Posted by timhigins 16 hours ago
Sadly that's not how LLMs work, since all they do is "token prediction". At least the models we have to today ...
Some amount of knowledge is required for reasoning. Maybe such model can dynamically knowledge domains to have taxonomy. For example, model can't effective reason about development task, if it has no knowledge about development best practices. But population of New York or recipies can definitely be loaded run time with tools.
This is the root of problem. If you think about STEM universities, they don't really teach you things you need in the real world. They teach you what you need to know in order to go out there and accumulate the necessary information which can then be used to solve problems. Giving a person access to the internet or a super powerful calculator (like Mathematica) won't mean that they can do anything useful. They need tons of experience to use these tools in an effective way. That experience is basically all that implicit adjacent knowledge that we pick up along the way getting our degrees. And LLMs pick that up during pre-training. Drop this part and the outcome will be worthless.
In my school, math teacher was giving me prose, which I was converting to math notation. I could argue, that this prose→reasoning conversion is not required at training, and can be obtained at inference time with search tools.
Our computers can already do everything, have access to all the tools and information, yet they still need a human/intelligence to use it and apply to specific problems.
Even defining the problem requires knowledge.
As for the tools, if the model has access to 1000 tools, how would it know which one to use if it doesn't have any knowledge itself?
What if I ask for "table tennis spin" it had a "magnus effect calculator", how would it know to make the connection between the two?
This is only one example, plus if the topic is more complex, maybe it had to search/learn everything (what is table tennis, what is spin, what is a human, what is a ball), etc. So it would be like spawning a baby human, have it spend an (instant) life learning about the world before providing an answer. Maybe this could work in 10 yesrs, if models get stronger with huge context lengths and almost instant data retrieval. Is it the best way to go about things though? Most animals have most of their core abilities embedded in their DNA and "instincts". A cat doesn't have to learn what a bird is in order to hunt it, it's already "embedded" in its neural pathways, or even deeper, at a full-body level. Those type of systems are a lot more efficient than the learned ones. Maybe the best future AI, will have everything already embedded, instead of just being a strong reasoning machine. All AI responses should be instant and like "reflexes" instead of reasoned steps.
I think grounding your abstract problem to an example makes it more trivial, than it sounds in general.
> How would it know about Wikipedia and when to use it?
2 general concepts "You have to get good understanding of subject area before you do actions" + "Wikipedia is a good source of knowledge of subject areas" will get a model there.
> spawning a baby human, have it spend an (instant) life learning
Humans spend 99% of their life on boring repeating tasks, not learning anything, just navigating on heuristics.
(what is turkish)->(parse lots of potentially relevant/irrelevant context because I have no way of knowing which if any of this informs the doner kebab before I've looked at it)
>dish made of meat
(what is meat) -> (parse lots of potentially ir/relevant context because I don't know if the specific origin/chemistry/mechanics or whether maillard reactions are important before I learn about them)
>cooked on a vertical rotisserie.
(what is a rotisserie) -> etc etc etc
Seems significantly less efficient than just having the various (how to cook > meat, tools > rotisserie, how to cook > seasoning > tomato; lettuce; cabbage; onion with sumac; fresh or pickled cucumber or chili; various sauces, etc) just already built in to the weights.
Yes, but still "how to cook" is not atomic. It involves knowing how to move stuff, how to measure, what "cooked" looks like in different environment (i.e. different lighting) or variations in ingredients, how to recover from specific failures (i.e. a good cook can fix accidentally adding too much salt, by counter-balancing with an ingredient that absorbs the extra salt). And this is only one skill.
It's a bit how deep image neural nets work, where simply detecting shape primitives is not enough, the net is also the connection and relation between those primitives.
Even saying, the AI should just have the "cooking" or "coding" skill, trivializes the problem.
> Humans spend 99% of their life on boring repeating tasks
But we are also non-stop unconciously learning about the world non-stop, from the analgous stream of inputs and seeing the immediate result/feedback. Even looking at static picture is like over-training a specific dataset.
Because if the recipe just says "boil for 10 minutes" but the thing being cooked really needs a temperature of 212F for 10 minutes, the thing isn't going to be cooked if you're not actually at 212 for 10.
E.g. you put a graph in its content window, and you ask it to find a Hamiltonian cycle, can it do it?
Probably this could be a next step in the future for more powerful AIs, a layer that abstracts the facts in its content window away, and a layer that solves this types of abstractions.
If "all the knowledge" is what our models now do, what exactly would be the most extreme "none of the knowledge +search" ?
> language specifications.
It would load in all the knowledge to figure it what "language" means, then it would continue trying to decode what "specifications" means.
That might sound absurd, but to figure out the population of New York It's either: Just going to google it, or derive from primary sources.
But how is it ever going to interpret the primary sources? It needs to understand the question, how complex a question is, and how complete an answer is and how things relate. Thats just _too_ much language.
There might be a way to compact this down into a LLM-native language such that the request of `the population of New York` or `use best practices` is encoded without our messy human language for a reasoning model to work with, but the encoding itself has to be done by the "all the knowledge" llm. Now it seems we just rebuild something related to MoE with extra step afaict.
Turns out that without the world knowledge to have a base of facts, it is not.
So I don't think it's true that relevant knowledge was deprioritized. At least it wasn't supposed to be.
First, if you know nothing you don't even know what you're missing or what to search for.
Then, without unlimited context, you have to do research for every task all over again every time.
RAG on the initial prompt would be the first thing to try.
> Then, without unlimited context, you have to do research for every task all over again every time.
Thing is, we're really really good at building very fast search engines. Doing research all over again every time shouldn't be a problem.
Even the most basic questions such as put a ball in a cup and place it on a table upside down then pick up the cup and put it in a box.
Requires knowledge of things not mentioned in the question (notably gravity).
Strict definition of all terms quickly gets you into a quagmire of complexity. Some base level of knowledge about things is required for you to give it instructions. If it only knows how to reason, it lacks any idea of what to aim to achieve.
There is quite a pronounced disconnect between the vast stores of written data that models are trained on and robust consideration of a topic. I do wonder if the path can be directed by the order of training.
For example if you train a model to basic literacy using tinystories, then math and philosopy texts, then psychology, and sociology texts, and then finally the mass data of everything from conversations and rants, to code and fiction.
Does that end up with a significantly different model to one that is trained on books on acting, creative writing, and fantasy novels, before introducing the same final mass data set.
How much does it's current ability allow it to contextualise new training data?
That reminds me - this used to be my go-to question for smaller models and on which they would always fail miserably on:
A small strawberry is placed in a large cup. The cup is placed upside down on the kitchen table. Someone then lifts the cup as-is and puts it in the microwave. Where is the strawberry when the cup is in the microwave?
Here's what the 1.9GB VibeThinker-3B-GGUF:Q4_K_M answered:
Answer: The strawberry is still on the kitchen table – it fell out when the cup was turned upside‑down, and the subsequent lift‑and‑microwave move doesn’t change that.
So it seems there is definite progress here. Both specialized and yet improved common sense on things outside its domain of specialization.
What happens if you ask
A small strawberry is placed in a large cup. The cup is placed upside down on a saucer on the kitchen table. Someone then lifts the cup and saucer as-is and puts them in the microwave. Where is the strawberry when the cup is in the microwave?
I do not think this is a great example. First, it is not a question. Second, it seems very related to robotics. A model itself cannot put a ball anywhere, it can just call tools and answer in text, image, etc.
An LLM seeing "put a x in a y and place it on a z upside down then pick up the y and put it in a z2." and then a question about what happens could check a rag for properties of those x,y,z,z2 and still answer. Alternatively, this could be useful for coding, for example. And that is a very extreme example. Some basic language plus tool use could go quite far. I think it is a very interesting direction vs here is a gpu the price of a car.
That you don't need to have a ball, cup, table, or even the ability to perform physical actions in order to consider where the ball ends up is in-itself required knowledge.
So if you don't train it on a large dataset of a lot of words with a lot of sensible connections, it won't be able to reason, as it won't be able to make proper connections between words and sentences.
You can try training a really small model and seeing the gibberish outputs when you train it on only a small dataset.
Minmaxing the dataset to extract maximum generation with minimal data does sound like fun, but if you want to build SoTA models as a company, the economic tradeoff of doing that vs slapping a few more GPU's together is terrible.
Imagine, for example, a model that's primarily train on typescript and general programming. It would be faster to train and it could be a lot smaller than a generalist model. It might be the best model to pick when you are doing typescript programming. And if you could squeeze that into 3B parameters a lot of consumer hardware could run it locally.
You could even expand it to just "webdev tech" or the like.
see the warning at the top of https://huggingface.co/WeiboAI/VibeThinker-3B
That plus this model should give you a very powerful and focussed assistant.
i remember karpathy mentioning in dwarkesh podcast. But is reasoning really possible without all the knowledge.
Except for the most basic of tasks, such as "turn on my lights" or "cross-reference these two lists", I wouldn't trust a small model to be as conscientious and reliable as one with deep knowledge.
Even recent massive models do not work anything like a smart human does at the moment so why are we assuming this can?
Now, if you ask this model to have a conversation with you, it's gonna fail and be incoherent. But boy, does it sure reason through math problems well.
Edit: seems fast! I'll try it out some more, thanks again.
This Q5_K_M quant should be near lossless and fit with full 256K context in about 100GB of RAM: https://huggingface.co/AesSedai/Qwen3.5-122B-A10B-GGUF
Edit: specifically Qwen 3.6 27B beats that on coding and agentic workflows.
The Q8_K_XL MTP model from Unsloth: https://huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF
I serve the model with ollama and am thinking about replacing ollama but haven't looked into it.
I have openwebui for chat if I want that too, but don't really use it.
Could you teach a 5 year old to drive a car? A 10 year old? A 12 year old? To drive a car requires being able to read, to have judgement about ice or rainy conditions, to anticipate a child running after a ball. By the time a human in in their mid teens they have acquired the base knowledge...
Small models need to have enough base knowledge to be able to be good enough -- even in a seemingly narrow regime. Where is that? Obviously they don't need all the obscure knowledge of a frontier model but there is some base level which is probably more than it would first seem.
Emphatically, it does not. Passing your drivers test may require being able to read, but plenty of illiterate people around the world drive just fine.
There is a reason we made all the common road signs recognisable purely by shape/colour, after all.
Though it is true you don't need to be able to read to operate a vehicle, you /do/ need to be able to read to operate a vehicle safely.
And for those who can read: could you teach someone how to drive using an LLM? Sure. Safely? Probably not.
[0] https://www.transport.nsw.gov.au/operations/roads-and-waterw...
Especially important in places like Europe, where it's common for the driver to be able to read, but unable to speak the language of the country they are currently driving through. I can't speak any Polish, but can travel on Polish roads just fine
1: https://en.wikipedia.org/wiki/Prohibitory_traffic_sign#No_en...
Then there is Hanoi.
Not really. You just need to be able to decipher the sign, which is trivial, even if you can't read it or spell it.
But in situations that could be ambiguous, I think this is a regional difference - the US, Australia, part of the rest of the Americas use lots of text on road signs (including literal "wrong way" signs); Europe and much of the rest of the world use far less text (including purely pictographic "wrong way" signs). Especially important in Europe where drivers just can't learn 20+ languages.
These signs, you mean? https://en.wikipedia.org/wiki/Prohibitory_traffic_sign#No_U-...
It's also trivial to do a u turn even when you can read, know what the sign says, and you feel like doing one because no car is coming anyway, and millions of people do that everyday too.
And whole lot of people have done stupid shit like that while perfectly able to read, many even with masters and PhDs.
It is really strange to see comments like this here, where people seem to reduce some basic human action into how it would work in a text-only computer game. Driving itself requires mainly muscular memory how to operate the car, which why people who drive a lot can just go on autopilot and think something completely different when driving long distances. That is of course a form of kno, but you only get it through repetition. Of course driving in traffic requires far more, basic understanding of traffic law etc, but most of driving is muscle memory, understanding the vehicle and anticipating future occurrences. Why we apes are so good at this is because we have some million years of evolution of just using our bodies and seeing what happens. And of course we all seen the gif of an orangutang driving a golf cart (how real it is I’m uncertain), so there’s that.
I think might help to think models not as some future replicants, but models with certain capabilities in certain domains. It probably doesn’t make much sense to ask Opus 4.8 to drive you around as it doesn’t make sense to except a small image model made for edge devices to be able to write a novel. Perhaps we should just think of them as tools with certain applications they are made for.
I would be interested to see a formal study of this. I say this not out of anything other than a observation that I think the only real blockers are a) judgement, and b) physical reflexes/strength. As a kid I was certainly aware of ice,snow, and rain, because I road my bike year round and had low confidence in my own ability to control my bike on snowy or wet terrain, especially during season changes. That translated into learning to drive in northern Canada in the winter and applying those lessons to driving.
In an environment devoid of consequences, I have seen kids operate driving simulations (both real simulations, and video games) with a degree of precision that is shocking, including seeing several 9-11 year olds play the simulations and games with a much higher degree of confidence than adult drivers. Children have an awareness that the simulations are consequence free, unless given other motivation. Adults that are consistent drivers have muscle memory and preconceived expectations that govern the decisions they make when playing the game. I am curious about the level of training and exposure required for children to overcome their lack of awareness of the hard limits and consequences of driving and driver error, versus the amount of training and exposure required for expert drivers that are novice gamers to stop applying their learned experience to consequence free simulations.
(i'm above average in both)
Different times though.
This requires not only knowledge, but also the control systems that develop with the prefrontal cortex. LLMs don't do much control yet.
Conflation. That's to drive a car safely. To just drive a car one only need know to press gas to move, press brake to stop, turn steering wheel to change direction and maybe use a gear stick to shift into drive/park (car can be modified to abstract that away). Not much more complex than riding a bicycle; maybe even less since no need to learn to balance.
Millions of people do drive who can't read. It's very common in parts of Asia, Africa, Latin America, etc, especially rural, but even in cities.
There are places where oral exams and audio-assisted testing is allowed. And there are places where people just drive (and drive fine) not bothering with a license.
I'm glad to see more domain-focused SLMs, we need more of them! A programming focused MoE should work well across many languages.
It is a cheap specialist for closed-world, verifiable reasoning tasks like math, self-contained coding problems, and similar.
"Closed-world" means the needed information is already in the context. It is not a tool-using agent that can discover missing context. "Verifiable" means answers are hard to generate but easy to check.
So no open ended research, repo wide agent work, factual Q&A, or SVG generation. More of a compact reasoning module for bounded problems.
Solve the following first-order ODE for f(x):
((-1 - 2*x)*f(x)*tan(1 + x - exp(-61 - 2*x)*f(x)/x)
+ exp(61 + 2*x)*x*(1 - x*tan(1 + x - exp(-61 - 2*x)*f(x)/x))
+ x*tan(1 + x - exp(-61 - 2*x)*f(x)/x)*f'(x)) = 0
Find the general solution f(x).
f(x) = x*exp(61 + 2*x)*(1 + x - arccos(C/x))
C is an arbitrary constant.
The answer is exactly what you have posted. I am impressed by Qwen!
I just tried the quantized Q4_K_M from [1] in my RTX 2070 Super, it ran at 110 tok/s with 1800 tok/s prefill, and found the same solution to your prompt. It generated valid LaTeX for the answer but its reasoning trace uses mostly compact ASCII math notation. Took 3min 22s to answer, spending 22k tokens almost all on thinking.
[1] https://huggingface.co/prithivMLmods/VibeThinker-3B-GGUF
It's like web hosting; all the open source tools are there and free, and yet website tools, hosts, etc flourish.
SOTA providers are expecting some level of margin. Companies everywhere have a tight eye on their AI bills right now.
The motivation is there if the models get good enough, even if it’s more painful.
How?
Once I can spend 10k to run Opus 4.6 at home, I'm done.
I find what makes frontier models actually work well isn't just the capability of the model, but how well the harness is tuned to its expectations. I wrote a about this in a bit more detail here. https://yogthos.net/posts/2026-06-08-dirge-code.html
A alot randomness in it
Please don't hype
Seems like a really good model to use in an IDE when you still want control over the code structure then.