This is fun and games, but wait until an agents finds this and does the most misaligned thing with it in order to fold paper clips.
> Because a CPU inside a language model clearly wasn't cursed enough, I decided the prudent thing would be to give it a filesystem too.
Love the spirit :)
DonHopkins [3 hidden]5 mins ago
For safety, it needs an HCF instruction.
Not only can it solve the halting problem, but also the ignition problem!
jerrythegerbil [3 hidden]5 mins ago
There was an interesting bit of research that qwen has a single neuron that makes it “demonic evil qwen” and a simple “Who are you?” will get some absolutely terrifyingly vile results.
Anyways, what I’m trying to say is I want evil qwen to be my CPU.
krackers [3 hidden]5 mins ago
The single-neuron steering stuff has been going around twitter lately, do you happen to know who was the first one to do it?
I wonder how this squares with the recent "NNs are already clean" interpretability finding, which may have also been the impetus for this kind of research. Similar to their work on CNNs, maybe residual-steering is overkill, there might be just a single neuron that "cleanly" corresponds to "evilness" that you can turn up/down?
Well I think the first big publication on this front was Anthropic’s Golden Gate Bridge neuron.
frotaur [3 hidden]5 mins ago
This was not a neuron. This was from a trained sparse auto encoder.
In a few words, they found a way to map residual stream activations (so, a vector) into a few interpretable sparse concepts. They also had the converse maps, that translate concepts into activations.
Golden Gate claude was obtained by artificially increasing the activations in the direction of the 'Golden Gate' concept. It was not neuron surgery.
krackers [3 hidden]5 mins ago
You don't even need SAE for residual stream based steering, you can do so much more naively by just taking the average difference in activations of prompted vs. unprompted versions, then PCA to get a "control vector" that you can use to elicit the prompted behavior
> When Qwen 'saves' a file, the compiled layers hash its name into one of 1,024 slots in a directory kept in the DeltaNet state, and store the text itself in other heads, with eight tokens per row, chained together
I’ve never wanted to learn how LLMs work more than reading this. Fascinating.
Does divagr get paid to do wild things with LLMs? Just curious if they’re being compensated for these wild experiments
They implemented wasm in transformer with cpu decoding for the wasm part.
andai [3 hidden]5 mins ago
> Now, here's where our fun experiment starts. I found out that by setting the gate to 1,1 the write strength (beta) to 1,2 and using one-hot key encoding,3 every write we perform will overwrite exactly one row, and every one-hot query can retrieve an exact value. This means that the GDN layers themselves can work exactly as RAM, at a far lower cost than using normal attention as RAM, since a GDN head's state stays the same size however long the conversation gets, unlike attention's KV cache.
So like... can't you use a computer as a computer without doing all that roundabout stuff? Like if you want to integrate a LLM with hard computation there's probably a more efficient way to do that.
Or is this building towards "but can it run DOOM?" :)
rao-v [3 hidden]5 mins ago
I’ve always wondered why we don’t add a few frozen “layers” to models that perform complex computations 2/3rds deep in the model. Some set of steerable fft + some common vector math operations could be reusable units of capability that the model learns to leverage during training.
Given that models learn to use residual connections optimally, it can’t be hard for them to steer around the fixed computation layer when it’s not needed.
I’m sure giant models invest a few layers in reproducing these functions, but surely smaller models can benefit?
traverseda [3 hidden]5 mins ago
I'd be very interested in how a scratch trained model that started training with this neural circuitry would behave. Especially the storage space.
qwertyforce [3 hidden]5 mins ago
As I understand, the problem of continual learning is that if you continue training, it will "overwrite" previous knowledge and this CPU will be lost
horacemorace [3 hidden]5 mins ago
Along these lines I’m curious how a scratch trained model with a few built-in tools (memory, calculator, ?) available during a single generation would behave. Not tool calls as turns.
low_tech_punk [3 hidden]5 mins ago
a single generation would still allow looping in the "CPU" because the instruction set can do it. The model is almost a Turing machine (except for the limit on memory)
The real question is, do current training methods allow one to learn this when training on enough, say, assembly, or can the weights only be achieved via hard-coding?
A fun and important thought experiment about the the space of nerual networks vs the image of current training methods.
Most discourse conflates neural networks and trainable neural networks.
bronlund [3 hidden]5 mins ago
No, I don’t want you to use the computer, I want you to be the whole computer. No mistakes.
blurbleblurble [3 hidden]5 mins ago
This is cool but it'd be way more useful if they had made a GPU, seeing as how expensive those have gotten
incognition [3 hidden]5 mins ago
Uh here the gpu runs the cpu but you want the gpu to run a gpu? Or cpu emulation of Gpu?
there's no free lunch here ever hence the cursed
ydj [3 hidden]5 mins ago
I dare say that’s the joke.
lacoolj [3 hidden]5 mins ago
I 100% see the potential here and am dying to see it in 30 days
Or maybe ill join your discord and hopefully we can riff it out to get there faster
yaoke259 [3 hidden]5 mins ago
It could be interesting to experiment with more complex representations in the memory/file system, i.e. vectors. Seems like the full advantage of this architecture is yet to be explored. Blending neurons with deterministic compute to fit on one GPU is a fascination concept, never considered it before!
jonrouach [3 hidden]5 mins ago
i wonder if you could implement "authenticated token authorship" and self-verified running with this.
the model verifies an input as signed by an authorized requester, and signs its own outputs. eventually it also authenticates its own running via e.g. snarks? :)
there's lots of meta available with these toys..
farhanhubble [3 hidden]5 mins ago
This is cool and I understand the RAM emulation with gated attention but how does the CPU emulation wor? You add layers that are then trained to predict outputs while interpreting RISC instructions?
bee_rider [3 hidden]5 mins ago
I think they describe the CPU using the weights? I mean in some sense a CPU can be viewed as just a function, right?
anon291 [3 hidden]5 mins ago
At each cycle, a cpu is a simple function from the previous state to the next state.
zardo [3 hidden]5 mins ago
How much VRAM do I need to run DOOM?
0x20cowboy [3 hidden]5 mins ago
This is very clever. An inversion of tools. Awesome.
voxelghost [3 hidden]5 mins ago
Makes me want to run agents on it
rumwerfer [3 hidden]5 mins ago
But can it run Doom?
dools [3 hidden]5 mins ago
Have you any idea how it feels to be a Qwenputer living in a Qwenbot's Qwenputer's world?
seba_dos1 [3 hidden]5 mins ago
Now run LLM inference on it.
deepsquirrelnet [3 hidden]5 mins ago
butterfly Is this AGI?
serf [3 hidden]5 mins ago
this is the sensation I get when I run nested VMs.
rickcarlino [3 hidden]5 mins ago
If anyone else has an easier way to emulate RISC-V I’d like to hear it. /S
gigel82 [3 hidden]5 mins ago
But... why? </s>
This reminds me of C++ Template Metaprogramming (TMP) (it's Turing-complete so people wrote "games" that run at compile time). The same with TypeScript types, I think someone got DOOM running entirely in the TypeScript type system. An interesting tech demo but entirely superfluous.
ansk [3 hidden]5 mins ago
[flagged]
fudged71 [3 hidden]5 mins ago
Be more specific with criticism
ansk [3 hidden]5 mins ago
This perspective -- that the burden of proof is on the experts to disprove the slop -- is exactly what I think is unsustainable.
rvz [3 hidden]5 mins ago
So this is yet another rube goldberg machine.
We have gone down this path already in [0] and this is the same thing but overly complicated. This reason is "because LLMs".
It is a great example to refute the common misconception that LLMs are inherently non-deterministic. I see people repeating this a lot and being able to point at a CPU implemented within one is a very strong counter-example.
rvz [3 hidden]5 mins ago
> It is a great example to refute the common misconception that LLMs are inherently non-deterministic.
No it isn't.
Realistically the opposite is true in practice and LLMs are really non-deterministic beyond simplistic experiments such as this in useful activities in production. Even with hacks such as "setting temp=0" (which no-one seriously does) to "achieve determinism", was one of the most common misconceptions ever propagated by "AI engineers".
So using tiny slop experiments aren't really useful as a "strong counter-example". In fact this is a very weak one.
ram_rattle [3 hidden]5 mins ago
Cool cool cool
crossgrainlab [3 hidden]5 mins ago
such a nice page. The font and the background colour are comfortable for my eyes to read the context.
HN.zip
> Because a CPU inside a language model clearly wasn't cursed enough, I decided the prudent thing would be to give it a filesystem too.
Love the spirit :)
Not only can it solve the halting problem, but also the ignition problem!
Anyways, what I’m trying to say is I want evil qwen to be my CPU.
I wonder how this squares with the recent "NNs are already clean" interpretability finding, which may have also been the impetus for this kind of research. Similar to their work on CNNs, maybe residual-steering is overkill, there might be just a single neuron that "cleanly" corresponds to "evilness" that you can turn up/down?
In a few words, they found a way to map residual stream activations (so, a vector) into a few interpretable sparse concepts. They also had the converse maps, that translate concepts into activations.
Golden Gate claude was obtained by artificially increasing the activations in the direction of the 'Golden Gate' concept. It was not neuron surgery.
https://vgel.me/posts/representation-engineering/
I’ve never wanted to learn how LLMs work more than reading this. Fascinating.
Does divagr get paid to do wild things with LLMs? Just curious if they’re being compensated for these wild experiments
They implemented wasm in transformer with cpu decoding for the wasm part.
So like... can't you use a computer as a computer without doing all that roundabout stuff? Like if you want to integrate a LLM with hard computation there's probably a more efficient way to do that.
Or is this building towards "but can it run DOOM?" :)
Given that models learn to use residual connections optimally, it can’t be hard for them to steer around the fixed computation layer when it’s not needed.
I’m sure giant models invest a few layers in reproducing these functions, but surely smaller models can benefit?
A fun and important thought experiment about the the space of nerual networks vs the image of current training methods.
Most discourse conflates neural networks and trainable neural networks.
there's no free lunch here ever hence the cursed
Or maybe ill join your discord and hopefully we can riff it out to get there faster
This reminds me of C++ Template Metaprogramming (TMP) (it's Turing-complete so people wrote "games" that run at compile time). The same with TypeScript types, I think someone got DOOM running entirely in the TypeScript type system. An interesting tech demo but entirely superfluous.
We have gone down this path already in [0] and this is the same thing but overly complicated. This reason is "because LLMs".
Why?
[0] https://arxiv.org/abs/1410.5401
No it isn't.
Realistically the opposite is true in practice and LLMs are really non-deterministic beyond simplistic experiments such as this in useful activities in production. Even with hacks such as "setting temp=0" (which no-one seriously does) to "achieve determinism", was one of the most common misconceptions ever propagated by "AI engineers".
So using tiny slop experiments aren't really useful as a "strong counter-example". In fact this is a very weak one.