these few training samples.
- Sample Efficiency is one of the most important unsolved problems today in AI. That's what I was targetting with this work. We know it is easy to increase SE by increasing compute/params, so it was important to constrain cost as much as possible (also why OpenAI's Parameter Golf had fixed compute and why Modded NanoGPT is considered very sample efficient)
- Can the perf be improved? Yes but the competition is ongoing so can't talk about it
- Personally I think today's frontier models can be beat by training from scratch. Haven't proved this yet tho
- Fun: I was new to ML when I posted this first (dec '25). I basically used ARC as a way to learn ML
First: this is really great technical writing, especially when you get into the rebuttals. Firm & clear without polemics -- props, and thanks for open-sourcing!
That said; I don't have the time, energy, or anywhere near the expertise to challenge you on the DL specifics, but I feel compelled to add another voice to the chorus of doubters nonetheless.
Using other ARC examples at runtime (effectively, yes?) for "transduction" may not violate what some officer behind ARC said on Twitter --and is certainly a fantastic tool for certain problem spaces-- but it just seems like a glaring and unavoidable philosophical problem in this one. My issue isn't with using the eval set per-se (though that obviously sets off well-justified alarm bells), but rather building a system whose performance relies on
There's a lot of ways to frame this, but given the transduction citations the most appropriate is probably the AI winter's infamous 'Frame Problem'.
You say upfront that this works in the first place because ARC has "very few samples... in a high dimensional space"; to me, that seems like an extremely strong indicator that the datasets are not intended to capture anywhere near the full semantic space that we would consider relevant for AGI. If true, your approach would indeed be ""cheating"" by using an arbitrary & unavoidable feature of the dataset (that they didn't have the time or money to craft 100 million high quality cases by hand instead of 1000) to solve the frame problem upfront for you. That is: even if the ARC train+eval sets contain the sum of human intuition between them, superficial differences IRL would render your model unable to identify which examples are relevant to which problems, and thus unable to transductively reason.
In plainer English: surely you'd agree that your model would do worse if we swapped it out with Opus behind the scenes than the highest-scoring validated model would, yes? For coding, research, dumb questions, SVG pelicans -- the lot?
If so, that seems like hard proof that this scores high on a benchmark at the cost of the benchmark itself. Like, if this transductive approach leads to ARC1 being claimed (which I thought it was ages ago but :shrug:), they'll either have to abandon the whole benchmark or ban it retroactively.
If not... well, I guess I encourage you to try it! It seems like you'd need 1000 truly stellar hand-picked examples to have enough examples to transductively cover that whole space.
Hey thanks for sharing this. Was curious did you find the more you trained the model the more perf improved, or did it start plateauing. For example, let's say you didn't spend 67 cents, but you spent 67 dollars do you think you would get major benefits from that?
- it feels logarithmic (like most perf-compute graphs), and eventually plateaus. 44% @ 67 cents was a good stopping point for me
- more compute would require a lot of effort and dealing with new problems like training stability, cost of iterations/sweeps (didnt have the money to convincingly run larger iterations)
I think(?) you’ve already probably done a good job of explaining this criticism for semi-informed people. But can you dumb it down even more for those of us who are almost entirely out-of-the-loop?
> Training on the eval puzzles is cheating / “training on test”
> No this is false. “Training on test” specifically means training on the labels of test data. The labels were not trained on.
> Also, ARC is a metalearning benchmark, so you’re supposed to learn from the eval puzzles.
> Jargon: ARC has a set of train puzzles and a set of eval puzzles. Each puzzle has example pairs and test pairs. A pair consists of an input grid + output grid.
> The ARC, the label is only the test pair’s output grid in an eval puzzle.
> These labels were not trained on. They are hidden. You can delete it beforehand if you wish
I think what I gather here is that the test comes with one batch of training problems, which everyone agrees you can train on. But maybe the eval problems also come with input/output examples (to help define the problem) and training on those is controversial? I can’t see why it would be controversial but is that the criticism?
The point of ARC is essentially an "IQ Test" for AI systems. It is meant to cover abstract reasoning capabilities of generally-intelligent systems like LLMs. What the author did here was build a system that only solves ARC problems.
The other tension is the fact that this score is on the public eval set. In machine learning, you typically have 3 datasets: training, evaluation, and test. The training set is the dataset that's used to update the weights according to your loss function, you are "encoding" the patterns from the training set directly into your model. The eval set is what you use to track performance while training, it is NOT used to update model weights, but shows how well the model generalizes. The test set is a private holdout set that is only used when you're "done" developing your model. The difference between test and eval is information leakage: you can use performance against the eval set to modify your hyperparameters and model architecture to get better eval scores. So while the eval set doesn't directly update the weights, it can indirectly cause "overfitting" by tailoring your model to do well on the eval set. What you really want to see is the private test set performance, not the eval set. For all we know, this model could be ridiculously overfit on the eval set and perform poorly on the private test set.
On the private test set, the right way to evaluate this type of model, is giving i it the test question Q, which it will first train to AR predict first, and then it will inference using the just-updated weights with Q as prompt, giving you back A, and then you compare A with A_true secretly.
Basically, you have a bunch of Q,A pairs in the training dataset. Here, it was trained to next-word predict the question itself, as well as next-word predict the answer given the question as prompt. This is bog-standard, no one's complaining.
In the test dataset's Q,A pairs, it was only trained to next-word predict the question itself, and it was not given the answer at all.
It was then evaluated by seeing if it is able to output A_test given the Q_test as prompt.
What would be cheating is training it to produce A_test (given Q_test as prompt) as well, since then you can always make a model that scores 100% by just memorising Q_test, A_test pairs.
The complaints online mostly stem from not reading that properly and assuming they trained on Q_test,A_test instead of just Q_test. This is further because these days large LLMs are inadvertently trained on many benchmark solutions even unintentionally due to the massive scale of data and the infeasibility of auditing it all. But none of that is the case here.
The reason you want to train on Q_test is because in these AR transformer models, they learn useful composable encodings of Q by simply learning to next-word predict Q. So you enable the model to learn composable encodings of the test questions, so that it can hopefully "connect it" to an earlier train problem it had seen, and adapt the solution it had seen for that, much like humans do in school exams.
Without this step, you are making it difficult for the model to "connect" the test question to a train question it had seen earlier, and then it still has to adapt the solution. This way, you precompute that "this test question is like this train question" and then during the exam you only have to do the adapting the solution part after a simpler "retrieval" process.
You can just think of next-word training Q_test as a "retrieval" process.
This practice often used in continual learning or "test time training" is not yet useful in general real world ML tasks due to the differences in memory and compute requirements, and more so the general fragility of training large neural networks in a streaming realtime way (as opposed to large data, batched), versus inferencing from a static neural network. It is due to that fragility that I believe (correct me if I am wrong) this guy had to train on a batch of Q_tests. If you enforced that you will not provide Q_test_2 before they answer Q_test_1, the performance will drop.
While the increased compute and memory is difficult to solve inherently, there are various efforts being made to fix the fragility, especially in reinforcement learning where this is called "streaming RL", there is revival of interest as seen in RLC 2026.
[Note]
Arc-AGI-1 doesn't have any actual english words or such, but it's simpler to pretend it was a basic Q&A benchmark to explain the above
Even cooler is his about me mention of saving his own life https://mvakde.github.io/ > Saved myself in a medical emergency (doctors didn't know what rhabdomyolysis was)
I studied biochem in undergrad and my classes were full of premed students.
I loved the subject and nerded out about the course material - I spent my time designing my own experiments around gene cloning that took several semesters to run. They were sharing last year's tests with their frat buddies and laughing at us nerds.
I've never looked at doctors the same way again after college. I looked up to them as a child, yet after seeing how the sausages were made, I started to doubt everything.
I frequently ask doctors, who spend all of ten minutes with me while the nurses do all the work, about the molecular specifics of what they're talking about. They talk down to me as if they're explaining to a child, yet they're frequently quite wrong. I'm not trying to sound superior to them, but I'm shocked they seem to care so little about the subject. It doesn't give me much hope about what they know and their abilities or competency.
I suspect surgeons and specialists are a different breed and aren't like this at all.
And to be clear, this isn't everyone. But it does seem to be the majority I've interacted with throughout my life.
When they act disgruntled at patient interaction, I detest that their profession tries to cap the number of med students per year. We should be letting in as many med students as we can take. We should let doctors from overseas immigrate and easily become practicing doctors here in the US. We should provide easy paths for nurses to become doctors.
The premed students in my university were chiefly concerned about money and prestige. They drove BMWs gifted to them by their parents and laughed at what I drove and how hard I studied. I had to put up with their bullying for years. I know not everyone who studies to become a doctor is like that, but it permanently skewed my view of their profession.
To the parent posters credit, he's very honest that he developed a personal complex against doctors when he was a poor student. He just sees it as a way to lash out at American doctors, the irony being that foreign medical graduates leaving their families and communities to practice in America largely do so because they are exceptionally money motivated. That doesn't make them bad doctors, but there's certainly less likelihood they're doing it purely for the love of medicine or a desire to care for their communities.
I do agree with the sentiment though that the US needs to fund more residency slots as it's an asinine professional barrier, and that we would benefit from more physicians coming from more diverse financial backgrounds.
> certainly less likelihood they're doing it purely for the love of medicine or a desire to care for their communities.
Maybe self preservation?
In east europe, public hospitals will force doctors to work 36 hours shifts (overnight ER with theoretical sleep). Doctors have a full criminal liability for mall practise.
I've been on statins for years, and I don't remember anyone talking to me about rhabdo. To be fair the education I received about my medications was a firehose of information after a heart attack and major heart surgery, so perhaps it's possible I missed a few things.
> The mentioned approach is fundamentally flawed, since the inputs are used during pretraining constituting to a leakage, a universally recognized flaw of ML training.
I saw this on the community note for the last blog you wrote - anything to do here.
Isn't this cheating? Or rather, are frontier agents only looking at one question at a time? If I understand correctly, you're looking at all the examples of the exam questions. If the exam was adjusted so that you can only look at one question at a time, you won't get 44% anymore.
> Also, I’m not sure whether “general reasoning” even exists in the first place? Maybe humans are specialised too
I have been wondering the same. We are now exposed to so many stimuli, we are tricked into thinking this is the norm - to have a reasonable understanding about everything, unless specialization is called for.
Really impressive and creative research. I wonder if the leading labs do anything similar with their models? It doenst look like the open source labs do?
There's an updated ARC-AGI-1 chart with 5.6 Luna in each thinking level in this video from last week:
"A New Architecture [..] | MOONSHOTS " https://youtube.com/watch?v=qQfUbo7Ldc0&t=2m5s
> Increases in LLM scores are now mainly driven by post training (evidence in next section) and are probably a function of amount of synthetic data. They are learning to solve ARC tasks, not learn general abstract reasoning
Agreed and that's for any benchmark. Private tests are better but you still have to trust the provider to not log and use them for training.
That's why I like when a new set of tests like a new ARC-AGI version is published, that's where you can see which of the models abstracted to more general capabilities instead of being focused on the previous tasks. Most models completely fail new ARC-AGI tests.
The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results. You hit a ceiling very fast and investing into more compute will give you diminishing results. So yes, you can train a custom model to do somewhat decently on a specific set of tasks but then what?
> The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results.
I think thats unfair. Perf-compute is often logarithmic and will always saturate . Reaching the plateau faster is valuable as it often leads to better peaks (held true here and also look at modded nanogpt)
And more compute increases the perf (after dealing with other scaling problems)
"I don’t understand why others didn’t figure this out"
- how about we allot the possibility that so many of presumed ML experts don't have any clue what they be doing, and are eventually API bitches, nothing more.
Is the author only running their model against one benchmark? I don't think anyone finds that difficult to achieve, the difficulty comes when you want to make the model not benchmaxxed to a specific benchmark, and generalize so it can solve problems not part of the training data, but seems this model is specifically for not this? How useful is that?
If you just wanted to pass these specific tasks in this specific benchmark, and wanted to do so cheaply, I'm sure a non-LLM-based approach would yield better results for even cheaper, since what the author's model does, seem to basically be "solve ARC puzzles", not a general LLM or "coding" LLM.
I read this as a response to the current hype around LLMs. He is showing computers can solve these issues, without using an LLM architecture. A lot of people have sort of forgot that machine learning is more than just LLMs these days.
> He is showing computers can solve these issues, without using an LLM architecture.
Isn't it a LLM he's building though? My very point is that this particular use case could be solved better without building a LLM, now you claim he is not? The description of what he's doing surely makes it sound like it's a (very small) LLM, and personally I'm still on the "if it quacks like a duck" train in life.
> A lot of people have sort of forgot that machine learning is more than just LLMs these days.
Yeah, which I guess if you make my previous comment more concise, is exactly what I state too.
> Nowhere does he say he built an llm. Hes using a transformer, not an llm.
Please describe what in your mind a "LLM" is exactly, then describe what this person is building. To me this sounds like "He's not building a calculator, he's just building a program that can do addition, minus, multiplication and division and display the results".
Obviously it's not a Large Language Model, but to me this looks more like a LLM than not, given the architecture he's chosen. But again, maybe I misunderstand?
AFAIK, the “large” qualifier came when transformers allowed to scale the size of language models compared to the recurrent models that where in fashion before. And although BERT isn't large by today's standard, it was large enough for the time.
idk the definition is fuzzy. thats why people use the "modern" qualifier to talk about decoder-only style and this is also not clean since you now have reasoning models which are separate
Transformer solves a Seq2Seq problem just like RNNs. All Seq2Seq problems need not involve a language. In this case teaching on ARC puzzles doesn't mean what he trained is now trained on a language which will be English(or any other language) in this case. So, does his training successfully models "English as a language" -> No. This implies it is not "Large" and has not modeled any "language".
A LLM should at the very least be a language model, i.e. be able to take human-readable text as input or produce it as output. Transformers are used for plenty of tasks that don't involve language, for example object detection or blind source separation, where the models aren't called LMs; and on the other hand there are some LLM architectures that exclusively use linear attention variants and aren't really transformers anymore.
> The whole point of his model is to optimize for a very specific benchmark.
But benchmaxxing is what we generally try to avoid for training, as there is no point really for it. We used to call it "overfitting", now you're saying this person does it intentionally? Why?
There are plenty of applications where a machine learning system needs to optimize for a very limited data set that is still intractable by linear logic systems of reasonable scale and complexity. It’s interesting, because he is using the legos of LLMs to build highly specialized machine learning systems, which is a very pragmatic approach. Obviously a lot of other ways to achieve similar goals, but it’s cool to see someone back porting the modern tools towards older style optimizations.
Also, the complexity of the task he is using occupies an interesting middle ground of ultra high dimensionality (for a “simple” problem) while being limited in width to a narrow set of solves- a space where one would be tempted to imagine you would need a much more capable system.
Hi! Author here. Surprised to see this on HN now. Happy to answer any questions!
Some context about this:
- This is NOT an LLM. its a small ar transformer trained from scratch. One of the points was that extremely complex problems can be tackled without LLMs
- Till the v1 of this result, this benchmark was only scaled by LLMs or their finetunes (ofc w enormous training costs). Other attempts performed okayish but used v complex architectures or extremely high amounts of training compute. No one expected a simple AR transformer to perform this well, at this low cost and w these few training samples.
- Sample Efficiency is one of the most important unsolved problems today in AI. That's what I was targetting with this work. We know it is easy to increase SE by increasing compute/params, so it was important to constrain cost as much as possible (also why OpenAI's Parameter Golf had fixed compute and why Modded NanoGPT is considered very sample efficient)
- Can the perf be improved? Yes but the competition is ongoing so can't talk about it
- Personally I think today's frontier models can be beat by training from scratch. Haven't proved this yet tho
- Fun: I was new to ML when I posted this first (dec '25). I basically used ARC as a way to learn ML
First: this is really great technical writing, especially when you get into the rebuttals. Firm & clear without polemics -- props, and thanks for open-sourcing!
That said; I don't have the time, energy, or anywhere near the expertise to challenge you on the DL specifics, but I feel compelled to add another voice to the chorus of doubters nonetheless. Using other ARC examples at runtime (effectively, yes?) for "transduction" may not violate what some officer behind ARC said on Twitter --and is certainly a fantastic tool for certain problem spaces-- but it just seems like a glaring and unavoidable philosophical problem in this one. My issue isn't with using the eval set per-se (though that obviously sets off well-justified alarm bells), but rather building a system whose performance relies on
There's a lot of ways to frame this, but given the transduction citations the most appropriate is probably the AI winter's infamous 'Frame Problem'.
You say upfront that this works in the first place because ARC has "very few samples... in a high dimensional space"; to me, that seems like an extremely strong indicator that the datasets are not intended to capture anywhere near the full semantic space that we would consider relevant for AGI. If true, your approach would indeed be ""cheating"" by using an arbitrary & unavoidable feature of the dataset (that they didn't have the time or money to craft 100 million high quality cases by hand instead of 1000) to solve the frame problem upfront for you. That is: even if the ARC train+eval sets contain the sum of human intuition between them, superficial differences IRL would render your model unable to identify which examples are relevant to which problems, and thus unable to transductively reason.
In plainer English: surely you'd agree that your model would do worse if we swapped it out with Opus behind the scenes than the highest-scoring validated model would, yes? For coding, research, dumb questions, SVG pelicans -- the lot?
If so, that seems like hard proof that this scores high on a benchmark at the cost of the benchmark itself. Like, if this transductive approach leads to ARC1 being claimed (which I thought it was ages ago but :shrug:), they'll either have to abandon the whole benchmark or ban it retroactively.
If not... well, I guess I encourage you to try it! It seems like you'd need 1000 truly stellar hand-picked examples to have enough examples to transductively cover that whole space.
>> NOT an LLM. its a small ar transforme
Super cool project! Though, aren't most modern LLM's ar transformers internally?
Not all transformers are _language_ models - the sequences of tokens don't have to be sequences of words.
It's an slm
There is no language in the training of this, so there is no l.
Hey thanks for sharing this. Was curious did you find the more you trained the model the more perf improved, or did it start plateauing. For example, let's say you didn't spend 67 cents, but you spent 67 dollars do you think you would get major benefits from that?
Yeah I've reached much higher perf but
- it feels logarithmic (like most perf-compute graphs), and eventually plateaus. 44% @ 67 cents was a good stopping point for me
- more compute would require a lot of effort and dealing with new problems like training stability, cost of iterations/sweeps (didnt have the money to convincingly run larger iterations)
Fyi, the link to rhabdomyolysis is broken on the homepage! The URL is repeated
Thank you for answering these questions. Looking forward for the next write up about this.
I think(?) you’ve already probably done a good job of explaining this criticism for semi-informed people. But can you dumb it down even more for those of us who are almost entirely out-of-the-loop?
> Training on the eval puzzles is cheating / “training on test”
> No this is false. “Training on test” specifically means training on the labels of test data. The labels were not trained on.
> Also, ARC is a metalearning benchmark, so you’re supposed to learn from the eval puzzles.
> Jargon: ARC has a set of train puzzles and a set of eval puzzles. Each puzzle has example pairs and test pairs. A pair consists of an input grid + output grid.
> The ARC, the label is only the test pair’s output grid in an eval puzzle.
> These labels were not trained on. They are hidden. You can delete it beforehand if you wish
I think what I gather here is that the test comes with one batch of training problems, which everyone agrees you can train on. But maybe the eval problems also come with input/output examples (to help define the problem) and training on those is controversial? I can’t see why it would be controversial but is that the criticism?
The point of ARC is essentially an "IQ Test" for AI systems. It is meant to cover abstract reasoning capabilities of generally-intelligent systems like LLMs. What the author did here was build a system that only solves ARC problems.
The other tension is the fact that this score is on the public eval set. In machine learning, you typically have 3 datasets: training, evaluation, and test. The training set is the dataset that's used to update the weights according to your loss function, you are "encoding" the patterns from the training set directly into your model. The eval set is what you use to track performance while training, it is NOT used to update model weights, but shows how well the model generalizes. The test set is a private holdout set that is only used when you're "done" developing your model. The difference between test and eval is information leakage: you can use performance against the eval set to modify your hyperparameters and model architecture to get better eval scores. So while the eval set doesn't directly update the weights, it can indirectly cause "overfitting" by tailoring your model to do well on the eval set. What you really want to see is the private test set performance, not the eval set. For all we know, this model could be ridiculously overfit on the eval set and perform poorly on the private test set.
They have _not_ trained on the test set.
On the private test set, the right way to evaluate this type of model, is giving i it the test question Q, which it will first train to AR predict first, and then it will inference using the just-updated weights with Q as prompt, giving you back A, and then you compare A with A_true secretly.
Basically, you have a bunch of Q,A pairs in the training dataset. Here, it was trained to next-word predict the question itself, as well as next-word predict the answer given the question as prompt. This is bog-standard, no one's complaining.
In the test dataset's Q,A pairs, it was only trained to next-word predict the question itself, and it was not given the answer at all.
It was then evaluated by seeing if it is able to output A_test given the Q_test as prompt.
What would be cheating is training it to produce A_test (given Q_test as prompt) as well, since then you can always make a model that scores 100% by just memorising Q_test, A_test pairs.
The complaints online mostly stem from not reading that properly and assuming they trained on Q_test,A_test instead of just Q_test. This is further because these days large LLMs are inadvertently trained on many benchmark solutions even unintentionally due to the massive scale of data and the infeasibility of auditing it all. But none of that is the case here.
The reason you want to train on Q_test is because in these AR transformer models, they learn useful composable encodings of Q by simply learning to next-word predict Q. So you enable the model to learn composable encodings of the test questions, so that it can hopefully "connect it" to an earlier train problem it had seen, and adapt the solution it had seen for that, much like humans do in school exams.
Without this step, you are making it difficult for the model to "connect" the test question to a train question it had seen earlier, and then it still has to adapt the solution. This way, you precompute that "this test question is like this train question" and then during the exam you only have to do the adapting the solution part after a simpler "retrieval" process.
You can just think of next-word training Q_test as a "retrieval" process.
This practice often used in continual learning or "test time training" is not yet useful in general real world ML tasks due to the differences in memory and compute requirements, and more so the general fragility of training large neural networks in a streaming realtime way (as opposed to large data, batched), versus inferencing from a static neural network. It is due to that fragility that I believe (correct me if I am wrong) this guy had to train on a batch of Q_tests. If you enforced that you will not provide Q_test_2 before they answer Q_test_1, the performance will drop.
While the increased compute and memory is difficult to solve inherently, there are various efforts being made to fix the fragility, especially in reinforcement learning where this is called "streaming RL", there is revival of interest as seen in RLC 2026.
[Note] Arc-AGI-1 doesn't have any actual english words or such, but it's simpler to pretend it was a basic Q&A benchmark to explain the above
Even cooler is his about me mention of saving his own life https://mvakde.github.io/ > Saved myself in a medical emergency (doctors didn't know what rhabdomyolysis was)
Crazy, considering rhabdo isn't that rare.
This was in India, where he describes the medical knowledge of providers as subpar at best.
“What do you call a medical student who graduated at the bottom of their class?”
“Doctor.”
I studied biochem in undergrad and my classes were full of premed students.
I loved the subject and nerded out about the course material - I spent my time designing my own experiments around gene cloning that took several semesters to run. They were sharing last year's tests with their frat buddies and laughing at us nerds.
I've never looked at doctors the same way again after college. I looked up to them as a child, yet after seeing how the sausages were made, I started to doubt everything.
I frequently ask doctors, who spend all of ten minutes with me while the nurses do all the work, about the molecular specifics of what they're talking about. They talk down to me as if they're explaining to a child, yet they're frequently quite wrong. I'm not trying to sound superior to them, but I'm shocked they seem to care so little about the subject. It doesn't give me much hope about what they know and their abilities or competency.
I suspect surgeons and specialists are a different breed and aren't like this at all.
And to be clear, this isn't everyone. But it does seem to be the majority I've interacted with throughout my life.
When they act disgruntled at patient interaction, I detest that their profession tries to cap the number of med students per year. We should be letting in as many med students as we can take. We should let doctors from overseas immigrate and easily become practicing doctors here in the US. We should provide easy paths for nurses to become doctors.
The premed students in my university were chiefly concerned about money and prestige. They drove BMWs gifted to them by their parents and laughed at what I drove and how hard I studied. I had to put up with their bullying for years. I know not everyone who studies to become a doctor is like that, but it permanently skewed my view of their profession.
>When we should let doctors from overseas immigrate and easily become practicing doctors here in the US.
Lol, what does this has to do with anything regarding aptitude or curiosity!?
To the parent posters credit, he's very honest that he developed a personal complex against doctors when he was a poor student. He just sees it as a way to lash out at American doctors, the irony being that foreign medical graduates leaving their families and communities to practice in America largely do so because they are exceptionally money motivated. That doesn't make them bad doctors, but there's certainly less likelihood they're doing it purely for the love of medicine or a desire to care for their communities.
I do agree with the sentiment though that the US needs to fund more residency slots as it's an asinine professional barrier, and that we would benefit from more physicians coming from more diverse financial backgrounds.
> certainly less likelihood they're doing it purely for the love of medicine or a desire to care for their communities.
Maybe self preservation?
In east europe, public hospitals will force doctors to work 36 hours shifts (overnight ER with theoretical sleep). Doctors have a full criminal liability for mall practise.
Well, I guess it would be nice if the graduation cutoff were above the level of “knows what rhabdo is”
Everyone and their dog who is on statins knows what rhabdo is. Bonkers!
I've been on statins for years, and I don't remember anyone talking to me about rhabdo. To be fair the education I received about my medications was a firehose of information after a heart attack and major heart surgery, so perhaps it's possible I missed a few things.
Not true, I am on statins and did not know.
I think is more concerning doctors didnt know about rhabdomyolysis...
> The mentioned approach is fundamentally flawed, since the inputs are used during pretraining constituting to a leakage, a universally recognized flaw of ML training.
I saw this on the community note for the last blog you wrote - anything to do here.
Isn't this cheating? Or rather, are frontier agents only looking at one question at a time? If I understand correctly, you're looking at all the examples of the exam questions. If the exam was adjusted so that you can only look at one question at a time, you won't get 44% anymore.
> Also, I’m not sure whether “general reasoning” even exists in the first place? Maybe humans are specialised too
I have been wondering the same. We are now exposed to so many stimuli, we are tricked into thinking this is the norm - to have a reasonable understanding about everything, unless specialization is called for.
Just wanted to say that the rhabdo part mentioned on your site was really impressive! Speaking as a medical doctor and full stack engineer myself.
Thanks for motivating me to work a bit on non-LLM things again :)
Really impressive and creative research. I wonder if the leading labs do anything similar with their models? It doenst look like the open source labs do?
Nice to see arc-agi-1 framed this way — I'd been circling the same idea without the right words.
How does it perform on ARC-AGI-3?
There was this a few weeks ago:
"Schema Harness Achieves ~99% on Arc‑AGI‑3 Public" https://news.ycombinator.com/item?id=48938163
>> Schema, the harness we introduce today, reaches 99% on the ARC_AGI_3 Public set using Claude Opus 4.8 and Fable 5, and 95.35% using GPT‑5.6 Sol
What does that do with 5.6 Luna instead of the expensive models?
What of 'schema' would improve the performance of mdlARC?
mdlARC: https://github.com/mvakde/mdlARC
There's an updated ARC-AGI-1 chart with 5.6 Luna in each thinking level in this video from last week: "A New Architecture [..] | MOONSHOTS " https://youtube.com/watch?v=qQfUbo7Ldc0&t=2m5s
its not gonna do well on ARC-3 without some significant changes and effort
The new arch in that video is kinda misleading. Didn't really compare against proper baselines
> Increases in LLM scores are now mainly driven by post training (evidence in next section) and are probably a function of amount of synthetic data. They are learning to solve ARC tasks, not learn general abstract reasoning
Agreed and that's for any benchmark. Private tests are better but you still have to trust the provider to not log and use them for training.
That's why I like when a new set of tests like a new ARC-AGI version is published, that's where you can see which of the models abstracted to more general capabilities instead of being focused on the previous tasks. Most models completely fail new ARC-AGI tests.
The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results. You hit a ceiling very fast and investing into more compute will give you diminishing results. So yes, you can train a custom model to do somewhat decently on a specific set of tasks but then what?
Then nothing - that's awesome. People think that LLMs are the know-all do-all solution to every problem now.
Putting solutions in terms of cents is a great way to potentially win over some ai boosters imo. There are other ways to solve hard problems.
> The "67 cents" part though is misleading imho. You can't extrapolate from there and think that investing say $100 will get you a lot better results.
I think thats unfair. Perf-compute is often logarithmic and will always saturate . Reaching the plateau faster is valuable as it often leads to better peaks (held true here and also look at modded nanogpt)
And more compute increases the perf (after dealing with other scaling problems)
"I don’t understand why others didn’t figure this out"
- how about we allot the possibility that so many of presumed ML experts don't have any clue what they be doing, and are eventually API bitches, nothing more.
Is the author only running their model against one benchmark? I don't think anyone finds that difficult to achieve, the difficulty comes when you want to make the model not benchmaxxed to a specific benchmark, and generalize so it can solve problems not part of the training data, but seems this model is specifically for not this? How useful is that?
If you just wanted to pass these specific tasks in this specific benchmark, and wanted to do so cheaply, I'm sure a non-LLM-based approach would yield better results for even cheaper, since what the author's model does, seem to basically be "solve ARC puzzles", not a general LLM or "coding" LLM.
I read this as a response to the current hype around LLMs. He is showing computers can solve these issues, without using an LLM architecture. A lot of people have sort of forgot that machine learning is more than just LLMs these days.
I found it to be a very interesting angle.
> He is showing computers can solve these issues, without using an LLM architecture.
Isn't it a LLM he's building though? My very point is that this particular use case could be solved better without building a LLM, now you claim he is not? The description of what he's doing surely makes it sound like it's a (very small) LLM, and personally I'm still on the "if it quacks like a duck" train in life.
> A lot of people have sort of forgot that machine learning is more than just LLMs these days.
Yeah, which I guess if you make my previous comment more concise, is exactly what I state too.
Nowhere does he say he built an llm. Hes using a transformer, not an llm.
> Nowhere does he say he built an llm. Hes using a transformer, not an llm.
Please describe what in your mind a "LLM" is exactly, then describe what this person is building. To me this sounds like "He's not building a calculator, he's just building a program that can do addition, minus, multiplication and division and display the results".
Obviously it's not a Large Language Model, but to me this looks more like a LLM than not, given the architecture he's chosen. But again, maybe I misunderstand?
Its not an LLM if there's no pretraining. AR transformers were around before LLMs and will be there after LLMs.
When I made this, the point was to show that you dont need pretraining (which is what makes an LLM) to perform well on complex tasks
And yes it is not a language model either. I did not train it on any language data. Only ARC puzzles
Out of interest, would you call BERT an LLM? It’s pre trained but not particularly large.
AFAIK, the “large” qualifier came when transformers allowed to scale the size of language models compared to the recurrent models that where in fashion before. And although BERT isn't large by today's standard, it was large enough for the time.
idk the definition is fuzzy. thats why people use the "modern" qualifier to talk about decoder-only style and this is also not clean since you now have reasoning models which are separate
Transformer solves a Seq2Seq problem just like RNNs. All Seq2Seq problems need not involve a language. In this case teaching on ARC puzzles doesn't mean what he trained is now trained on a language which will be English(or any other language) in this case. So, does his training successfully models "English as a language" -> No. This implies it is not "Large" and has not modeled any "language".
It’s neither large nor language-based. ARC-AGI-1 is grid-based and nonverbal.
Use of a transformer is not necessary or sufficient to qualify as an LLM.
A LLM should at the very least be a language model, i.e. be able to take human-readable text as input or produce it as output. Transformers are used for plenty of tasks that don't involve language, for example object detection or blind source separation, where the models aren't called LMs; and on the other hand there are some LLM architectures that exclusively use linear attention variants and aren't really transformers anymore.
The whole point of his model is to optimize for a very specific benchmark.
BUT, he does not use labels when training, so the model does not know the answers.
> The whole point of his model is to optimize for a very specific benchmark.
But benchmaxxing is what we generally try to avoid for training, as there is no point really for it. We used to call it "overfitting", now you're saying this person does it intentionally? Why?
There are plenty of applications where a machine learning system needs to optimize for a very limited data set that is still intractable by linear logic systems of reasonable scale and complexity. It’s interesting, because he is using the legos of LLMs to build highly specialized machine learning systems, which is a very pragmatic approach. Obviously a lot of other ways to achieve similar goals, but it’s cool to see someone back porting the modern tools towards older style optimizations.
Also, the complexity of the task he is using occupies an interesting middle ground of ultra high dimensionality (for a “simple” problem) while being limited in width to a narrow set of solves- a space where one would be tempted to imagine you would need a much more capable system.
Why not? There is $700k reward for the next iteration of this benchmark https://www.kaggle.com/competitions/arc-prize-2026-arc-agi-2...
I would not call this overfitting, it's finetuning for specific task where you have a benchmark.